Cluster trending analysis for control loop assessment and diagnosis

Highly reliable automated systems require health monitoring capable of detecting any equipment faults as they occur and identifying the faulty components. Loop re-tuning can improve the performance when the operating environment has changed. However, if some equipment in the loop is malfunctioning, the simple control loop re-tuning will be less effective in improving the loop performance. Therefore, it is important to design a loop performance monitoring system with the capability of monitoring both the loop performance and the faults of equipment in-loop. This paper presents a new mechanism for control loop performance monitoring and equipment fault detection, based on cluster trending analysis. This mechanism is very sensitive to small signal variations and capable of detecting the abnormal signals embedded in the normal signals.