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Okuma Learning Lab: Predicting Downtime

Dr. Laine Mears, an assistant professor at Clemson University’s CU-ICAR campus, presented the Okuma Learning Lab “Predicting Downtime.” His case study presentation focused on the use of sensing devices, software and other tools to predict component failures and the benefits and saving that predictive process provides.

Specifically, the system Mears’ group developed allows early detection of spindle bearing problems. Because of the high level of vibration present in a spindle, spindles experience a higher failure rate than other components and spindle failure causes significant downtime, he noted.

Mears added that the detection system incorporates an ultrasonic sensor and an accelerometer, which are positioned side by side to provide “looking and feeling signals.” Considering the signals together can reveal additional information, he noted, adding that the goal is to not only determine what needs fixing but why it is failing.

Mears demonstrated that by taking the ultrasonic signal and scaling it down to make it audible, it’s possible to hear a malfunctioning spindle getting worse the fastest it rotates, with the loudest sound being at the spindle’s natural frequency.

The system’s software provides gages to show when micro and macro damage occurs, and the program can also offer spindle monitoring history and, using Okuma’s Constant Care software, provide remote alerts.

During the study, the group purposely damaged a milling machine’s spindle by removing the lubrication, eliminating the cooling and introducing contamination, Mears explained. The spindle seized at 9,000 rpm after running about 10 minutes. Then the spindle was dismantled to examine component damage. “We gathered very detailed data and learned a lot about how a spindle fails,” he said. “The idea is to have sensors on a machine permanently at a low cost to do data analysis.”

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