A method for intelligent fault diagnosis of rotating machinery

Abstract This paper presents an intelligent methodology for diagnosing incipient faults in rotating machinery. In this fault diagnosis system, wavelet transform techniques are used in combination with a function approximation model to extract fault features. Wavelet neural networks are also constructed. The main contributions of this paper are as follows: First, a wavelet theory based on a nonlinear adaptive algorithm is developed for an excitation function approximation of neural networks. Preprocessing of a single fault signal is required to perform diagnosis using an intelligent system. Second, a neural network classifier for identifying the faults is developed. The system is scalable to different rotating machinery and has been successfully demonstrated with a turbine generator unit.

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