Fault Diagnosis of RV Reducer with Noise Interference

doi:10.3901/JME.2020.01.030

Abstract

Abstract: The vibration signal of rotate vector (RV) reducer is often covered with noise, which poses challenges to the fault diagnosis of RV reducer. To this end, a novel model of convolution neural network (Anti-noise network, ANNet) is proposed to achieve the fault mode identification of RV reducers under noise interference. The one-dimensional vibration signal is first converted into a two-dimensional gray image with the method of signal stacking, and then the dropout operation is utilized to directly interfere with the original input signal. Additionally, different sizes of kernels are applied to extract and fuse the different features of the input signal. Furthermore, the proposed method is compared with other algorithms. The results demonstrate that the proposed algorithm has a stronger anti-noise performance than that of others. Particularly, under the condition of intense noise (3 dB), the accuracy of the model is 10%-20% higher than the existing approaches. Finally, the special structural design of the proposed model and the reason for anti-noise performance of the model are discussed and explained.

Key words: RV reducer, random noise, Dropout of input signal, multi-scale kernels, fault diagnosis

CLC Number:

TH132

PENG Peng, KE Liangliang, WANG Jiugen. Fault Diagnosis of RV Reducer with Noise Interference[J]. Journal of Mechanical Engineering, 2020, 56(1): 30-36.

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