Abstract
Aiming at addressing the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an axial piston pump fault diagnosis method that is based on the combination of Mel-frequency cepstrum coefficients (MFCC) and the extreme learning machine (ELM) is proposed. Firstly, a sound sensor is used to realize contactless sound signal acquisition of the axial piston pump. The wavelet packet default threshold denoises the original acquired sound signals. Afterwards, windowing and framing are added to the de-noised sound signals. The MFCC voiceprint characteristics of the processed sound signals are extracted. The voiceprint characteristics are divided into a training sample set and test sample set. ELM models with different numbers of neurons in the hidden layers are established for training and testing. The relationship between the number of neurons in the hidden layer and the recognition accuracy rate is obtained. The ELM model with the optimal number of hidden layer neurons is established and trained with the training sample set. The trained ELM model is applied to the test sample set for fault diagnosis. The fault diagnosis results are obtained. The fault diagnosis results of the ELM model are compared with those of the back propagation (BP) neural network and the support vector machine. The results show that the fault diagnosis method that is proposed in this paper has a higher recognition accuracy rate, shorter training and diagnosis times, and better application prospect.
Highlights
Hydraulic systems are highly nonlinear systems [1]
This paper presents acharacteristics fault diagnosis method for the1 axial pump that is based the fault diagnosis process is characteristics divided into three stages,Figure which1 shows are data and preprocessing, combination of voiceprint and extreme learning machine (ELM)
ELM ispump applied to the feature fault diagnosis of the voiceprint characteristics of the axial piston sound signal
Summary
Hydraulic systems are highly nonlinear systems [1]. Circuits are coupled with each other. Processes 2019, 7, x FOR PEER REVIEW can only be obtained by setting the number of neurons in the hidden layer It has the advantages of used inspeed classification, regression, clustering, feature learning, andinother problems.regression, awidely fast learning and strong generalization ability. It is widely used classification, it has not been applied to the fault diagnosis of the axial piston pump based on sound signals. The fault sound signal of the axial pump is fault diagnosis process is divided into Firstly, three stages, which are data acquisition andpiston preprocessing, collected and denoised by the wavelet packet method. ELM ispump applied to the feature fault diagnosis of the voiceprint characteristics of the axial piston sound signal.
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