Mechanics & Industry
Volume 22, 2021
|Number of page(s)||10|
|Published online||09 April 2021|
Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory
Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, PR China
2 School Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, PR China
3 Lushan College of Guangxi University of Science and Technology, Liuzhou 545006, PR China
* e-mail: firstname.lastname@example.org
Accepted: 5 March 2021
Misalignment fault is the main factor that affects the normal running of dual-rotor system. Quantitative identification the misalignment fault is an important way to ensure the safe and stable service of the dual-rotor system, while the identification accuracy of traditional methods is low. Aiming at the above problems, this paper proposed a dual-rotor misalignment fault quantitative identification method based on DBN and D-S evidence theory improved by mutual information measure (MIMD-S). Seven groups experiments were conducted and several vibration signals were collected. By comparing it with the traditional methods D-S, and Pignistic improved D-S (PD-S) evidence theory, the results show that the method proposed in this paper improves the accuracy of the misalignment fault quantitative identification of the dual-rotor, the identification error rate was only 0.36%.
Key words: Deep belief network / mutual information measure / D-S evidence theory / dual-rotor system / misalignment fault quantitative identification
© Y. Dalian et al., published by EDP Sciences 2021
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