Modelling and diagnosis of feedback-controlled processes using dynamic principal components analysis and neural networks

Statistical Process Control (SPC) and Engineering Process Control (EPC) have evolved rapidly in manufacturing industry. Typical feedback controllers, such as proportional integral derivative (PID) and exponentially weighted moving average (EWMA) controllers, are designed to maintain desirable operations by compensating for the effects of disturbances and changes in industrial processes according to process data. However, little attention is paid to the modelling and diagnosis of feedback-controlled processes. A new scheme to diagnose the root cause of faults in a feedback-controlled process is presented here by integrating dynamic principal component analysis (PCA) and neural networks. The dynamic PCA is introduced first to extract the dynamic relationship between control action and process output and to generate feature sets. Furthermore, the neural networks' classifier is trained by a scale-conjugated gradient algorithm using training samples. Simulation results show that the above scheme could model the relationship between the feature extracted by dynamic PCA and disturbance parameters, and could improve the ability to diagnose the root causes of faults in comparison with traditional classifiers. In addition, this scheme offers a broad perspective in production variability reduction and optimization of the process controller.