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RESEARCH PAPER
Rolling bearing fault diagnosis method based on adaptive signal diagnosis network and its application
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Ou Li 1
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1
School of Vehicle and Transportation Engineering, Henan University of Science and Technology, China
 
2
College of Vehicle and Transportation Engineering, Henan University of Science and Technology, China
 
 
Submission date: 2024-06-20
 
 
Final revision date: 2024-09-09
 
 
Acceptance date: 2024-10-15
 
 
Online publication date: 2024-10-19
 
 
Publication date: 2024-10-19
 
 
Corresponding author
Jing Zhu   

School of Vehicle and Transportation Engineering, Henan University of Science and Technology, China
 
 
Eksploatacja i Niezawodność – Maintenance and Reliability 2025;27(2):194673
 
HIGHLIGHTS
  • Rolling bearing fault diagnosis.
  • Temporal Convolutional Network(TCN).
  • Adaptive Signal Diagnostic Network.
KEYWORDS
TOPICS
ABSTRACT
Aiming at the problems of fixed convolution kernel size and poor targeting of extracted features in the application of deep learning in fault diagnosis, this paper develops a fault diagnosis framework called adaptive signal diagnosis network (ASDN), in which the EEMD of multi-scale signal decomposition is improved in the adaptive preprocessing stage to adaptively capture the transient changes of signals. Meanwhile, SSA is improved to further extract fault trends and periodic components to optimize the signal representation. In the adaptive deep learning stage, an innovative temporal convolutional network (TCN) with a dynamic adjustment mechanism was developed to enable the neural network to adjust its convolutional kernel size according to different frequency components so as to accurately process signals of different frequencies. Validation on datasets from Case Western Reserve University and Xi'an Jiaotong University shows the superior performance of the proposed method, with diagnostic accuracies of 100% and 97.42% on these two public datasets, respectively.
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eISSN:2956-3860
ISSN:1507-2711
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