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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

This article presents a performance investigation of a fault detection approach for bearings using different chaotic features with fractional order, where the five different chaotic features and three combinations are clearly described, and the detection achievement is organized. In the architecture of the method, a fractional order chaotic system is first applied to produce a chaotic map of the original vibration signal in the chaotic domain, where small changes in the signal with different bearing statuses might be present; then, a 3D feature map can be obtained. Second, five different features, combination methods, and corresponding extraction functions are introduced. In the third action, the correlation functions of extension theory used to construct the classical domain and joint fields are applied to further define the ranges belonging to different bearing statuses. Finally, testing data are fed into the detection system to verify the performance. The experimental results show that the proposed different chaotic features perform well in the detection of bearings with 7 and 21 mil diameters, and an average accuracy rate of 94.4% was achieved in all cases.

Details

Title
The Performance Investigation of Smart Diagnosis for Bearings Using Mixed Chaotic Features with Fractional Order
Author
Shih-Yu, Li 1   VIAFID ORCID Logo  ; Lap-Mou Tam 2 ; Wu, Shih-Ping 3 ; Wei-Lin, Tsai 4 ; Chia-Wen, Hu 4 ; Li-Yang, Cheng 4 ; Yu-Xuan, Xu 4 ; Cheng, Shyi-Chyi 5   VIAFID ORCID Logo 

 Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan 
 Institute for the Development and Quality, Macao 999078, China; Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macao 999078, China 
 Master Program, Graduate Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan 
 Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan 
 Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan 
First page
3801
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2806611228
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.