Bearing Fault Identification Based on CEEMDAN-SVD and CNN
1. Department of Electronic and Communication Engineering, School of Computer and Communication Engineering, Dalian Jiaotong University, Dalian 116028, China
2. Department of Mechanical Electronics, School of Mechanical Engineering, Dalian Jiaotong University, Dalian 116028, China
| Abstract: | Bearings are the core components of motors, and they often rotate at high speeds during operation, which can easily cause wear and fatigue. The failure of rolling bearings can lead to equipment downtime, production delays, and increased maintenance costs. The fault diagnosis of rolling bearings has become the key to ensuring the safety, efficiency, and availability of motor systems. Fault diagnosis of rolling bearings usually involves processing and analyzing their vibration signals to make judgments. In this paper, Complete Ensemble Empirical Mode Decomposition (CEEMAND), Singular Value Decomposition, Renyi Entropy, and Convolutional Neural Networks are used to process the bearing vibration signals for fault diagnosis. The intrinsic mode function (IMF) components obtained from the vibration signal of rolling bearings after CEEMDAN decomposition contain false components that cannot describe the characteristic information. Therefore, based on the calculation of the correlation coefficients between the original signal and each IMF component, IMF components with higher correlation coefficients are selected, and singular value decomposition is performed on the selected IMF components with higher correlation coefficients to obtain their singular values, Calculate the Renyi entropy of the singular value again to form the fault feature vector. Finally, the fault feature vector is input into a Convolutional Neural Network (CNN) for fault category recognition. When the training set accounts for 20%, the accuracy rate is 99.3%. The accuracy of fault signal detection is 100%, indicating that the method proposed in this paper has good fault recognition performance. |
| Keywords: | Fault Identification; Singular Value Decomposition; Complete set Empirical Mode Decomposition; Convolutional Neural Network |
| DOI: | 10.57237/j.mse.2024.01.002 |
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Received: 30 November 2023
Accepted: 28 December 2023
Published Online: 20 January 2024
Funding
| 1. | 大连市重点科技研发计划项目 (2022YF11GX008) |
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