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Statistical Inference of Machinery Fault Classification Based on KS Test

Enuo Chen


In this paper, KS test is applied to the intelligent machinery fault diagnosis, and the mult-fault classification system based on KS test is presented. On the premise of less fault samples and not extracting the data sample characteristics, three kinds of fault waveform of rolling bearing are identified and classified using the standard fault samples directly. Through simulation and gear fault diagnosis, it shows that the method can correctly judge the fault when the data samples contain a certain noise. The rolling bearing fault classification system directly uses raw data to calculate, and pre-process data and extract the fault feature are not needed. This method can completely meet the needs of the intelligent fault diagnosis on line.


Statistical inference, machinery fault, classification, KS test.

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