The Application Of Wavelet Transform In Data Handling And Fault Diagnosis

In this paper, the applications of wavelet transform in data handlingand fault diagnosis have been studied. Firstly the property of wavelettransform, the necessity of data handling and fault diagnosis are introduced,and the applications of wavelet transform in fault diagnosis are reviewed.Then the effects of wavelet transform in filtering, de-noising and signaldetection are investigated, and wavelet de-noising technique is used tode-noise the real time measurement data—PH, DO and ORP in thesequential batch reactor (SBR) process. The result indicates that usingwavelet to de-noise is reasonable and valid, and it can sensitively identifyuseful information from noised data without the prior knowledge of theprocess, and can judge the feature points of the reaction in wastewatertreatment. In fault diagnosis, multi-scale discrete static wavelet principalcomponent algorithm is proposed by combining the principal componentanalysis and discrete static wavelet transform. This proposed algorithm andgeneral principal component analysis are applied in the Tennesse Eastman process. The results show the proposed algorithm is more accurate. Inorder to identify the faults effectively, an algorithm based on wavelettransform and dynamic time warping is proposed. Aiming at the TennesseEastman process, the proposed algorithm and the general dynamic timewarping algorithm are compared in faults identifying. The results show theproposed algorithm can exactly identify the type of the fault, but thegeneral dynamic time warping algorithm has some misplays. Finally, thispaper is concluded with a summary and some new challenges.