Beyond the Black Box of Predictive Maintenance

#News(General) [ via IoTForIndiaGroup ]

The Industrial Internet of Things promises to usher in an era of predictive maintenance, but manufacturers are moving slowly, struggling to transform asset data into actionable information.
The sheer magnitude of the leap—moving from decades-old, clipboard-based data collection and maintenance processes performed by onsite plant personnel to digital workflows that can be automated and orchestrated by remote workers—requires a certain level of confidence and digital infrastructure maturity not yet pervasive among a majority of manufacturers. Many legacy industrial assets are still not outfitted with sensors, let alone connected, which impedes any potential data collection. In addition, most manufacturers don’t yet have a clear picture of how to create and apply machine learning and predictive models to drive these next-generation maintenance workflows.

“Despite the IIoT buzz, there’s a lot of FUD (fear, uncertainty and doubt),” says Kevin Starr, advanced service global program director for ABB. “Companies know there really is an industrial revolution on the horizon and there’s a lot of discussion, but they don’t want to make a mistake and have to redo their efforts.”

Though many customers of Fluke, which provides computerized maintenance management software (CMMS) and enterprise asset management (EAM) software, are talking about predictive maintenance, they don’t fully understand the concept nor have they built a proper condition monitoring foundation to capture the base data and provide context for whether something is about to fail or deteriorate, says Kevin Clark, vice president of Accelix. Accelix is a new integration layer that connects Fluke’s eMaint CMMS with a variety of connected tools such as sensors as well as third-party systems.

The data dilemma

Getting the underlying data right is also critical to fueling the right analytics. Not only is it important to identify the right data resources, you also need to ensure the data is accurate and at a granular enough level to enable predictions, notes Michael Donohue, vice president for thermal energy at Uptake. Uptake markets a cloud-based system that overlays existing ERP or supervisory control and data acquisition (SCADA) systems, ingesting and analyzing sensor, enterprise and even contextual data such as weather and lightning strikes to deliver insights related to performance, energy optimization and predictive maintenance.

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