Leveraging IIoT and Analytics to Reduce Equipment Failure

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It’s no secret that the Industrial Internet of Things (IIoT) has led to an ever-expanding explosion of big data. Most assets today are equipped with data-transmitting sensors that communicate with other sensors, applications, control devices, historians and any number of other systems.

Equipment sensor data supports preventative and Condition-Based Maintenance (CBM) approaches. According to research by ARC Advisory Group only 18 percent of assets have a failure pattern that increases with use or age. Preventative maintenance alone is not enough to avoid failure in the other 82 percent of assets and a more advanced approach is required.

The IIoT has significantly impacted traditional asset management and maintenance approaches. Software solutions can now be used to make sense of the large amounts of industrial data through the application of machine learning and advanced pattern recognition (APR).

With predictive analytics capabilities, organizations can move to more proactive and predictive maintenance programs to spend less time looking for potential issues and more time taking actions to achieve the greatest return on every asset. For instance, Schneider Electric’s Avantis PRiSM predictive asset analytics software can identify subtle deviations in operating behavior that are often the early warning signs of equipment problems days, weeks or month before failure.

To learn more about the impact that the IIoT has on maintenance and asset management programs, join us for a complimentary Automation World webinar with ARC Advisory Group Research Director Ralph Rio on June 17. The webinar will feature Avantis PRiSM customer EDF Group (one of the world’s leading energy utilities) who will explain how they are leveraging predictive analytics to reduce equipment failures, increase reliability and improve operational performance.

Register on the Automation World website.

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