Plant Services| By Sheila Kennedy
Amid a lot of industry noise, there’s no better way to get a sense of the value of modern AI driven maintenance approaches – and the imperatives associated with implementing these – than to hear stories directly from practitioners. In every industry and across the world, companies driven to improve reliability at their facilities are testing and investing in new ways to optimize their assets. Their efforts are paying off in greater uptime, efficiency, safety, and cost savings.
Run-to-failure is increasingly reserved for rare and unique circumstances. This trend started when increasingly capable condition inspection and monitoring tools shifted the asset management focus from “fix what’s broken” to “keep it from breaking down.” Today, unprecedented opportunities afforded by the industrial internet of things (IIoT) have further changed the playing field, and there are potential benefits yet to be realized.
For example, predictive maintenance (PdM), originally based on selected asset condition data, has grown to accommodate online, real-time streams of multiple types of condition data received via sensors and even drones. Some companies are applying machine learning (ML) to further refine their predictive analytics and prognostics.
The newest opportunity, prescriptive maintenance (RxM), is a multivariate approach that merges asset condition data with any combination of operating, environmental, process safety, engineering, supplier, or other related data to better diagnose conditions and prescribe specific options for corrective action. The advanced analytics, pattern recognition, modeling, ML, and artificial intelligence (AI) that empower RxM may help companies finally greatly curtail, if not eliminate, the need for reactive maintenance on critical equipment.
We asked experts at seven manufacturing companies to describe their PdM and RxM progress, including successes that are helping keep their programs alive. Here are their stories, followed by some examples of enabling solutions.
- Machine learning for PdM: Covestro
- Convincing POC: Borealis
- Zero-failure objective: Mercer Celgar
- Greenfield opportunity: Saudi Aramco
- Growth management: AGL Energy
- Engineering advantage: Church & Dwight Co.
- New business model: Kaeser Compressors
Varied AI & PdM approaches to a common goal
Numerous software and technology solutions work together to enable PdM and RxM, though some companies won’t name specific choices. A sampling of products involved in some of these stories includes, in alphabetical order: Aspen Mtell from AspenTech, IMx and WMx from SKF, Leonardo IoT from SAP, ORM Digital Twin from Sphera, PI System from OSIsoft, Predict-It from Engineering Consultants Group, and PRiSMfrom AVEVA.
Human nature is another consideration. Despite the advancements enabled by IIoT technologies in the ability to practice PdM and RxM, indications are that workplace culture continues to be a tug-of-war between operations and maintenance (O&M), cautions Paula Hollywood, senior analyst at ARC Advisory Group.
While the functions and objectives of O&M are different, they share common goals, and an asset performance management (APM) approach helps to synchronize the efforts. Hollywood explains: “APM is not just about maintenance; it is evolving to become more about the two groups collaborating along with engineering to systematically utilize assets to maximize profits and reduce risk factors.”
Nevertheless, as the manufacturers above have demonstrated, it is easy to become committed to the practice after taking that first step.