Production System Performance Identification Using Sensor Data
针对先进制造系统动态复杂、传感器数据未充分利用的问题，提出数据驱动模型来识别永久性生产损失的原因，实现实时性能诊断，提升系统效率。
Advanced manufacturing systems are characterized by their complex dynamics, which are subject to constant changes caused by technology insertion, engineering modification, as well as disruption events. To support daily operation, distributed sensors are utilized to monitor the status of each process. The advantages of the sensor data are not fully realized in current production system analysis due to a lack of data-driven system level modeling. Motivated by this need, we propose a data-driven manufacturing system model to describe production dynamics and develop a systematic method to identify the causes of permanent production loss. This research enables real-time production system performance diagnosis, which is invaluable in increasing system responsiveness and improving real-time production control to effectively enhance overall system efficiency.