Fault diagnosis of single-variate systems using a wavelet-based pattern recognition technique

A pattern recognition-based methodology is presented for fault diagnosis of a single-variate and dynamic system. A group of wavelet coordinates discriminating the classes of events most efficiently among other wavelet coordinates are determined according to the linear discriminant basis (LDB) method and a principal component analysis (PCA) technique. The proposed feature extractor couples the LDB method with the double wavelet packet tree in order to determine the best configuration of pattern windows causing the most discrimination among classes. The lifting scheme-based wavelet filters are used so that the required computation time is reduced significantly without degrading the robustness of the method. To reduce the size of the feature space, the wavelet coordinates are projected into a new low-dimensional space, by using a PCA technique, where minimum correlation exists among the new space variables. The tuning of some parameters, which affect the performance of the approach, is also discussed. The feature classifier is a binary decision tree that employs a soft-thresholding scheme for recognition of a noisy input pattern. The performance of the proposed technique is examined by a classification benchmark problem, and the faults classification problems for the Tennessee Eastman process. It is observed that the proposed pattern recognition methodology succeeds satisfactorily to classify the noisy input pattern into the known classes of events.