A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles
Abstract
:1. Introduction
- The mainstream methods and combinations for the vibration signal are analyzed.
- The wayside acoustic diagnosis approaches with features are reviewed.
- Temperature forecasting methods with spatial analysis are displayed.
- The future directions and challenges of railway bearings are discussed.
2. Fault Diagnosis of the Vibration Signals on Train Bearings
2.1. The Vibration Signals of Railway Vehicle Bearings
2.2. Application of Data Decomposition Algorithms
2.3. Application of Feature Extraction and Machine Learning Methods
2.4. Application of Optimization and Ensemble Algorithms
3. Fault Diagnosis of Wayside Acoustic Features on Train Bearings
Reference | Applied Features | Corresponding Techniques | Authors |
---|---|---|---|
[108] | Time and frequency domain | TSK | Amini et al. |
[109] | Time domain waveform, Envelope spectrum | ISVD-RSSD | Zhang et al. |
[110] | Frequency domain, acoustic Doppler signal | CSMW | Christos et al. |
[111] | Time domain waveform, Envelope spectrum | MSCPP | Zhang et al. |
[114] | Time domain waveform, Envelope spectrum, TFD | TSFR | Zhang et al. |
[116] | Frequential domain features | Hilbert transform, analytical description | Dybała and Radkowski |
[118] | Time-domain features (TDF) | FLDA/SVM | Kilinc and Vagner |
4. Fault Diagnosis of Temperature Features on Train Bearings
5. Discussion
6. Conclusions and Future Work
- (1)
- Fault diagnosis algorithm and integrated application. To fulfill the various data processing requirements of the trains, other existing algorithms can also be gradually adopted, such as gated recurrent units, reinforcement learning, graph neural networks, etc. [96,97]. The integration of train rolling bearing diagnosis technologies and the integration of different types of data can be further strengthened to improve the train failure database [140];
- (2)
- Anti-interference processing. From various tests in the fault diagnosis research, it is obvious that the noise reduction of the complex environment and strong interference in train operation directly affects the quality of the sensor data. Regardless of vibration or temperature research, certain processing or quantification of environmental interference is required for the accuracy of data input. The improvement of feature extraction methods is the key to raising the overall fault diagnosis accuracy of train bearings;
- (3)
- Spatio-temporal correlation analysis. In the train, especially the multi-bearing structure in the bogie, the spatial and time-dependent temperature data change phenomenon is worthy of in-depth study. The potential information will affect the fault diagnosis modeling method and contribute to the analysis of the changes in the monitoring data of the train operation areas and the correlation between the internal train components. The changes of temperature data of multiple components in spatio-temporal series may play a positive role in the analysis of the whole train driving and braking system, and the prediction of the life cycle of the train mechanical system will effectively promote the maintenance work of the railway department;
- (4)
- Big data management application. In some literature in the review, the amount of train bearing data is already close to 100,000. With the increase in train operation time, the continuous accumulation and expansion of rolling bearing data, comprehensively recording fault data, and a large-scale data-parallel calculation are paramount to raise the performance of the fault diagnosis system. For example, the Apache Spark framework can be conducted to process large sample test data. Through centralized and distributed programs, the computing efficiency would be greatly raised to realize rolling bearing health detection and historical data analysis, which can accurately evaluate the safety of different railway vehicles and conduct in-depth mining of the relevance in the operation networks and maintenance.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Reference | Extracted Features | Sampling Frequency | RPM | Window Length |
---|---|---|---|---|
[78,79,90] | Frequency domain | 25.6 kHz | - | 200 |
[80] | Correntropy | 12.8 kHz | - | - |
[85] | Transferable features | 12.8 kHz | 1200 r/min | - |
[86] | Transferable features | 5 kHz | 1590 r/min | - |
[87] | Low/High-level fault features | - | - | 200 |
[88] | Time domain, high-layer features | 12.8 kHz | - | 200 |
[90] | Time and frequency domain | 12 kHz | 1730 r/min | - |
[80] | Symmetric alpha-stable (SαS) | 25.6 kHz | 230 r/min | - |
Reference | Fault Diagnosis Model | Classification Accuracy Rate/% |
---|---|---|
[42] | TDWAE | Over 98% |
[62] | WPD-MG-SVM | 93.75% |
[85] | FTNN | 74.81% |
[87] | Der-1DCNN | close to 100% |
[89] | VMD-HT-DBN | 98.07% |
[94] | EMD-GA-BPNN-AdaBoost | 88.75–95% |
Reference | Corresponding Techniques | Evaluation Index | Prediction accuracy (%/°C) |
---|---|---|---|
[130] | MLSTM-iForest | RMSE(root mean square error) | RMSE 1.57 |
[133] | ANN | RMSE, Correlation coefficient (CC) | RMSE 3.089, CC 0.982 (Bearing T4) |
[136] | LSTM | RMSE, CC | RMSE 1.3933, CC 0.9909 (Position 1 K = 15) |
[138] | MTL-LSTM | MSE (mean square error), MAE, R2_score | MSE 0.000731, MAE 0.012677, R2_score 0.951049 |
[139] | Seq2seq | RMSE, MAE | RMSE 8.72, MAE 5.8 |
Fault Diagnosis Category | Model Framework | Methods | Test Device | Measuring Location | Diagnostic Accuracy | Simulation Test | External Interference | Space Analysis | Reference |
---|---|---|---|---|---|---|---|---|---|
Vibration | Decomposition + classifier | EEMD + SVM | complex | fixed | high | No | medium | No | [69] |
Vibration | Decomposition + feature extraction + classifier | EEMD-FNLM + SαS + LSSVM | complex | fixed | high | No | medium | No | [80] |
Vibration | Decomposition + classifier + ensemble learning | EMD + GNN + AdaBosst | complex | fixed | high | No | medium | No | [95] |
Acoustic | De-noising + Decomposition + Demodulation | ISVD + RSSD + HT | simple | fixed | medium | Yes | high | No | [109] |
Acoustic | De-noising + Decomposition + Demodulation | KWP + EMD + HT | simple | fixed | medium | Yes | high | No | [115] |
Temperature | Predictor | LSTM | medium | Not fixed | high | No | medium | Yes | [136] |
Temperature | Attention-based Predictor | MTL + LSTM | medium | Not fixed | high | No | medium | Yes | [138] |
Temperature | Spatio-temporal-attention Predictor | Seq2seq | medium | Not fixed | high | No | medium | Yes | [139] |
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Yan, G.; Chen, J.; Bai, Y.; Yu, C.; Yu, C. A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles. Processes 2022, 10, 724. https://doi.org/10.3390/pr10040724
Yan G, Chen J, Bai Y, Yu C, Yu C. A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles. Processes. 2022; 10(4):724. https://doi.org/10.3390/pr10040724
Chicago/Turabian StyleYan, Guangxi, Jiang Chen, Yu Bai, Chengqing Yu, and Chengming Yu. 2022. "A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles" Processes 10, no. 4: 724. https://doi.org/10.3390/pr10040724
APA StyleYan, G., Chen, J., Bai, Y., Yu, C., & Yu, C. (2022). A Survey on Fault Diagnosis Approaches for Rolling Bearings of Railway Vehicles. Processes, 10(4), 724. https://doi.org/10.3390/pr10040724