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Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism

Xu, Z, Li, C and Yang, Y (2021) Fault diagnosis of rolling bearings using an Improved Multi-Scale Convolutional Neural Network with Feature Attention mechanism. ISA Transactions, 110. pp. 379-393. ISSN 0019-0578

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Machine learning techniques have been successfully applied for the intelligent fault diagnosis of rolling bearings in recent years. This study has developed an Improved Multi-Scale Convolutional Neural Network integrated with a Feature Attention mechanism (IMS-FACNN) model to address the poor performance of traditional CNN-based models under unsteady and complex working environments. The proposed IMS-FACNN has a good extrapolation performance because of the novel IMS coarse grained procedure with training interference and the introduced the feature attention mechanism, which improves the model's generalization ability. The proposed IMS-FACNN model has a better performance than existing methods in all the examined scenarios including diagnosing the bearing fault of a real wind turbine. The results show that the reliability and superiority of the IMS-FACNN model in diagnosing faults of rolling bearings.

Item Type: Article
Uncontrolled Keywords: Science & Technology; Technology; Automation & Control Systems; Engineering, Multidisciplinary; Instruments & Instrumentation; Engineering; Multi-Scale; Convolutional Neural Network; Fault diagnosis; Deep learning; Rolling bearings; Convolutional Neural Network; Deep learning; Fault diagnosis; Multi-Scale; Rolling bearings; Industrial Engineering & Automation; 0102 Applied Mathematics; 0906 Electrical and Electronic Engineering; 0910 Manufacturing Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Engineering
Publisher: Elsevier
SWORD Depositor: A Symplectic
Date Deposited: 18 Aug 2022 09:44
Last Modified: 18 Aug 2022 09:45
DOI or ID number: 10.1016/j.isatra.2020.10.054
URI: https://researchonline.ljmu.ac.uk/id/eprint/17419
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