BISTel’s New A.I. Powered Equipment Health Monitoring and Predictive Maintenance Solution for Smart Manufacturing Eliminates Downtime
Bistel Demonstrates A.I. Based Hmp at Semicon West Smart Manufacturing Pavilion, July 8-11
BISTel, a leading supplier of adaptive intelligence (A.I.) applications and equipment engineering solutions (EES) for smart manufacturing introduced a next-generation, A.I. powered equipment health monitoring and predictive maintenance (HMP) solution for smart manufacturing. HMP provides manufacturers with real-time, actionable insights into the health of their equipment and allows them to optimize performance, resulting in higher equipment utilization, better uptime, and substantially improved factory productivity. BISTel will demonstrate the new HMP in the Semicon West Smart Manufacturing Pavilion, Moscone Center, San Francisco, July 8-11, 2019. BISTel will also feature HMP in its Creating the Factory of the Future 2019 event at the Marriot Marquis, July 10.
New AI health monitoring and predictive maintenance solution for smart manufacturing eliminates downtime
HMP is a fully integrated solution that addresses a myriad of manufacturing problems and bottlenecks that are a drag on production, yield and engineering productivity. Today, manufacturers face three common threats to maintaining high quality, high productivity plants and equipment unscheduled production stoppages due to lack of asset monitoring, lack of plant-wide insight into the health of equipment caused by data fragmentation across the factory and highly inefficient equipment maintenance programs that increase production costs. HMP addresses these challenges.
Industry Leading Data Visualization
Whether you are an operator, engineer or executive, HMP seamlessly integrates with all other factory data management systems to provide the ultimate data visualization experience. HMP provides users with factory insights they need resulting in better and more meaningful decision making.
“Our 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,” noted W.K. Choi.