Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks

This paper proposes a new approach for rotating machinery which integrates wavelet transform (WT), principal component analysis (PCA), and artificial neural networks (ANN) to classify the fault and predict the conditions of components, equipment, and machines. The standard deviation of wavelet coefficients are extracted from processed historical signals of manufacturing equipment as features. Then, the features are analyzed by PCA and several new principal features obtained from original features can be used as inputs to train ANN. After training, the conditions and degradations of components and machines can be predicted, and the fault of them can be classified if it exists, by the trained ANN using the same kinds of principal features extracted from real time signals. A case study is used to evaluate the proposed method and the result indicates its higher efficiency and effectiveness comparing to traditional methods.

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