Application of Nonlinear Output Frequency Response Functions and Deep Learning to RV Reducer Fault Diagnosis

In this work, a new method based on the nonlinear output frequency response functions (NOFRFs) and deep convolution neural network (CNN) is proposed for RV reducer fault diagnosis. In order to solve the problem of low accuracy caused by output signal that cannot describe the nonlinear characteristic...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-14
Main Authors: Chen, Lerui, Hu, Heyu, Zhang, Zerui, Wang, Xiaoqi
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive systems
Artificial neural networks
Back propagation
Back propagation networks
Blindness
Convolution
Convolution neural network (CNN)
Cost function
Deep learning
Fault diagnosis
Feature extraction
Frequency response
Frequency response functions
Image classification
Neural networks
nonlinear characteristics
nonlinear output frequency response functions (NOFRF) spectrums
Optimization
RV reducer
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Summary:In this work, a new method based on the nonlinear output frequency response functions (NOFRFs) and deep convolution neural network (CNN) is proposed for RV reducer fault diagnosis. In order to solve the problem of low accuracy caused by output signal that cannot describe the nonlinear characteristics of system, the nonlinear spectrum based on output frequency response function is adopted. To improve the performance of error cost function model in back propagation (BP) process of CNN, a new comprehensive error cost function model is designed to guide the network parameters' optimization in direction of the feature classification. Besides, an adaptive network structure optimization algorithm is proposed to avoid the blindness of network structure selection by experience. Specifically, 4-order NOFRF spectrums of system are first obtained by adaptive identification algorithm. Next, NOFRF spectrums are transformed into 2-D images, and CNN extracts the fault features hidden in spectrum images and performs fault classification. The experiment results indicate that the proposed method is effective for RV reducer fault diagnosis with the accuracy of 99.69%.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2020.3029383