Motor fault diagnosis method based on uniformity and kurtosis calculation

The invention discloses a motor fault diagnosis method based on uniformity and kurtosis calculation. Comprising the step of analyzing uniformity and kurtosis of transient starting current signals of a motor to obtain simpler parameters capable of effectively representing motor faults. The method comprises the following steps of: predicting an error by initializing a weight, selecting a parent weight according to the predicted error, processing the selected parent weight by two algorithms, namely a self-adaptive genetic algorithm and a cuckoo search algorithm, and finally combining results obtained by the two algorithms, thereby obtaining an optimized neural network weight and an optimal solution. Therefore, a motor fault diagnosis model based on uniformity, kurtosis and AGA-CS is constructed, diagnosis of five faults of the induction motor is realized, and a good basis is provided for motor fault diagnosis. Compared with the prior art, the method has the advantages that the calculation complexity is low, the