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Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical fault diagnosis of bearings |
Xu WANG1,2, Hongyang GU2, Tianyang WANG1(), Wei ZHANG2, Aihua LI2, Fulei CHU1 |
1. State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China; Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China 2. High-Tech Research Institute of Xi’an, Xi’an 710025, China |
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Abstract The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.
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Keywords
bearing
cross-severity fault diagnosis
hierarchical fault diagnosis
convolutional neural network
decision tree
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Corresponding Author(s):
Tianyang WANG
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Just Accepted Date: 23 September 2021
Online First Date: 29 October 2021
Issue Date: 28 January 2022
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