Multiscale Nonlinear Principal Component Analysis (NLPCA) and Its Application for Chemical Process Monitoring

The wavelet theory and multiscale method has generated an interest for fault monitoring and control in petrochemical processes. Principal component analysis (PCA) has been used successfully as a multivariate statistical process tool for detecting faults by extracting feature information from complex petrochemical data. The traditional linear PCA (LPCA) is restricted to complicated nonlinear systems; therefore, an adaptive nonlinear PCA (NLPCA) that is based on an improved input training neural network (IT-NN) is presented. A momentum factor and adaptive learning rates are added into the learning algorithm, to improve the training speed of the IT-NN. A novel method of wavelet-based adaptive multiscale nonlinear PCA (MS−NLPCA) is proposed for process signal monitoring. It can effectively monitor the slow and feeble changes of fault signals that cannot be monitored by conventional PCA, and yet detect early faults to yield a minimum rate of false alarms. The validity of the proposed approach has been proved by experimental simulations and practical application.