Fault Diagnosis of Slipper Wear Signal of Piston Pump Based on Random Forest Algorithm
In order to improve the accuracy of slipper wear fault diagnosis of piston pump under low load,a method for slipper wear fault identification of piston pump based on random forest algorithm was proposed.The frequency domain eigenvalues of the fault signals of the slipper of the piston pump under the condition of low load were analyzed emphically,and the characteristic da-tabase was constructed.The adaptability of above method was verified,and the fault diagnosis of the piston pump with different degrees of loose boots was tested.The results show that the frequency domain has an obvious fluctuation after slipper wear,out-of-Bagerror and the number of decision trees show an inversely proportional change rule,which are basically around 0.05,and the optimal number of decision trees n is set at 400.When the random forest algorithm is used to diagnose the feature database,only one group of misidentification occurs in 250groups of samples,and the recognition accuracy reaches 98.75%.The training time,training accuracy and testing accuracy of random forest method are better than other algorithms.The overall diagnostic accuracy is higher than 99.5%.The random forest method shows excellent adaptability to the wear fault diagnosis of the piston pump and can accurately diagnose each fault state of the piston pump.
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