Condition Monitoring, Predictive Analytics Materializing in the IoT

Most manufacturing operations would put maintenance on the top of their pain lists. While unexpected maintenance from a breakdown can be catastrophic in terms of cost and lost time, predictive maintenance can also be expensive, particularly if it’s unnecessary. Today, more manufacturers are using technology to monitor parts and operations in order to reduce expenses and downtime -- even from planned maintenance. The Internet of Things (IoT) is going a long way toward improving the gains that machine-to-machine communications have promised for a long time.

While the Internet of Things is a relatively new idea, machine-to machine (M2M) communication is not, Sean Riley, global industry director for manufacturing, supply chain and logistics at Software AG, told Design News.

“The ability to perform the analysis at speed and scale has been available for at least 10 years; however, the cost and the ability to implement this type of solution quickly and efficiently have just come about,” he said. “What is new is the ability to leverage the enabling technology extraordinarily cost-effectively and efficiently.”

The enabling technology includes analytics that help make the jump from condition monitoring -– understanding what’s going on right now in industrial machinery -– to predicting which parts are going to fail and when.

“While condition monitoring is akin to real-time diagnostics, it doesn’t help predict failures, but it’s absolutely critical to ensuring that when a failure is predicted, the root cause and overall impact are understood,” Riley told us. “In the past, condition monitoring has focused on single pieces of equipment or sensors. As part of a predictive maintenance program, condition monitoring analytics now provide a critical evaluation of total line health as well as single-component and single-machine health. It also serves as a real-time aggregation function for condition data to be ‘fed’ into a dynamic predictive model.”

The result is a continuous streaming analytics engine that provides automated alerts on the forecasted failures identified by the predictive models. From here, companies can schedule planned maintenance with a high confidence level that the effort –- and the new part -- won’t be wasted. Predictive monitoring is particularly suited for critical machinery, machinery that is complicated to maintain in terms of labor costs, or machinery with components that are expensive or difficult to obtain quickly.

There are also benefits from an inventory standpoint: spare parts and components can be reduced because needs are predicted weeks to months in advance.

“This allows for critical components to be maintained at minimal levels around the world or ordered on-demand with the confidence that they will not be immediately needed,” said Riley. “Supporting this is the streamlining of the ordering process. It’s critically important for a manufacturer to automate this process to ensure that a failure does not occur because a manual process broke down when acquiring a spare component or part.”

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At this point, it makes sense for manufacturers to look into networking with parts suppliers’ electronic catalogs to expedite and even automate ordering in advance of planned maintenance based on failure predictions.

Colorado-based Digabit offers a cloud-based electronics parts catalog solution with a visual interface that can add an e-commerce element to ordering parts, and even -– going forward -- potentially automate it based on condition monitoring and predictive analytics.

“We are basically at the point where we have the hardware and software to enable machines to diagnose their own operating conditions -- via sensors -- and determine when parts and consumables need to be replaced using software analysis,” Alan Sage, CEO of Digabit, told Design News.

Going forward, of course, the nature of the Internet of Things may make machinery even smarter. It’s not far-fetched to imagine an industrial machine that senses when a part is becoming worn out and automatically ordering a replacement via an e-commerce portal, and scheduling its own maintenance, all without human intervention.

Riley said that while the desire for automated parts ordering capability based on predictive analytics isn’t yet widespread, there are multiple companies in the M2M sensors and networking marketplace pursuing the endeavor. With the right network architecture, the sky is the limit in terms of what manufacturers can do with IoT technology.

[image via Software AG]

Tracey Schelmetic graduated from Fairfield University in Fairfield, Conn. and began her long career as a technology and science writer and editor at Appleton & Lange, the now-defunct medical publishing arm of Simon & Schuster. Later, as the editorial director of telecom trade journal Customer Interaction Solutions (today Customer magazine) she became a well-recognized voice in the contact center industry. Today, she is a freelance writer specializing in manufacturing and technology, telecommunications, and enterprise software.

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