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Home » Prescriptive Analytics for Industrial IoT Failure Risk Management

Prescriptive Analytics for Industrial IoT Failure Risk Management


Written by: Davorin KuchanPublished on: Aug 8, 2016No comments

industrial IoTRisk Management techniques and enterprise tools have been around for some time, mostly in insurance, finance and banking. With the growth of industrial IoT and connected devices, OEMs and industrial customers have more opportunity to apply similar techniques to industrial equipment failure and maintenance problems. As some of our customers like ABT have shown, such new problems require state of the art Prescriptive Analytics tools to reduce failure risk and optimize maintenance costs in any industrial setting.

There are three obvious reasons the latest analytics tools can improve IoT failure risk management:

1. New, more granular IoT data may be difficult to correlate with failures –

Unlike the financial transactions where human behavior and fraud have been tracked for some time, machine data from newly connected, sub-components and related equipment failures are relatively new. Since there may be limited historical information correlating newly archived data with documented failures, it is essential to augment predictive analytics (machine learning) tools with traditional human experience (business rules).

2. Correlating multiple IoT components with a failure is difficult –

Distillery
Image credit: www.oldworldspririts.com
As the sensors and components become more prevalent, it may be difficult to correlate a particular component behavior to a failure. For example, in a commercial distillery, the increased temperature of a distillate and related loss of alcohol efficiency on a hybrid still may be caused by either reduced coolant flow, blockage of a redistillation plate or a steam valve failure. By tracking sensors on each component and correlating them with human operator experience, a distilling plant can predict a more appropriate cleaning and maintenance of a particular distilling component. In other works, collecting data from multiple industrial IoT components and blending it with experience-based learning will significantly improve predicting likelihood of failure or a need for unscheduled maintenance to maintain equipment efficiency.

3. Learning improves maintenance insight, reduces costs –

power line maintenanceToday, most OEMs have periodic scheduled maintenance whether needed or not. Frequently, such maintenance does not account for higher risk of failure due to a component problem. As a result, industrial customers experience both unnecessary maintenance and unpredictable failures, both which increase costs and prolong costly downtimes. For example, one of our customers, a major power distributor in Western Australia, combined predictive analytics with decision logic to identify power grid components more likely to fail soon.

Modern decision management platforms like Sparkling Logic SMARTS allow improving the ultimate Risk Management problem. They allow evolution of intelligent industrial machinery that learns and suggests failure before and outside scheduled maintenance intervals. I predict that using such tools, progressive OEMs and industrial customers will move to variable maintenance schedules and predict the majority of failures BEFORE they happen.

In summary, industrial customers and industrial equipment OEMs need modern tools to manage connected IoT components and equipment and ultimately implement advanced industrial IoT risk management. These techniques will result in higher uptime, lower maintenance costs and higher productivity. Such modern prescriptive analytics tools provide two key areas of expertise:

  1. Predictive Analytics –
  2. to quickly analyze IoT device data, visualize, predict and learn the patterns of failure and suggest best course of action or improved maintenance schedules.

  3. Decision Management / Rules Engines –
  4. to implement predictive discoveries in an easy, graphical fashion as well as to test multiple failure scenarios and instantly deploy the industrial failure risk logic. Deploying and automating improved failure risk decisions will allow even less skilled operators to manage even most complex industrial systems with great efficiency.

Learn more about how SMARTS Decision Manager can help improve your IoT failure risk.


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