Abstract:A fault diagnosis model based on wavelet multi-scale chaotic characteristic parameters-least square support vector machine (LS-SVM) was proposed. Firstly, the different net positive suction head available (NPSHa) values were classified into 4 states according to the experimental phenomena and the chaotic characteristic parameters of frequency bands of the inlet pressure signals. Then, the pressure signals were decomposed by wavelet at 3 scales, and the chaotic parameter feature vectors of low-frequency signals at each scale were constructed. Finally, the LS-SVM which was optimized by genetic algorithm was used to realize cavitation fault diagnosis. Results show that the cavitation states and their evolution laws can be accurately described by using the chaotic characteristics of frequency bands, which is more in line with the actual operation condition of the centrifugal pump. The fault diagnosis accuracy of the model was above 87.5%, which can identify the different cavitation states in the centrifugal pump efficiently.
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