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Beyond the black box of predictive maintenance

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.

Predictive maintenance is one of the most compelling elements of IIOT, but manufacturers are struggling to transform asset data into actionable information.
Predictive maintenance is one of the most compelling elements of IIOT, but manufacturers are struggling to transform asset data into actionable information.

As part of the crescendo of noise surrounding the Industrial Internet of Things (IIoT), predictive maintenance is generally viewed as one of the most compelling initial arguments for moving forward. As manufacturers make their way into implementations, however, they’re finding that collecting data from connected assets is just the tip of the iceberg. Identifying the right data, contextualizing that data so it’s mapped to desired objectives, and linking the entire process back into existing workflows is where the real heavy lifting comes in, creating obstacles for all but the most progressive companies.

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.”

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