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BISTel Announces Real-Time, Equipment Health Monitoring and Predictive Maintenance (HMP) Solution for Smarter Manufacturing at SEMICON West Booth 1811
For manufacturers, equipment downtime and failures due to defective or failing components is a drag on productivity, quality and profitability
By
Apac CIOOutlook | Thursday, January 01, 1970
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SAN FRANCISCO - BISTel, a leading provider of adaptive intelligence (A.I.) solutions for smarter manufacturing announced today a new, real-time, equipment health monitoring and predictive maintenance (HMP) solution that provides real-time fault detection, classification and predictive analytics to let customers know when equipment might fail or will require servicing. In addition, BISTel’s new HMP solution lets customers know the remaining useful life (RUL) of their equipment. Launched here at the opening of the SEMICON West tradeshow in San Francisco, HMP provides an early warning system to let auto, semiconductor and other manufacturers know when a problem might occur. As a result, the new solution will significantly reduce downtime, servicing costs, and improve overall engineering productivity.
For manufacturers, equipment downtime and failures due to defective or failing components is a drag on productivity, quality and profitability. BISTel’s new HMP solution integrates A.I. based advanced machine learning technologies to help customers detect and classify faults real-time, then uses predictive analytics to determine when faults might occur in the future or when maintenance should be done. As a result, downtime is vastly reduced, and productivity is greatly increased.
“We are excited at the launch of the new HMP solution for smarter manufacturing,” noted W.K. Choi, Founder & CEO of BISTel. “HMP’s ability to detect, analyze and predict real-time will enable our manufacturing customers to accurately know the health of their machines at any point, know the remaining useful life of the machine and when to conduct maintenance. We believe HMP is a winning solution because it boosts productivity and substantially achieves operational efficiencies across the factory.”