Nonparametric data-driven control loop assessment and diagnosis

In this paper, we present a new mechanism for the control loop performance monitoring and equipment in-loop fault detection. Our method is based on the cluster trending analysis which is very sensitive to small signal variations and capable of detecting the abnormal signals embedded in the normal signals. We would also present two test cases based on the real measurement data. We would use the real control loop data to monitor the loop performance. Based on the sensor data, we detect the faulty conditions of an industrial pump in near-real time.