Abstract：Aiming at the problem of low fault diagnosis accuracy due to the scarcity of fault samples of rolling bearings and the difficulty in extracting vibration features, a fault diagnosis method of rolling bearings with few samples based on time-frequency enhancement is proposed. Firstly, one-dimensional vibration signals of rolling bearings are overlapped and sampled, and a two-dimensional time-frequency matrix is constructed by using continuous wavelet transform to map the sampled signals in time-frequency domain. Secondly, the real time-frequency samples are trained by deep convolutional generative adversarial network, and the generated time-frequency samples are added to the training set. Then, the time-series convolutional network is used to fuse the deep time-frequency domain features. Finally, Softmax classifier is constructed to output states corresponding to fault categories. Simulation experiment results show that under the condition of only 10 training samples, the method in the diagnosis of rolling bearing at case western reserve university data set average accuracy of 91.00%, compared with an enhanced time-frequency method is increased by 7.56%, and the time-frequency method is validated by the measured data can significantly improve the fault diagnosis accuracy of less sample circumstances.
胡向东,梁川,杨希. 基于时频增强的滚动轴承少样本故障诊断方法[J]. 计量学报, 2023, 44(1): 12-20.
HU Xiang-dong,LIANG Chuan,YANG Xi. A Method of Fault Diagnosis with Few Samples for Rolling Bearing Based on Time-Frequency Enhancement. Acta Metrologica Sinica, 2023, 44(1): 12-20.
Goodfellow I, Pouget-abadie J, Mirza M, et al. Generative adversarial nets［C］//Advances in Neural Information Processing Systems, 2014: 2672-2680.
［1］
ViolaJ, Chen Y Q, Wang J.FaultFace: Deep convolutional generative adversarial network (DCGAN) based ball-bearing failure detection method［J］. Information Sciences, 2021, 542(1): 195-211.
Li J M, Wang H, Li M, et al. Rolling Bearing Fault Diagnosis Based on Improved Adaptive Parameterless Empirical Wavelet Transform［J］. Acta Metrologica Sinica, 2020, 41(6): 710-716.
Wang B J, Zhang X L, Xing S, et al. Sparse representation theory for support vector machine kernel function selection and its application inhigh-speed bearing fault diagnosis ［J］. ISA Transactions, 2021, 118: 207-218.
［4］
Xia T B, Zhuo P C, Xiao L, et al. Multi-stage fault diagnosis framework for rolling bearing based on OHF Elman AdaBoost-Bagging algorithm［J］. Neurocomputing, 2021, 433(4): 237-251.
［5］
Li X, Yang Y, Pan H Y, et al. Non-parallel least squares support matrix machine for rolling bearing fault diagnosis［J］. Mechanism and Machine Theory, 2020, 145(3): 103676.
［8］
Cheng Y, Lin M, Wu J, et al. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network［J］. Knowledge-Based Systems, 2021, 216(3): 106796.
Wen C L, Lv F Y. Review of Fault Diagnosis Methods Based on Deep Learning ［J］. Journal of Electronics & Information Technology, 2020, 42(1): 234-248.
［11］
Han T, Liu C, Yang W G, et al. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults［J］. Knowledge-Based Systems, 2019, 165(2): 474-487.
Chen W X, Cui C C, Li X J, et al. Bearing Fault Diagnosis of Wind Turbine Based on Multi-wavelet-1D Convolutional LSTM［J］. Acta Metrologica Sinica, 2021, 42(5): 615-622.
Zhao B X, Yuan Q. Improved generative adversarial network for vibration-based fault diagnosis with imbalanced data［J］. Measurement, 2021, 169(2): 108522.
［7］
Wang X, Mao D X, Li X D. Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network［J］. Measurement, 2020, 173(3): 108518.
［9］
Dong Y J, Li Y Q, Zheng H L, et al. A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem ［J］. ISA Transactions, 2022, 121: 327-348.
He Q, Tang X H, Li C J, et al. Bearing Fault Diagnosis with Small Sample Data under Load Unbalance ［J］. China Mechanical Engineering, 2021, 32(10): 1164-1171+1180.
Dong S J, Pei X W, Wu W L, et al. Rolling Bearing Fault Diagnosis Method Based on Multi-layer Denoising Technology and Improved Convolutional Neural Network ［J］. Journal of Mechanical Engineering, 2021, 57(1): 148-156.
Chen J, Huang K X, Lü W Y, et al. Bearing Fault Diagnosis Method Based on VMD and Convolutional Neural Network Undervarying Operation Conditions［J］. Acta Metrologica Sinica, 2021, 42(7): 892-897.
2023, Vol. 44 
