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Algorithm monitors equipment health

Technology in trial stage increases accuracy of diagnosing vibration anomalies in engines and gearboxes.

February 2, 2016

2 Min Read
Algorithm monitors equipment health

An artificial intelligence algorithm created by University of Alabama in Huntsville principal research scientist Dr. Rodrigo Teixeira greatly increases accuracy in diagnosing the health of complex mechanical systems.

“The ability to extract dependable and actionable information from the vibration of machines will allow businesses to keep their assets running for longer while spending far less in maintenance. Also, the investment to get there will be just software,” said Teixeira, technical lead for the Health & Usage Monitoring Systems (HUMS) analytics project at the university's Reliability & Failure Analysis Laboratory.

In blind tests using data coming from highly unpredictable and real-life situations, the algorithm consistently achieves greater than 90% accuracy, Teixeira said.

"This technology is in the trial stage. We are seeing how it performs in the field. If the results so far hold, we will build credibility and hopefully gain acceptance with our Department of Defense partners," he said. "At the same time, we are expanding our client base to include the private sector. There, we believe we will have an even larger impact in the way they do business."

Typical vibration analysis searches for anomalies in the vibration of machinery such as engines and gearboxes. These changes in vibration can signal wear and future maintenance needs long before the machinery fails.

"Any machine shakes and vibrates, and it will vibrate a little differently when there is something wrong, like a fault," Teixeira said. "If you can detect a fault before it becomes serious, then you can plan ahead and reduce the time machinery spends idle in the shop. As we all know, time is money."

The difficulty in extracting useful information from machinery vibration is the amount of random noise that exists in normal operating environments, and finding that useful information has been a "needle-in-a-haystack" problem. Current monitoring algorithms assume that vibrations are static and that signals and noise can be differentiated by frequency, Teixeira said.

"The problem is that those assumptions never hold true in real life," he added. "Instead, what we have done is to take an artificial intelligence algorithm and 'teach' it the basic principles of physics that govern faults in a vibrating environment."

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