ABSTRACT
Nowadays, global industry is witnessing the explosion of exible manufacturing systems considered as key role for the fourth industrial revolution. Almost hardware and software producers tried their best to assure the best performance for those system. However, some chemical processes in particular requires more strict conditions beside high-accuracy operation of actuators. Chemical reactions may lead to a poor quality for output product even a small change. Therefore, beside the necessary of high-performance integrated control system, monitoring operation should be fast and correct enough to detect and isolate faults when any problems happened in system. In this paper, a method for process fault detection and diagnostic based on data-driven estimation is investigated. In this method, the process fault is detected based on the error between process model response and process response. The Kernel Principal Component Analysis (KPCA) is utilized to classify errors for fault diagnostic. In this research, the method is veried on Stirred-Tank Heating Process. The simulation results demonstrate the effectiveness of proposed method
ABSTRACT
Objective With generalization and steadiness,a new evaluation model by Integrating Non Linear Features extraction algorithm with artificial neural networks(ANN) used for pattern recognition of quality control of Radix Paeoniae Alba was proposed in this paper.Methods The HPLC data from 29 samples with different quality were proceeded with nonlinear kernel principal component analysis(KPCA) and an improved Back propagation algorithm of ANN.The extract characteristics was fed into BP neural networks as input elements for pattern recognition.In the meantime,the processing data,the optimal numbers of hidden layers,the numbers of hidden nodes,excitation functions,and over-fitting,etc. were discussed wholly so that standardization networks was designed without jamming.Results As recognition ratio was 100%,the pattern recognition of quality control of Radix Paeoniae Alba was established successfully by trained networks and predicted results.Conclusion Integrating KPCA algorithm with ANN for pattern recognition of quality control of Radix Paeoniae Alba has been proved to be available.