Riviera Maritime | BY Martyn Wingrove

Container ship managers explain why they adopt IoT, data analytics and remote diagnostics to reduce their environmental footprint.

Shipmanagers discussed their growing use of digitalisation technologies during Riviera Maritime Media’s Vessel Optimisation Webinar Week in early March 2021. Bernhard Schulte Shipmanagement (BSM) head of data governance and analytics Frank Paleokrassas explained how BSM has unlocked significant fuel savings and reduced emissions across its fleet. He said BSM saved US$5M in 2020 just from managing hull maintenance and cylinder oil consumption. “It is not that much. There is scope for a lot more,” said Mr Paleokrassas.

This could include digital twin benchmarking, automatic alert systems, predictive hull inspection recommendations, emissions reporting, performance analysis and prescriptive engine fault diagnostics.

Data telemetry is used to remotely monitor onboard system performance. “We have 50 ships with telemetry integrated with our enterprise resource planning tools,” said Mr Paleokrassas.

He expects to save even more using information for voyage optimisation, weather routeing, and improving the performance of engines and propulsion. “We are moving into prescriptive analytics and have a joint venture with Navidium,” he says. Navidium provides information and analysis on ship performance to operators.

Ships in its fleet are benchmarked for voyage, hull and propeller performance and engine operation, while BSM also monitors lubricant oil consumption. It uses a traffic-light system to identify underperforming vessels and those making the most energy savings.

“We are implementing real-time data streams and focusing on having edge computing and analytics on board our ships,” said Mr Paleokrassas.

Thome Group technical manager Rajiv Malhotra explained the benefits of engine performance monitoring and analysis. He said the focus of the group was ensuring “engine availability and reliability” on its managed ships, which number more than 200 vessels worldwide.

Engines are monitored “to minimise downtime, for energy efficiency and emissions control,” he said. Monitoring is also used to prove compliance with forthcoming environmental regulations.

Mr Malhotra went on to explain the importance of using accurate data “to achieve those objectives”. To improve operational data accuracy, shipmanagers can use validation in the system, manual screening in the office and train crew and vessel managers in its use.

Also important is installing measuring equipment such as torsion, energy and flow meters, in-line sensors and automatic data loggers to minimise human intervention in data collation.

FML Ship Management director and general manager Sunil Kapoor said monitoring vessel speed and fuel consumption ensures vessel operators can keep to their commercial contractual and efficiency requirements.

While ship operators “can avoid breakdowns” by detecting potential problems and “rectifying issues”, they can also reduce emissions, he said.

FML has developed a portal for 24/7 vessel performance monitoring. It combines data from the ship and weather information. “We can monitor and compare performance with sister ships or vessels of a similar design,” said Mr Kapoor.

He provided case studies demonstrating how this information enables FML to detect operational issues, under-performance and ways to optimise trim to improve efficiency.

IoT connectivity

Analytics, trending and monitoring tactics require fast and reliable ship-to-shore communications, increasingly through very small aperture terminals (VSAT) and IoT connectivity. One dedicated communications service is KVH Watch IoT over Ku-band networks. During Q1 2021, KVH added several data analytics and remote intervention service providers to its pack, including the Smart Ship Hub platform. This provides performance advisory and predictive diagnostics for vessel performance optimisation. It also delivers remote video-based maintenance, surveys and a wide range of related services that rely on real-time data feeds.

“IoT is a real game-changer for the industry,” says KVH Industries senior director of business development Sven-Eric Brooks. “It needs dedicated connectivity channels, isolated from crew and ship operations, to capture the benefits.”

Other application providers on KVH Watch include TechBinder’s smart vessel optimiser, Tile Marine, GreenSteam’s analytics, Kilo Marine’s V-Node platform and IoCurrent’s MarineInsight platform for IoT data acquisition, remote monitoring and real-time vessel analytics.

Mr Brooks says these services and technologies “can result in operational efficiencies, cost savings, and increased sustainability for fleets.”

GreenSteam chief executive Simon Whitford says more shipping companies are using this type of connectivity for remote monitoring and data transfers for cloud-based or onshore analytics. “There is value to be exploited and insights from the data,” he says.

Kilo Marine will use Watch to provide on-demand remote expert intervention with its technicians supporting vessel owners through IoT data acquisition and monitoring.

TechBinder will offer remote expert interventions, allowing technical troubleshooting and remote maintenance support. IoCurrents’ MarineInsight uses machine-learning algorithms to support maritime maintenance and fuel optimisation.

They join Kongsberg Digital’s Vessel Insight in KVH’s growing partnership programme. Kongsberg has its own collaboration strategy and has started offering OrbitMI’s maritime intelligence, compliance, vessel tracking and vessel performance applications to its Vessel Insight customers via the Kognifai Marketplace.

Vessel Insight collects and contextualises data from vessels enabling shipowners and operators to begin their digitalisation process. Kongsberg also added Kyma’s specialised vessel monitoring applications on Kognifai for Vessel Insight customers.

Inmarsat has built a certified application provider (CAP) catalogue for its Fleet Connect dedicated bandwidth for IoT services. Its latest addition is OneOcean, which can transmit its voyage planning software and updates over Inmarsat’s Fleet Xpress communications. OneOcean can deploy route planning services, updates and improve ship-to-shore integration of navigation information.

In March, Brightree joined Inmarsat’s CAP programme. It will use Fleet Connect to offer its marine bunker and fuel consumption monitoring application and remote engine monitoring services.

It uses Coriolis mass flow meters to accurately measure marine engine fuel consumption and bunkering transfer. Brightree’s Dandelion cloud-based remote controller transmits real-time consumption data over Fleet Connect, for fuel efficiency.

Fleet Connect ensures safety-critical navigational tools remain up to date, says Inmarsat Maritime president Ronald Spithout. “By using Fleet Connect, vessels can update mission-critical software easily and cost effectively without installing new hardware, at a time when Covid-19 continues to make ship visits especially challenging.”

This connectivity is needed as stakeholders demand more operational data. Inmarsat director for strategy and business development Alberto Perez says the “average volume of data downloaded per ship has doubled in less than six months”. It was 4 GB in April 2020 and by October 2020 it had risen to 8 GB. “A lot of this growth has been driven by welfare, but also the increase in digitalisation,” he says. Inmarsat has invested in new satellites for its Global Xpress network to support this capacity growth.

Data challenges

Despite technology developments, there remain challenges to using data effectively. Nautilus Labs senior director for strategy and insights Ross Millard explains how stakeholders have different interests. “They each have different angles,” he says. “Owners control the data, but how do they use the data and share it with other stakeholders?”

He thinks the shipping industry needs to find ways to share information across multiple parties as this will be increasingly required for emissions reporting.

“We need some type of standard if we are to run efficiently as there will be emissions pressures and others will be getting involved,” says Mr Millard. “As the industry moves forward, there will be incentives to reduce emissions.” He says there needs to be a common structure for owners, charterers and managers to work together and “align their goals with the realities of the industry”.

Digital Container Shipping Association (DCSA) provides that through its guides and standardisation. Its latest development is track and trace (T&T) standards enabling most of its member carriers to offer data access through standard application programming interfaces (APIs). These provide a streamlined way for shippers to receive real-time, cross-carrier data regarding the whereabouts of their containers.

DCSA says widespread adoption of its standards will advance the industry in terms of visibility and real-time responsiveness, resulting in greater reliability and a better customer experience. DCSA T&T comprises a downloadable information model and interface standard. Container lines are behind DCSA’s initiatives.

MSC global chief digital and information officer and chairman of the DCSA supervisory board André Simha says “While a variety of digital innovations exist in the maritime industry, MSC believes new solutions will only be fit for purpose if they can be operated across multiple carriers, service providers and geographies.”

While CMA CGM executive vice president IT, digital, SSC and transformation Nicolas Sekkaki says “DCSA digital standards will not only enable this interoperability, they will make it easier for carriers to achieve customer excellence and operational efficiency.

“But adopting standards and collaborating across the industry requires more than standards alone, it requires a cultural change in the industry which will hopefully start now.”

Yang Ming chief information officer Steven Tsao says, “With the T&T standards-based API in place, shippers will have real-time information about a container’s location and receive notification of delays.”

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Riviera Maritime | BY Martyn Wingrove

Combine engine condition monitoring with ship performance analytics to optimise the whole vessel, using data analytics to lower fuel costs, reduce emissions, minimise propulsion issues and prevent vessel downtime

These were the key conclusions from a panel of technical experts at Riviera’s How operators use data to optimise engine performance webinar. Sponsored by Aquametro Oil & Marine and Propulsion Analytics, the webinar was held on 3 March 2021 during Riviera’s Vessel Optimisation Webinar Week.

On the panel were FML Ship Management director and general manager Sunil Kapoor, Thome Group technical manager Rajiv Malhotra, Aquametro Oil & Marine international sales manager Thomson John, Propulsion Analytics engine performance manager Sokratis Demesoukas and Propulsion Analytics communications and marketing executive Zoe Lygizou-Karlou.

They discussed how owners and operators can harness engine and fuel-flow monitoring data to optimise engine power and performance.

Condition monitoring

Mr Kapoor said monitoring vessel speed and fuel consumption ensures vessel operators can keep to their commercial contractual and efficiency requirements. While ship operators “can avoid breakdowns” by detecting potential problems and “rectifying issues”, they can also reduce emissions, he said.

FML has developed a portal for 24/7 vessel performance monitoring. It combines data from the ship and weather information. “We can monitor and compare performance with sister ships or vessels of similar design,” said Mr Kapoor. He provided case studies demonstrating how these information sources enable FML to detect operational issues, under-performance and ways to optimise trim to improve efficiency.

Mr Malhotra explained the benefits of engine performance monitoring and analysis. He said the main focus was ensuring “engine availability and reliability” on managed ships. Thome technically manages more than 200 ships for owners worldwide.

Engines should be monitored “to minimise downtime, for energy efficiency and emissions control” said Mr Malhotra. Monitoring is also used to prove compliance with forthcoming environmental regulations.

He went on to explain the importance of using accurate data “to achieve those objectives”. To improve operational data accuracy, shipmanagers can use validation in the system, manual screening in the office and train crew and vessel managers in its use. Installing measuring equipment such as torsion, energy and flow meters, in-line sensors and automatic data loggers minimises human intervention in data collation.

Mr John presented the Aquametro range of onboard sensors and meters including Controil flowmeters for measuring the actual fuel consumption of the engines, power meters “and sensors for monitoring and analysing the recorded information of fuel, power and other engine parameters”. Aquametro also provides Viscomaster for fuel viscosity measurement and Homogenizer for fuel treatment to improve the fuel combustion.

“The use of high-quality sensors along with real-time monitoring and analysis strategies will provide an excellent opportunity to improve the efficiency and safety of ships and related equipment,” said Mr John. “Collecting high-quality ship data with reliable sensors will open up new ways to optimise and extend the lifecycle of the vessel according to the highest standards of operation,” he added.

Mr Demesoukas said sensors and information from bridge systems can be analysed with weather information to evaluate whole vessel performance in different conditions. “All data should be collected with high frequency, perhaps every five minutes,” he said. “Data can come from the navigation signal, such as speed over water, position and rudder angle, and from engine revolutions, power management and fuel consumption, with weather data coming from sensors on vessels.” This data is then uploaded to a cloud-based database for analysis by software.

Propulsion analytics

Ms Lygizou-Karlou introduced Propulsion Analytics’ new analysis product VesselQuad, a combination of an engine performance management suite and Quad vessel performance evaluation software. “This fusion is the most accurate vessel and engine performance assessment software,” she said. “It combines engine monitoring, machine learning, vessel performance and data analytics.”

There was general agreement from those attending the webinar that speed and consumption performance optimisation is significantly enhanced through using high-frequency auto-logged data collection using in-line sensors, such as flowmeters, shaft power meters, anemometers and speed loggers. Of those who responded to the poll question, 51% strongly agreed and 34% agreed, while just 4% disagreed and 11% did not have an opinion.

When the audience was asked which methods they considered to be the most reliable for fuel consumption measurement, 82% said fuel-flow meters, 16% said tank soundings and just 2% bunker delivery notes.

Around 94% of attendees then agreed continuous power and torque measurement of an engine were critical for optimising vessel performance.

They were then asked which of the following was the most important feature in a vessel performance monitoring system, with half (50%) of the responses for reliable measurement sensors, while 31% said it was real-time data, 12% thought it was predictions and actionable insights based on artificial intelligence and machine learning, and 7% thought user-friendly interfaces.

In another poll question, attendees were asked whether advanced engine performance monitoring technologies were able to compensate for shortcomings in crew competence in engine performance evaluation. 45% agreed with this statement, 25% strongly agreed, while 9% disagreed, 6% strongly disagreed and 15% remained on the fence.

Attendees were then asked their opinion on operational and environmental issues. They were asked which factor showed the greatest potential for improving the Energy Efficiency Operating Index (EEOI) to achieve the IMO 2030 greenhouse gas targets for carbon emission intensity.

Based on their operational experience with managed vessels, 54% said engine performance optimisation, 23% hull and propeller performance optimisation, 18% thought speed optimisation and just 5% commercial operations optimisation and better fleet utilisation.

On another question, 71% of those who responded thought EEOI was the more appropriate carbon-intensity index for assessing the carbon footprint of a vessel and 29% said annual emission ratios.

Attendees were then asked, besides engine power limitation, which other solutions did they foresee contributing the most to enable existing vessels to meet the required Energy Efficiency Existing Ship Index levels set by IMO.

45% said engine upgrades, such as modifications to turbochargers, fuel injection components, exhaust valves and control systems, for greater efficiency.

28% said propulsion improvement devices, including post-swirl and pre-swirl devices or rudder and propeller modifications, 16% thought using waste-heat recovery systems, 9% said installing shaft generators and just 2% said air lubrication.How operators use data to optimise engine performance webinar panel

Riviera’s How operators use data to optimise engine performance webinar panel were (left to right): Aquametro Oil & Marine international sales manager Thomson John, FML Ship Management director and general manager Sunil Kapoor, Thome Group technical manager Rajiv Malhotra, Propulsion Analytics communications and marketing executive Zoe Lygizou-Karlou and Propulsion Analytics engine performance manager Sokratis Demesoukas

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Maritime Logistics | BY Matthew Tremblay, ABS

Industries are adapting to an increasingly digitalized landscape. The floating production, storage and offloading (FPSO) industry is no different. As concerted efforts are made to improve project economics Matt Tremblay, ABS Senior Vice President, Global Offshore, discusses how digitalisation and data analytics can increase safety, reduce costs, and build robust technical and operational capabilities.

No one could have predicted what a challenging year 2020 would become. Offshore production activity fluctuated dramatically with changing market economics and a global pandemic, the fall out of heightened geopolitical tensions, not to mention new IMO regulations introduced at the beginning of this year.

On the horizon, however, is positive news. Markets are beginning to evolve, and an example of this is Brazil, where both Petrobras and international oil companies are again active. Increased activity is also taking place in the North Sea, and in Australia. New entrants are making strong headway in regions such as Mexico, where Petroleum Reform is opening offshore exploration and production to foreign companies.

Evolving FPSOs through the influence of digitalisation
With these positive developments, the Floating Production Storage and Offloading (FPSO) markets and the assets themselves, continue to evolve.

With the largest fleet of classed FPSOs, ABS has supported their development in both size and complexity. At its core, an FPSO is simply a production, storage, and offloading system. While the basic design concept hasn’t fundamentally changed, what is evolving are the technologies, systems, and tools available for an FPSO to optimize its design and operation.

Influencing this change are the fundamentals of how asset management can be applied to achieve leaner, cost-effective operations and reliable exploration activities. There is a primary focus on improving maintenance scheduling and performance, reducing human factor involvement, and increasing the lifetime use of the FPSO asset, safely. The importance of digitalisation is increasingly becoming a priority on the boardroom agenda of many operators, particularly with industry-wide initiatives toward net-zero carbon.

Managing complex assets

Managing the integrity of an FPSO poses a particular set of challenges, and integrity management is still often managed using outdated, labor-intensive spreadsheets or other basic systems.

However, mindsets are changing with increasing awareness on how data can be leveraged to help provide real-time answers to common maintenance, operations and performance questions.

One of the benefits of digitalisation is the increase in performance and productivity that can be achieved with a minute-by-minute visibility of how an FPSO is operating. Giving an owner-operator the opportunity to clearly track the change in asset condition over time, from construction through to late-life, helps make more informed decisions, supported by more reliable data, making the industry safer.

Considering the challenges of FPSO operations, fewer, more focused inspections that reduce the need for tanks or equipment to be physically examined while maintaining safety standards, represent a compelling proposition. As does an optimized maintenance system to improve uptime and increase reliability. Combine this, and you have a simpler way to manage maintenance crews on board, optimize turnarounds, simplify logistics, streamline the POB, and improve operations. The end result leads to safer operations, a reduction in OPEX, and improved profitability.

The trick is how to avoid adding unacceptable risk. It’s why ABS has moved to develop solutions using data science as the basis for an informed and targeted decision-making process, using predictive analytics to guide operational decisions. Examples include analysis of early corrosion detection and coating failures using machine learning and pattern identification intelligence, and real-time monitoring and transparency into how a vessel’s operational profile and loading patterns are affecting its structural integrity, providing predictive alerts for detected anomalies to reduce the risk of unplanned downtime and improvement in maintenance strategies.

The goal in moving to a condition-based system is that you are letting the condition of the asset – such as the FPSO hull – tell you how often you need to inspect and maintain it. For example, consistent hull inspections showing no corrosion may allow you to increase your inspection interval.

It starts with collecting data that will be processed and analyzed. Most of this data is something we already have, such as the original design information, the engineering assessments and analysis, the inspection records, and survey results. Environmental data may be acquired from industry sources or measured onboard. Operational data such as loading patterns, production profiles, failure modes, maintenance data, are also available by manual intervention and measurement, or through sensor-based monitoring systems.

When you combine all this information with diagnostic and potentially prognostic models, can then detect health and performance anomalies in the form of impending failure or performance degradation at an early stage.

This provides valuable information for corrective and preventative actions by allowing both onboard and onshore management teams to observe the condition and status of their vessels’ integrity. This information empowers operators to develop appropriate strategies for maintaining their assets, optimizing decision-making, and managing integrity and maintenance as efficiently as possible to avoid unexpected downtime and productivity loss in operations.

Creating your data ecosystem

With any offshore unit, and in any operation, there are different kinds of data generated. There is data generated from the operational side, such as oil and vibration testing. There is data generated from repairs, maintenance, warranty claims, CMMS data, as well as met-ocean conditions and environment data.

All these data configurations are very diverse and were traditionally kept in silos. Today, we’re able to combine data sets from multiple sources together with technologies that help operators make better and more informed to-the-minute decisions. Combining multiple data sets generates big data analytics, which is the concept of using different data sources to create penetrating new insights.

Auditing your data and applying digital technology will automate the translation and data analysis process.

Digital solutions can then be used to visualize the status of that asset, and monitoring tools can be used to help you focus on the big picture. For example, combining data analytics with Artificial Intelligence (AI) can investigate the ‘what if scenarios’ and provide future insights, enabling operators to begin to answer not only what happened, but also what will happen.

Building a Digital Asset Framework: Digital twin

“Digital Twin” is a familiar term, but it is a term that is hard to define. For example, if you assessed 10 separate projects, each with their own Digital Twin, all 10 of them would give you a different description of what it is, what it does, and what it delivers.

This is not necessarily a bad thing, and in reality, no two projects will be the same, particularly in the design and operation of FPSOs where there are vast technical challenges that require numerous detailed process, control, safety, and flow simulations to maximize production, from subsea to offloading.

In a Digital Twin, physics-induced data are used to mirror and predict the status and life of its corresponding physical twin. This enhances the operation of an FPSO by helping to both visualize and predict the performance of that asset. As the digital twin is designed to continuously collect and process operating data from sensors and other data sources, it presents a constantly evolving picture of the FPSO’s living status at all times.

While you may not need all of the potential analysis capability available today to support a new FPSO that has just been installed, it is beneficial to deliver a new FPSO with a robust condition model to allow you to begin the full lifecycle of the model alongside the physical asset. Ultimately, it allows operators to better calculate and forecast the remaining life of their asset.

Applying Digital Twin technology as part of a “Digital Asset Framework”, allows for a single source of truth that can be shared with stakeholders, including owners, operators, project financiers, insurance providers, and regulators alike. Everybody can access the same reports, data, and insights in a way that makes sense through data visualization.

Digital-driven business outcomes

The digital solutions applied to an FPSO impact its entire value chain, from equipment and inventory to operational efficiency, including optimization of inspections and onboard activities. Coupling these digital solutions with traditional risk-based inspection and maintenance planning techniques has shown a 10:1 return on investment opportunity over the total asset life due to optimized repair and inspection planning.

In early project CapEx planning, condition models, sensor data ingestion tools, remote inspection technology, are all aspects of what should be looked at to ensure an FPSO is future-proofed, so that in 10 years from now, an operator can apply the latest predictive analytics techniques to forecast its remaining asset life.

Where operators manage a fleet of multiple offshore units, using the right digital tools and data insight offers benchmarking that helps compare one offshore unit’s performance against another in the fleet. This gives further opportunity to improve asset management activities through the efficient allocation or re-allocation of resources resulting in streamlined scheduling of fleet maintenance activities that are focused and specific.

A connected future

The transition from legacy systems to Digital Twin driven operations will be incremental, which will naturally incorporate a range of digital solutions to improve asset management and optimization.

The digital tools now entering the market allow operators to ingest, store, track, and analyze condition data in a way that was never possible before. These are technologies we are both building and implementing, focused on three primary goals; improving asset reliability, streamlining the Class process on offshore operations, and ultimately, supporting the improved profitability of the industry. Of course, this is all rooted in a framework of safety and quality spanning an end-to-end solution.

ABS has developed several innovations to support the monitoring and management of structural, machinery, and condition metrics needed for asset management and regulatory compliance.

ABS Condition Manager is just one of a suite of advanced new digital services and applications launched by ABS that offer unprecedented understanding of the status of an asset. A compartment-based, digital visualization of an asset’s condition, including inspection and repair history, as well as critical area monitoring using Class and operational data, the ABS Condition Manager application is a good example of how digital technologies can empower users with insights surrounding structural health, anomaly impacts and maintenance opportunities. The ability of Condition Manager to seamlessly integrate with onboard CMMS systems can result in up to a 50% reduction in effort to prepare annual maintenance work plans.

ABS is also exploring practical applications of AI-enabled inspection capabilities in areas such as corrosion detection, machinery performance, and monitoring, through to accumulated fatigue damage on structures based on asset-specific data, and even metocean route planning.

As we continue to move deeper into a condition-based and ultimately predictive approach, we are looking towards more sophisticated AI and using additional data from onboard sensors for advanced monitoring of vessel health, from the hull structure to whole-life integrity support with ‘live’ operation decision support.

From the design concept, through to construction, operations, and end of life, Class is connected to the asset, and the digital tools and technology that support it are transforming the future of FPSO operations.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Plantservices.com | By Sheila Kennedy, CMRP, contributing editor

Maintenance and reliability best practices are continually improving and so are the technologies that support them. Firmly embedded in the realm of the “best” is predictive maintenance (PdM), which combines real-time monitoring of asset condition, environmental, and/or operational data with smart analytics to detect, assess, and forewarn of impending problems.

Prescriptive maintenance (RxM) takes PdM a step further by prescribing corrective actions for deteriorating conditions and including them in the alerts. RxM is a newer concept just gaining ground that is made possible by machine learning (ML), artificial intelligence (AI), and the internet of things (IoT).

“Preventing dreaded unplanned downtime provides a clear business benefit and driver for adoption of PdM,” said Ralph Rio, vice president at ARC Advisory Group. “Unplanned downtime often cascades into major losses including revenue, WIP materials, and larger equipment repair costs.”

Additionally, the costs for implementation and support of a PdM application have dramatically lowered with the IoT, cloud platforms, microservices for analytics, and wireless sensors, Rio observes. “Clear benefits and lower costs have improved financial justification and driven adoption in a wide range of industries,” he added.

The six case studies summarized below illustrate some of the numerous ways PdM and RxM are transformative.

BASF / Schneider Electric

BASF, the largest chemical company in the world, has digitalization as a corporate strategy. This includes using data to better forecast maintenance requirements and reduce unexpected shutdowns.

One such initiative involved an electrical infrastructure expansion at BASF’s Beaumont, TX, plant. Since production efficiency depends on having predictable electrical power, the plant chose to enable remote monitoring and management of its new power distribution substation’s operations and asset health with EcoStruxure Asset Advisor from Schneider Electric. The data-driven, IIoT-enabled service employs asset sensors for continuous condition monitoring along with predictive analytics to identify threats that could lead to asset failures.

BASF also has access to customized, proactive advice about how to prevent failures and improve its maintenance strategies through its partnership with Schneider Electric’s Connected Services Hub (formerly Service Bureau).

Read “How 7 companies are accelerating PdM and RxM at their plants”
The use case involved having more than 100 condition variables continually collected, measured, and computed for 63 substation assets. The asset data is monitored and analyzed from a digital dashboard that provides 24/7 insight into the substation’s global health index and specific asset statuses, from any location.

“We were surprised by some of the things that we found out. Asset Advisor is helping us to prevent catastrophic failures,” said Lee Perry, electrical design engineer at BASF. The cloud-based service “connects assets so we can look at the health of not just our electrical distribution equipment, but also the motor control centers and the motors that actually drive the process,” he explained.

“If there’s an issue, then we get an email from the Service Bureau saying, ‘We’re noticing this.’ I can log in to it remotely, look at the same information, and help troubleshoot that issue,” added Perry.

With around-the-clock access to data and expert guidance, the plant has the information it needs to make the right decisions at the right time, perform PdM to optimize asset health, and take steps to improve the efficiency of critical electrical distribution assets. These actions consequently benefit plant uptime, performance, productivity, and safety.

ALCOA / Senseye

PdM is a key part of Alcoa’s strategy to become a more stable and profitable organization. To help automate PdM and reduce downtime and maintenance costs, a proof-of-concept (POC) project involving 50 assets across two casthouse systems was conducted at its Fjar∂aál aluminum smelter in Iceland. The solution is now being expanded to at least 1,000 assets in Fjar∂aál and is scalable enterprise wide.

Senseye was chosen to unify and synchronize the equipment sensor data in the plant’s OSIsoft PI ecosystem with maintenance data in its Oracle eAM solution. From the current and historical ingested data, it automatically builds models and starts learning, without having to set up parameters or alarm levels. Isolated peaks in raw data create signatures that reveal trends and hidden failures. Predictive analytics identify what is happening and why, and can provide prognostic insights on the asset’s remaining useful life.

Operators are automatically notified of cases needing attention and can drill down for further details. It is “like having a thousand eyes in the plant” letting you know when data streams start to move away from normal, explained Árni Einarsson, reliability implementation manager at Alcoa in his presentation at OSIsoft PI World San Francisco 2020.

For instance, an idling current increase provided an indication of a fault in the HDC saw motor system. It was determined a belt guard had come loose and was in contact with the sawing drive, damaging the belt. Replacing the belts and fastening the belt cover during a maintenance shutdown resolved the issue, avoiding 12 hours of unplanned downtime.

A sensor failure detected in a coiler rod cropping shear motor prompted the discovery that a lower pinch roller sensor had come loose, leading to a sharp increase in shear motor torque. Re-fastening the sensor returned the torque to normal levels, avoiding three hours of unplanned downtime.

In total, Alcoa reduced unplanned downtime of the machinery by up to 20 percent and achieved full ROI in 4-6 months. “With the POC completed, we were able to scale the solution to other parts of the business,” said Einarsson. “The focus is on finding the best business cases to continue.”

Duke Energy Renewables / Seeq

Duke Energy Renewables, an owner/operator of wind and solar farms across the U.S., used advanced analytics and ML to automate profiling and detection of failing contactors on one of its wind turbines. “This was a trial run with one use case that was interesting, the data was available, and it seemed like a good candidate. We wanted to get up and running fast and to see if this even works for us,” said Abhi Hullatti, manager of performance analytics at Duke Energy Renewables, in his presentation at the ARC Industry Forum Orlando 2020.

Hullatti described how each turbine has six contactors that help to ensure that when the generator kicks in, it ramps up smoothly and synchronizes with the electrical grid. When any of the six contactors fail, the turbine goes offline for 2-10 days for diagnosis and repair. Turbine contactors at one site tended to fail more frequently than at others. Automating the prediction of impending failure would enable PdM, improve uptime, reduce maintenance costs, and allow better management of spare parts inventory.

Using an automated profiling tool from Seeq, a model was trained to look for the contactor fault error code and plausible leading-indicator signals (reactive power, active power, current, wind speed, rotor speed, and generator speed), to recognize what normal behavior looks like, and to provide one-hour advance notification of a fault.

The model ran against 2.5 years of cleansed signal data for the turbine and it found 12 occurrences of the error code, mostly within the few months preceding a failure. There were no false positives leading up to that outage, and no further error codes or predictions followed once all the contactors were replaced. The model can run continuously once it is validated and it will give automatic notification when something is going wrong, said Hullatti.

“The results are extremely promising, so we want to next expand this model to the rest of our turbines at that site, and also, more interestingly, look at other failure modes. That will be a lot more complex but also far more financially rewarding,” observed Hullatti. The return on the ability to catch a generator that is about to fail, and act proactively instead of reactively, is in the hundreds of thousands of dollars per event, he explained.

Global Mining Company / Uptake

A leading global mining company needed to predict and prevent failures that can cause unplanned downtime in its material transport operations. The company owns and operates a network of private railway tracks, rolling stock, and signals that move iron ore from its mining sites to the seaport. Any faults, delays, or breakdowns in the process of moving the high-value material is not only costly from an equipment maintenance and operational performance perspective, but it can also be potentially catastrophic.

The trains and rail infrastructure already produce immense amounts of data from wayside telematics devices. If harnessed, the Wheel Impact Load Detection (WILD), Wheel Condition Monitoring (WCM), Hot-Box Temperature Detection (HBD), and Bearing Acoustic Measurement (BAM) data could provide operational and condition insights into anomalies with sufficient time to take corrective actions.

The mining company chose Uptake, an industrial AI software company, to provide the necessary visibility into degrading asset conditions across the network and advanced analytics to prescribe recommended solutions. The software is now analyzing the four telematics data sources for indications of faulty wheels, wheel bearings, and axles on 10,000 rail cars.

Events are ranked according to criticality. Each high-severity wheel or bearing downtime event for a rail car costs the company at least $6,000 and requires immediate attention to return it to operation. Previously unforeseen, the mining company now has roughly a weeks’ advance notice on high-severity wheel alerts, 11 days’ notice of high-severity bearing alerts, two weeks on medium-severity alerts, and a month on opportunistic events.

With the advanced notice of issues and prescriptive alerts, the company can proactively bundle its maintenance activity, significantly reducing downtime and avoiding rail car failure. As a result, it is able to reduce its single-car unscheduled maintenance events by an estimated 50 percent, from 1,850+ per year to 925+ per year, which represents approximately $34 million in savings over five years.

Cement Plant / Aspentech

A cement plant that had struggled with cyclone blockage in its cement kilns was one of several PdM case studies shared by Chris Williams, global director of asset performance management (APM) services for AspenTech, at his ARC Industry Forum Orlando 2020 workshop. Frequent blockages in filters and cyclones limit production and increase maintenance costs, he explained. Subtle changes in the preheater process can eventually lead to blockage.

To reduce downtime and optimize maintenance, the plant sought a solution to predict and avoid cyclone blockages through early and accurate detection. Having the added benefit of RxM capabilities to resolve emerging problems quicker would minimize production losses.

The cement plant chose a PdM and RxM solution with AI and ML capabilities from AspenTech. Maestro for Aspen Mtell was used to build enhanced agents capable of predicting cyclone blockages. The failure agents trained on past blockages to identify the failure signatures that precede degradation, breakdowns, and process disruptions, and learned to recognize the pattern.

The multivariate (MV) models helped the plant to better understand the root cause of the condition and provided prescriptive guidance on feed composition and kiln operating conditions to the operators, enabling them to quickly bring the process back to normal operation. Within weeks, the solution was able to provide several days of early warning. “Eliminating just 25 percent of blockages generated over $1 million in savings,” said Williams.

Wärtsilä / Pega

Wärtsilä, a Finnish technology and service provider for energy and marine markets around the world, needed to improve its ability to process and analyze asset condition data received daily from thousands of equipment installations. An IoT engine upgrade was needed to help preempt performance issues with its customers’ power plants and vessels, and to automate feedback to each customer. Its legacy system could not sufficiently scale to handle the desired automation.

Many of Wärtsilä’s installations have multiple engines per site, and each engine has attached equipment and hundreds of sensors generating condition-based data. To better harness the global data, the company needed to map it to a normalized data structure for quick processing and apply a rules framework to assess the sensor data for anomalies. This would enable PdM practices to preempt equipment failures and also help to optimize service support.

Read “RxM: What is prescriptive maintenance, and how soon will you need it?”
The company worked previously with Pega on other projects and chose the Pega Platform to extend its existing IoT capabilities. The digital transformation platform for Digital Prescriptive Maintenance is designed to help optimize the cost and duty-cycle of devices and systems.

With the new IoT-powered solution, Wärtsilä now compiles all incoming sensor data in a data lake in the cloud, where it is normalized and fed into the Pega Platform. Using a complex rules framework, the data is processed to identify exceptions that may indicate a developing risk of engine failure or a reduction in equipment performance. Daily feedback reports document the findings and prescribe recommended actions, enabling the company to save time and money by avoiding unplanned downtime and production problems.

Benefits to the customers include improved environmental and economic performance and long-term predictability of their vessels or power plants. Wärtsilä is also benefitting from optimized equipment maintenance, improved reliability and performance, and increased sales of proactive maintenance contracts.

Each of these industry leaders is enjoying the rewards of using maintenance and reliability best practices. With the benefits of PdM and RxM being realized, the old ways of doing business seem antiquated and companies like these are not looking back.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

The technology group Wärtsilä has been awarded an Optimised Maintenance Agreement covering the main engines for two new, ultra-modern 174,000 cbm LNG Carriers (LNGCs) managed by Greece-based Thenamaris LNG Inc. The vessels, ‘Cool Discoverer’ and ‘Cool Racer’, are powered by low pressure, WinGD X-DF two-stroke, dual-fuel main engines, and were built at the Hyundai Heavy Industries (HHI) shipyard in South Korea. The five-year agreement was signed in September 2020.

The object of the agreement is to ensure certainty of operations with budgeted maintenance costs. Under the contract terms, Wärtsilä will enable remote support with condition monitoring systems including Expert Insight, Wärtsilä’s unique digital predictive maintenance product, which is now capable of supporting both two- and four-stroke engines.

To provide accurate and pro-active advice and recommendations, Expert Insight utilises artificial intelligence (AI) and advanced diagnostics backed by Wärtsilä’s vast experience and in-house know-how. This enables prompt notification should performance deviations occur, thus allowing corrective and mitigating actions to be made early. The solution is expected to deliver an estimated 50 percent reduction in unplanned maintenance requirements, and an improvement of 2 to 5 percent in fuel efficiency, with a corresponding reduction in emissions. The service is delivered through Wärtsilä Expertise Centres around the world.

“To achieve optimal operational efficiency, it is necessary to take advantage of the latest and most advanced technology. The agreement with Wärtsilä allows us to benefit from the technological and physical support they can provide through Expert Insight and their global service network. The tailored agreement addresses our main concerns and needs, and provides ways to better manage our costs and risks,” says Mr Andreas Rapanakis, Deputy Technical Manager, Thenamaris LNG Inc.

Predictive maintenance and advanced diagnostics features

“Our Optimised Maintenance Agreement is the smart way to ensure that the increasing complexity of modern engines is handled efficiently. It is an important element within Wärtsilä’s Lifecycle Solutions offering, which strengthens the business performance and competitiveness of our customers. We will be providing a broad range of services to support the reliable and efficient running of these vessels, including 24/7 remote technical support, contract management, maintenance planning, and annual engine health audits, as well as the planning and delivery of spare parts prior to each engine overhaul,” says Mr Rajeev Janardhan, Sales Manager, 2-stroke engine Lifecycle solutions, Wärtsilä Marine Power.

Thenamaris LNG Inc. is a global ship manager of high specification, modern ocean-going vessels, and currently manages eight vessels with an additional five newbuildings set for delivery by 2022. Wärtsilä has an existing ongoing Technical Management Agreement with the company covering the Wärtsilä engines in three LNG vessels fitted with diesel-electric technology. This however is the first Maintenance Agreement signed with Thenamaris LNG Inc. for two-stroke engines.

Wärtsilä currently has about 700 vessels under maintenance agreements, of which approximately 160 are LNG Carriers.

Through Wärtsilä’s and WinGD’s 10-year service partnership agreement signed in December 2017, Wärtsilä has access to WinGD’s Intellectual Property Rights (IPRs) and technical specifications and is appointed as an Authorized Global Service Provider for all WinGD engines. This in turn, provides WinGD and its customers continued access to Wärtsilä’s worldwide service network and comprehensive services offering. It also enhances the opportunity for Wärtsilä to provide integrated smart solutions and smart services to the merchant shipping industry.

The products and services herein described in this press release are not endorsed by The Maritime Executive.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Wartsila | BY Stefan van Loenhout, Frank Velthuis

When Wärtsilä’s Propulsion Condition Monitoring Service (PCMS) was launched in 2010, it was first of its kind. The latest version of the service is a state-of-the-art tool designed to facilitate preventive maintenance and help maximise uptime for Wärtsilä’s customers across the globe. Here’s a closer look at the offering.

The Propulsion Condition Monitoring Service (PCMS) is Wärtsilä’s Condition-Based Maintenance (CBM) solution for propulsion equipment. By carrying out measurements of critical parameters such as vibrations and oil condition, as well as operational parameters including steering angles, rotational speeds, and pitch angles, the Wärtsilä PCMS provides customers with real-time advice and periodic reports on the condition of their machinery, as well as crucial information for maintenance planning.

Since its launch in 2010, the Wärtsilä PCMS has been successfully installed on hundreds of applications. Late last year, Wärtsilä released a new, more cost-effective version of the solution, based on third-party hardware, featuring even higher quality vibration data and reduced on-site maintenance requirements.

Why do customers need the Wärtsilä PCMS?

The PCMS enables Wärtsilä customers to improve the availability, reliability and profitability of their vessels while reducing risks and maintenance costs. By monitoring and analysing the parameters that affect the condition of the propulsion equipment, Wärtsilä’s experts can predict an upcoming failure and also advise customers on the best action to take to mitigate its impact.

The Wärtsilä employees responsible for analysing the data produced by the PCMS have extensive experience and in-depth knowledge of both the propulsion equipment itself and the operational context in which it is used. Once a problem is detected, Wärtsilä will not only make the customer aware of the issue but also provide expert advice on how to address the problem and minimise operational disturbances. For example, this might involve reducing the load on the thruster in order to complete an on-going voyage and scheduling maintenance once the vessel reaches a port.

To make optimal use of the PCMS, and other CBM equipment of this kind, they are best combined with a long-term Wärtsilä service agreement. This allows Wärtsilä not only to detect a possible future failure but also to schedule and carry out the required maintenance in a way that minimises stoppages and costs and maximises uptime and operational efficiency for the customer.

Reliability of Wärtsilä PCMS, towards predictive maintenance

Wärtsilä is recognised as a Condition Monitoring service supplier by four of the world’s major classification societies: The American Bureau of Shipping, Lloyd’s Register, China Classification Society (CCS) and DNV-GL. These societies have acknowledged that the Wärtsilä PCMS can determine the condition of propulsion equipment and enable the extension of required visual internal inspections.

The exceptional reliability of the equipment enables Wärtsilä to carry out optimised maintenance, whereby service is only carried out when necessary and not according to a fixed, time-based schedule. The result is significantly extended service intervals and the reduction of both service-related costs and downtime. For example, a thruster may only require two overhauls over a 15-year lifecycle, instead of three.

What’s new?

Released in December 2018, the new version of PCMS, which is based on third-party condition monitoring hardware systems, is significantly more cost efficient than the previous version of the solution. Customers who sign up for the new PCMS will obtain even better quality vibration data. Moreover, the new system requires fewer software updates and less maintenance, thereby significantly reducing the need for on-site service.

The PCMS can be installed on all kinds of vessels and is available for both Wärtsilä and non-Wärtsilä propulsion equipment including transverse thrusters, steerable thrusters, electric pods, controllable pitch propellers, gearboxes, and water jets. Going forward, Wärtsilä intends to expand the scope of their condition monitoring services to include all types of rotating equipment such as pumps, generators, electric motors, and compressors.

One of the major benefits of the new PCMS is that, unlike the previous version, which could only be applied to propulsion equipment, the new version has made it possible for these types of generic rotating equipment to be included in the scope.

How does the Wärtsilä PCMS work?

Figure 1 above shows the general layout of a vessel with two propulsors, which are both equipped with PCMS sensors and connected to one PCMS cabinet. The cabinet acquires and processes data from sensor readings and the propulsion control system according to the set operational parameters.

The data is processed on board and sent to an assigned technical expert at Wärtsilä. A dashboard (an optional feature) installed on the bridge or in the engine control room allows the operator to monitor the condition of the vessel’s propulsion machinery using both real-time and trend data with advice available in case of irregularities.

The central PCMS server continuously processes the data and sends an immediate alert to Wärtsilä’s certified experts if an issue arises. If daily follow-up is included in the PCMS agreement, a thorough analysis will be carried out the same day and, if abnormalities requiring immediate attention are detected, the CBM expert will inform the operator.

All Wärtsilä PCMS customers receive a periodic report detailing the latest findings and recommendations from their propulsion monitoring. The report also describes the condition of the propulsion equipment, the recommended maintenance interval and advice for how best to keep the equipment in optimal condition.

The PCMS and Artificial Intelligence – what does the future hold?

The CBM systems we know today are based on sensors and techniques such as trend- and vibration analysis. While they are effective, they rely on the engineering rules and their application is limited to specific failure modes. In short, every issue that needs to be monitored requires an engineer to design a rule to detect it.

Looking to the future, advancements in Artificial Intelligence (AI) and automation stand greatly to enhance the sophistication of CBM systems such as the PCMS. With the advent of Machine Learning (ML) technologies such as Google’s TensorFlow, the world of CBM stands before a major paradigm shift. In the coming years, there is reason to believe that these systems will come to rely less on the rigid rules of engineering and more on the flexibility offered by ML algorithms.

Holistic solutions will replace point solutions and periodic reports will be a thing of the past, as real-time reporting becomes the new normal. Backed by the enormous processing power of AI, the ML algorithms will immediately and automatically analyse all incoming data, enabling human experts to dedicate less time to crunching numbers and troubleshooting, and more time to supporting customers, and delivering support and value-adding optimisations.

In short, AI will make us more proactive, helping us detect and solve problems more quickly and accurately. Customers of the future will be able to expect enhanced levels of service, more precise prediction of problems and faster planning and execution of preventive maintenance. The work that currently has to be carried out by humans will eventually only need to be supervised by humans, allowing Wärtsilä to provide a faster, more reliable service than ever before.

The Wärtsilä PCMS enables ship owners and operators to:

• Base operational decisions on the actual condition of their equipment
• Maximise the availability of their vessel by performing overhauls only when needed
• Reduce the likelihood of breakdowns by being proactively informed of faults
• Increase the lifetime of equipment and preserve its condition by obtaining feedback on the factors that cause excess wear and failures
• Reduce the total cost of ownership and maximise profitability

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Wärtsilä | BY Frank Velthuis, Wartsila

In the asset management domain, systems designed to detect anomalies and potential failures are referred to as condition monitoring, or, CM systems. Some CM systems use specific techniques such as thermography and vibration analysis for rotating equipment. These techniques are effective, but their applicability is limited to specific failure modes. By far, however, most CM systems rely on rules based on engineering knowledge. Both rules and the engines that evaluate them have been improved incrementally over the years to, for example, consider more parameters and conditions, or to be run more frequently. For every issue that needs to be detected an engineer needs to design a rule that can detect it.

As machinery becomes more complex, more issues may occur, and more rules are needed to monitor the system. The better a traditional CM system becomes at detecting issues the more numerous and complex the rules become. Eventually the system becomes difficult to maintain and, in some cases, unreliable. Furthermore, engineered rules are specific to one kind of machinery. Monitoring another kind of machinery entails creating a completely new set of rules.

Often actual failures occurring in the field trigger the creation of new- or better rules to detect those, previously unknown or misunderstood failure modes. Hence, in practice it can take years before a CM system to reach maturity; meaning it is able to detect critical failures.

For engines Wärtsilä has a good CBM (Condition Based Maintenance) service available. Its rules and formulas have been improved and refined for many years and it is able to detect many critical failure modes in a timely fashion. Nonetheless, like other systems that rely on rules, it takes considerable effort to maintain, and expanding the current approach to other equipment types would be cumbersome.

Fortunately, thanks to advancements in the field of artificial intelligence and technologies such as Google’s TensorFlow, we are now on the verge of a paradigm shift, where we move from:

• engineering rules to self-learning ML algorithms,
• point solutions to holistic solutions,
• experts crunching data to experts supporting customers,
• periodic reports to real-time collaboration, and from
• reactive troubleshooting to proactive support and optimisation.

Because the ML algorithms automatically analyse all incoming data in real-time, the equipment experts can focus fully on supporting the customer, rather than creating rules, crunching data and making reports. By combining raw processing power of an AI with the deep understanding of equipment experts we can create the CBM system of the future and support our customers much better.

Equipment knowledge remains critical

Compared to a human being, a well-trained AI is superior at detecting anomalous behaviour. Whereas it might take a human being several days to go through a month of machinery data, with varying results, an AI might process it in less than a minute with reliable results.

Unfortunately, AIs today do not yet have the capability to interpret the anomalies. They cannot determine whether an anomaly is a precursor of a severe failure or a less problematic defect, which has no effect on machinery performance, such as a broken sensor. This is where the equipment expert comes in. With knowledge and experience in engineering and troubleshooting an equipment expert can review the anomaly and provide a recommendation to the operator.

If the anomaly detected by the AI indicates a valid issue, the Wärtsilä expert can notify the on-board crew the moment it occurs, thus preventing the situation from worsening.

Teaching the AI

It is also possible that the AI makes a mistake and finds an anomaly which is in fact normal equipment behaviour, or it can miss an event in its entirety. This is where one of the main strengths of machine learning comes in play: its ability to continue improving even when in active use. By pointing out the mistakes of the AI, the expert can teach the AI to correctly assess similar cases in the future.

When doing predictive maintenance on engines the learning capability of the AI shines. A challenge with combustion engines is that every engine is unique due to its environment, load characteristics, the way piping is mounted, and other factors. Whilst it might be too labour intensive to create a single rule set for each engine, it takes virtually no effort to train an AI for each engine. This makes it possible to fine-tune the algorithms for each engine, making it possible to handle engine specific differences without sacrificing accuracy.

Data driven approach to go beyond point solutions

A rule-based approach relies on engineering knowledge on how components within machinery interact. Creating an AI however is fundamentally different. When an AI is fed with sensory data it learns the relationship between measured signals. Whenever one of the signals deviates from what the AI has learned as normal, this is considered as anomalous and flagged as a potential issue.

Such a data driven approach has the advantage that the same method can easily be applied on different types of equipment. From combustion engines to HVAC systems, if it is equipped with sensors, an AI can be trained to detect anomalies with minimal configuration.

Better decisions through collaboration

The traditional method of communication in CBM systems has been periodic reporting. This makes sense in a setup where the expert spends a lot of time on analysis, and addresses each installation one by one, typically in a monthly cycle. When however, the ML algorithms crunch the data, the expert can focus on diagnosing anomalies and providing recommendations to the customer.

In such a setup, the monthly reports no longer make sense. One would be tempted to resort to e-mails and phone calls, but such would ignore the fact that there are dozens or even hundreds of machineries on-board a ship or on a power plant. A better alternative is to use a collaboration application that allows cases to be reviewed in context.

An overview of the analysis software and the collaboration application are included on the overview below. After the equipment expert has diagnosed a potential issue and included its recommendations it becomes visible in the collaboration application. The expert then collaborates with the operator to resolve the issue. It’s a two-way street, and the operator can also request a 2nd opinion from the expert on issues experienced at the site.

Wärtsilä has engaged in a small -scale pilot with Royal Caribbean Cruise Lines (RCCL). RCCL is a front-runner when it comes to the adoption of new digital technologies. Thus far the ML algorithms and the assigned experts have been able to detect all potential issues in a timely manner, allowing the crew on-board to take timely preventive- and optimisation actions.

Looking ahead

Wärtsilä has a broad portfolio of equipment and experts supporting those equipment in the field. Leveraging AI- and machine learning technology allows our experts to focus on supporting our customers and to provide recommendations proactively in real-time. 

These new technologies are anticipated to greatly enhance both the service level Wärtsilä customers experience, as well as the performance of equipment in the field. In addition, it opens the doors to new business models and revenue streams that were not possible before. The future is bright.

Machine Learning and Artificial Intelligence explained

The term ‘artificial intelligence’ or AI dates back to 1956 and is attributed to John McCarthy, a Stanford researched working in the field of computer science. AI is the ability of a machine or a computer program to learn. The concept of AI is based on the idea of building machines capable of learning like humans and then be able to think and act on the basis of those learnings.

The terms machine learning and AI are often used interchangeable but they are not the same. AI is a broad concept while machine learning is a common application of AI in today’s industry. In machine learning (ML), algorithms are created that use computational methods to help the machine gather and therefore “learn” information directly from data without relying on pre-set logic, such as the rules created by an engineer. ML algorithms adaptively improve their performance as the number of samples available for learning increases.

The AI referenced in this article consists of several such ML algorithms. Typically, a ML system consists of three major parts: the model which makes the predictions, the parameters used by the model to weigh different inputs and the learner which adjusts the parameters based on the ability of the model to predict the right outcome. A simple overview is included below.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value

Marine Link | BY Walter Mitchell

Ship owners and operators, machinery OEMs and regulatory entities are embracing much needed technological innovation as demand grows in protecting machinery and communications on maritime assets. Cognitive analytics is a game-changing technology that is now more widely available to the maritime sector.

data analytics

This is the latest evolution of data analytics: from the “days of yore” of logging data in a logbook, to sensing data and connecting to a central console, transmitting data ashore and using artificial intelligence (AI)-enhanced tools to develop a deep understanding of how machines behave.

These platforms have dramatically expanded the toolbox for fleet managers by creating the most in-depth analysis available in the marketplace. It is estimated that 10-12 percent of maritime industry asset owners now use some form of predictive analytics, but only to a limited extent. While many executives understand the potential benefits primarily as cost savings in maintenance and capital cost replacement they often don’t know how to obtain this technology for themselves. 

As data science continues its growth into more industrial applications, tech-savvy and forward-thinking operators are embracing the full reach and potential of AI-enhanced technologies. The progression of analytics from the descriptive (what happened?) to diagnostic (why did it happen?) to predictive and prescriptive analytics (when is it likely to happen again and what can I do to prevent it?) is changing the way industry addresses maintenance and operations.

Predictive analytics

Predictive and prescriptive analytics are the logical next step in analytics, and an important new frontier for the maritime industry. At SparkCognition, we are focused on the foresight presented by cognitive analytics, or analytics that use machine learning. Consider these “what ifs”:

• What if mechanical anomalies could be detected in real time?
• What if that detection was so granular that it could categorize those anomalies into minor, intermediate, or serious?
• What if those anomalies could be shown in 3D, displaying exactly which component of the machine was degrading?
• What if that detection could be built upon with automated model building so that it could predict when maintenance was actually needed, or when failure might occur?
• What if gigabytes of sensed data were streamlined from shipboard sensors, aggregated via IIoT functionality from all vessels in the relevant fleet, and transmitted to a central receiving point?

 Cognitive analytic tools are capable of all of these applications. They detect anomalies in machine operations and predict failure with high degrees of confidence. Detection and forewarning greatly assist the operator by allowing for more precise planning of maintenance and capital replacement. The result is that the life of the machine can be extended, yielding considerable cost savings. Ship owners, operators and others in the maritime space are increasingly interested in this new technology for the following reasons:

• Cognitive analytics can extend its understanding of the difference between traditional diagnostic maintenance and predictive and prescriptive maintenance.
• Ship owners and operators recognize that there exists the capability to ingest the (potentially) gigabytes of data that have already been generated and can use it to gain new insights into operations.
• Cognitive analytics allows ship owners and operators to intelligently plan major maintenance periods such as special surveys and drydockings, adjust spare parts and consumables inventories and support seagoing staff in assessing in-voyage and longer-term maintenance needs.
• Ship owners and operators appreciate that there is a role in developing a deeper understanding of the machine and improving its overall health.

 SparkCognition’s SparkPredict platform is an AI-based cognitive and prognostic system already in use in the aerospace, oil and gas, utility and financial market sectors. It is physics and asset agnostic—it can use data regardless of the platform producing the data, or the format of the data.

It ingests new and historical data (structured or unstructured), installs quickly and easily, and has a low learning curve for operators. Its automated model building feature allows for notification of suboptimal operating conditions before harm occurs to the machinery. Combined with natural language processing (NLP) technology, the platform ingests and presents free-form text from OEM manuals and service guides. 

In one example, a ship operator had attempted to engage NLP across its 105-vessel fleet to digitize more than 35,000 technical manuals, but gave up as it was too large a task. The capacity needed to ingest that volume of information can overwhelm most platforms. This is not the case, however, with SparkPredict, which can easily handle datasets of that size and larger.

Dependency on a new technology to inform machinery maintenance is a new way of thinking about overall machine health. Lloyd’s Register, in its “Global Marine Technology Trends 2030,” estimated a 4,300 percent increase in the annual data generated by ships by 2020, and says that “by 2030, that figure will have increased even further as this is an accelerating trend.” The proper management and analysis of “smart data” will have a major impact on the maritime space. This trend is being driven by the demand for better use of information coming from the ship, and the need to provide the most cutting-edge tools to managers desiring to have stronger control over maintenance and capital equipment replacement budgets.

This blogpost is originally from Aquantico | Challenge, innovate & deliver value