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ARTICLE
Bearing Life Prediction Model for Electromechanical Equipment by Integrating Deep Neural Network and K-Nearest Neighbor Algorithm and Its Application
1
School of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China
Submission date: 2023-12-22
Final revision date: 2024-08-26
Acceptance date: 2024-10-07
Online publication date: 2024-10-22
Corresponding author
Journal of Theoretical and Applied Mechanics 2024;62(4):721-735
KEYWORDS
TOPICS
ABSTRACT
Current life prediction methods of Electromechanical equipment bearings have issues of low accuracy and lack of stability. To address these problems, firstly, indicators based on life degradation characteristics of bearings are selected. Then, a deep neural network-based life prediction model is constructed. Finally, the K-nearest neighbor algorithm is introduced to correct the deviation of the deep neural network prediction model, and a hybrid life prediction model is designed. Results show that effectiveness of the designed model was better, which was of great practical significance for detecting bearing failures in advance, reducing equipment losses and improving equipment reliability.
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| eISSN: | 2543-6309 |
| ISSN: | 1429-2955 |
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