Predictive maintenance benefits more from implementation of the Internet of Things than perhaps any other function at this early stage of wide-spread adoption.
I have written on this topic several times over the past couple of years.
Predictive Condition-Based Maintenance
IoT Testbed For Condition Monitoring To Predictive Maintenance
Use Of Internet of Things Enhances Preventive Maintenance
10 Myths About Predictive Analytics (SAP)
A foulup at Starbucks, Preventive Maintenance Prevents Production
Cloud Platforms For Internet of Things
Predictive or Condition-Based
The asset management community has not made it easy for us generalists with its terminology and definitions. Searching for predictive maintenance (PdM) often serves up results for condition-based maintenance. I am not going to attempt a final definition, but I found something that made sense on the OSIsoft Website. “PdM defines methods to predict or diagnose problems in a piece of equipment based on trending of test results. These methods use non-intrusive testing techniques to measure and compute equipment performance trends.”
Condition-based maintenance (CBM) is a methodology that combines predictive and preventive maintenance with real-time monitoring. PdM uses CBM systems to detect fault sources well in advance of failure, making maintenance a proactive process. CBM accurately detects the current state of mechanical systems and predicts the systems’ ability to perform without failure.
Business Risk
The Aberdeen Group, Report: Building the Business Case for the Executive, December 2013, found that 40 percent of 149 manufacturing executives identified failure of critical assets as the top risk they face.
How do we mitigate this risk? Predictive maintenance and condition-based maintenance are methodologies that help. One thing that makes these strategies work is data. With sufficient data along with a model of the asset’s condition at operational efficiency, reliability engineers can begin to predict failures before they happen.
Just like your car, productive assets pick the worst time to fail. This unplanned downtime is exceedingly expensive. Using predictive technologies, managers can plan for shutdowns at an appropriate time. The right parts can be on hand, labor lined up, production schedules adjusted, all because everything can be planned.
I’ve been talking with Dell often since October when I attended Dell World and it unveiled its Internet of Things initiative.
The interesting thing about Dell compared to almost everyone else I cover is that they approach the IT/OT convergence issue from the IT side rather than the OT side.
Dell’s first IoT product is something I think we’ll see more of–analytics at the edge combined with gateway technology that can bring disparate sources of data together, massage them, send them off to the cloud for further analytics, storage, and visualization. Dell’s current partners are SAP for predictive maintenance and Statistica for analytics.
Expect to see more of these partnerships evolve. In some cases, such as PTC, we are seeing acquisitions to add IoT capability. On the other hand, larger companies who do not have enough in common overall to merge will forge partnerships to offer complete solutions to customers.
We see some of this through the rise of Industrial Internet and IP organizations.
Collecting, moving, analyzing, and displaying data is becoming a big and important business. Customer executives will come to appreciate the work as their companies gain efficiency–and profits.
