NOV-DEC 2018

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34 INTECH NOVEMBER/DECEMBER 2018 WWW.ISA.ORG AUTOMATION IT historical data traces and other real-time traces to best identify anomalies. In figure 1, HMP used vibration data to identify faults in a drivetrain and to classify them in real time, saving engineers the trouble and time of diagnosing faults, which can often take weeks or months. When testing the drivetrain or other vital automobile parts, HMP allowed problems to be nar- rowed down quickly to specific causes, such as broken bearings, misalignment, imbalances, and lack of lubrication. Golden chamber The highly automated semiconductor industry has also seen great success with the implementation of HMP systems. In chip manufacturing, there are typically thousands of vacuum pumps on each process line, each producing their own data traces. HMP's adaptive intelligence can monitor all the data simultaneously, in real-time, to detect anomalies and send alarms for faults. Additionally, HMP en- ables the user to easily set gold standard pumps for chamber matching to ensure that all of the wafers are being produced in the same environment. The RUL of each individual pump can be monitored to maximize maintenance efficiency. View performance Users of equipment HMP can custom- ize their dashboards to monitor their equipment, from the entire floor at once down to the individual sensor data on a single machine. In the dashboard image No more scheduled-based maintenance? As HMP technology adapts to each unique production environment, it can predict failures and create a smarter al- ternative to routine maintenance for vital equipment—one based on sensor feedback, big data, and machine learning rather than on-hours and use-time. HMP is the beginning. The next phase for HMP is developing a next-generation, artificial intelligence–based solution that builds a dynamic knowledge base. This will en- able machines and systems to detect patterns that they have seen before and prescribe solutions to these problems in real time. For now, though, equipment HMP technology can help bring sizeable improvements in equipment utilization, yield, engineering productivity, and cost reduction to semiconductor, flat panel, display, big pharma, steel, and other manufacturing sectors. n ABOUT THE AUTHOR Stewart Chalmers (stewart.chalmers@bistel. com) is a senior strategic marketing executive and advisor to CEOs and smart manufactur - ing technology companies worldwide. He is currently an advisor to BISTel, where he is helping launch artificial intelligence–based manufacturing applications. James Na is senior director of research and development at BISTel. View the online version at (figure 2), a top semiconductor company implemented the HMP system to moni - tor its vacuum pumps. The individual RUL of each pump can be viewed on the floor map or as a chart, as seen in the top two windows. The bottom left window shows the alarm trends for all pumps as recorded by the dynamic fault detec - tion system, and the last window shows how select pumps are comparing to the golden pump (top performing reference pump) established by the user. Foundation for prescriptive, self-healing applications Equipment HMP takes advantage of full trace data. Unlike most traditional fault detection systems, which compare sum- mary data from equipment to thresholds set by engineers, HMP's fault detection analyzes the full spectrum of data being generated—equipment sensor data and output quality data—and uses it to create dynamically defined control limits. This means that subtle shifts in equip- ment functionality and yield quality are identified and investigated. In ad - dition to the data already generated by each piece of equipment, sensors can be placed throughout a manufacturing process, creating a rich mine of informa - tion for the adaptive intelligence to learn from and discover patterns in. Remem - ber, smart manufacturing is event driv- en, meaning that you address issues be- fore they occur and only take machines offline when absolutely necessary. Figure 2. An example of the user-custom- izable dashboard to monitor equipment

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