A diagnostic algorithm diagnosing the failure of railway signal equipment

• Autorzy: Wu Yongcheng , Cao Dejin

Identyfikatory

DOI

10.29354/diag/143937

• Języki publikacji: EN

Abstrakty

EN

Failure of railway signal equipment can cause an impact on its normal operation, and it is necessary to make a timely diagnosis of the failure. In this study, the data of a railway bureau from 2016 to 2020 were studied as an example. Firstly, denoising and feature extraction were performed on the data; then the Adaptive Comprehensive Oversampling (ADASYN) method was used to synthesize minority class samples; finally, three algorithms, back-propagation neural network (BPNN), support vector machine (SVM) and C4.5 algorithms, were used for failure diagnosis. It was found that the three algorithms performed poorly in diagnosing the original data but performed significantly better in diagnosing the synthesized samples, among which the BPNN algorithm had the best performance. The average precision, recall rate and F1 score of the BPNN algorithm were 0.94, 0.92 and 0.93, respectively. The results verify the effectiveness of the BPNN algorithm for failure diagnosis, and the algorithm can be further promoted and applied in practice.

Słowa kluczowe

EN

failure diagnosis; railway signal equipment; denoising; feature extraction

PL

diagnostyka uszkodzeń; sygnalizacja kolejowa; odszumianie; ekstrakcja cech

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2021
• Tom: Vol. 22, No. 4
• Strony: 33--38
• Opis fizyczny: Bibliogr. 16 poz., tab., wykr.

Twórcy

autor

Wu Yongcheng

• Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

autor

Cao Dejin

• Lanzhou Institute of Technology, Lanzhou, Gansu 730050

Bibliografia

• 1. Cerrada M, Sánchez RV, Cabrera DR, Zurita GV, Li C. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal. Sensors 2015; 15(9): 23903-23926. http://dx.doi.org/10.3390/s150923903.
• 2. Cerrada M, Zurita G, Cabrera D, Sánchez R, Artés M, Li C. Fault diagnosis in spur gears based on genetic algorithm and random forest. Mechanical Systems & Signal Processing 2016; 70-71: 87-103.
• 3. Chine W, Mellit A, Lughi V, Malek A, Sulligoi G, Pavan AM. A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewable Energy 2016; 90: 501-512. http://dx.doi.org/10.1016/j.renene.2016.01.036.
• 4. Chine W, Mellit A, Lughi V, Malek A, Sulligoi G, Pavan AM. A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks. Renewable Energy 2016; 90: 501-512. http://dx.doi.org/10.1016/j.renene.2016.01.036.
• 5. Daubechies I, Lu J, Wu H T. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis. 2011;30(2):243-261. http://dx.doi.org/10.1016/j.acha.2010.08.002.
• 6. Gao Z, Cecati C, Ding S X. A Survey of Fault Diagnosis and Fault-Tolerant Techniques - Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches. IEEE Transactions on Industrial Electronics. 2015;62(6):3757-3767. http://dx.doi.org/10.1109/TIE.2015.2417501.
• 7. Gao Z, Ding S X, Cecati C. Real-time fault diagnosis and fault-tolerant control. IEEE Transactions on Industrial Electronics 2015; 62(6): 3752-3756. http://dx.doi.org/10.1109/TIE.2015.2417511.
• 8. Glowacz A. Acoustic based fault diagnosis of threephase induction motor. Applied Acoustics 2018; 137(AUG.):82-89. http://dx.doi.org/10.1016/j.apacoust.2018.03.010.
• 9. Jiang L, Liu Y, Li X, Chen A. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis. Shock & Vibration 2015;18(1-2):127-137. http://dx.doi.org/10.3233/SAV-2010-0572.
• 10. Li B, Wu S, Wang Z, et al. Railway Track Circuit Signal State Check Using Object Detection. Journal of Physics: Conference Series 2020; 1486(4): 042018 (7pp).
• 11. Ngoc P V, Ngoc C, Ngoc T, Dat ND. A C4.5 algorithm for English emotional classification. Evolving Systems. 2017:1-27. http://dx.doi.org/10.1007/s12530-017-9180-1.
• 12. Pu H, Xie A, Sun D W, Kamruzzaman M, Ma J. Application of Wavelet Analysis to Spectral Data for Categorization of Lamb Muscles. Food & Bioprocess Technology. 2015;8(1):1-16. http://dx.doi.org/10.1007/s11947-014-1393-8.
• 13. Shi J, Lee W J, Liu Y, Yang Y, Wang P. Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines. IEEE Transactions on Industry Applications 2015; 48(3): 1064-1069. http://dx.doi.org/10.1109/TIA.2012.2190816.
• 14. Tang B, He H. Kernel ADASYN: Kernel based adaptive synthetic data generation for imbalanced learning. Evolutionary Computation 2015: 664-671. http://dx.doi.org/10.1109/CEC.2015.7256954.
• 15. Zhang Y, Gao X, Katayama S. Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding. Journal of Manufacturing Systems 2015; 34: 53-59. http://dx.doi.org/10.1016/j.jmsy.2014.10.005.
• 16. Zhao L Y, Wang L, Yan R Q. Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy. Entropy 2015; 17(9):6447-6461. http://dx.doi.org/info:doi/10.3390/e17096447.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).