Intelligent Detection of Railway Axles Fatigue Crack Using Acoustic Emission-Stacked Denoising Autoencoders
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OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Machine Fault Diagnosis Techniques
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References (64)
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1. Wang, Z., Wang, J., Wang, Y., et al.: An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition. Neurocomputing. 310, 213–222 (2018). https://doi.org/10.1016/j.neucom.2018.05.00210.1016/j.neucom.2018.05.002 ↗
2. He, S., Hu, D., Yu, G., et al.: Trackside acoustic detection of axle bearing fault using wavelet domain moving beamforming method. Appl. Acoust. 195, 108851 (2022). https://doi.org/10.1016/j.apacoust.2022.10885110.1016/j.apacoust.2022.108851 ↗
3. Kumar, A., Kumar, R.: Role of signal Processing, modeling and decision making in the diagnosis of rolling element bearing defect: A review. J. Nondestruct Eval. 38(5), 1–29 (2019). https://doi.org/10.1007/s10921-018-0543-810.1007/s10921-018-0543-8 ↗
4. Rolek, P., Bruni, S., Carboni, M.: Condition monitoring of railway axles based on low frequency vibrations. Int. J. Fatigue. 86, 88–97 (2016). https://doi.org/10.1016/j.ijfatigue.2016.03.00610.1016/j.ijfatigue.2016.03.006 ↗
5. Mineo, C., Cerniglia, D., Pantano, A.: Numerical study for a new methodology of flaws detection in train axles. Ultrasonics. 54, 841–849 (2014). https://doi.org/10.1016/j.ultras.2014.01.00510.1016/j.ultras.2014.01.005 ↗
6. Hassan, M., Bruni, S.: Experimental and numerical investigation of the possibilities for the structural health monitoring of railway axles based on acceleration measurements. Eng. Struct. 18, 902–919 (2019). https://doi.org/10.1016/j.engstruct.2019.01.02910.1016/j.engstruct.2019.01.029 ↗
7. Hwang, Y.-I., Lee, H., Kim, Y.-I., et al.: Methodology for mapping the residual stress field in serviced rails using LCR waves. J. Nondestruct Eval. 41(63) (2022). https://doi.org/10.1007/s10921-022-00895-w10.1007/s10921-022-00895-w ↗
8. Joseph, R., Giurgiutiu, V.: Analytical and experimental study of Fatigue-Crack-Growth AE signals in thin sheet metals. Sensors. 20(16), 5835 (2020). https://doi.org/10.3390/s2016583510.3390/s20165835 ↗
9. Ma, G., Du, Q.: Structural health evaluation of the prestressed concrete using advanced acoustic emission (AE) parameters. Constr. Build. Mater. 250, 118860 (2020). https://doi.org/10.1016/j.conbuildmat.2020.11886010.1016/j.conbuildmat.2020.118860 ↗
10. Hoseinlaghab, S., Farahani, M., Safarabadi, M., et al.: Tension-after-impact analysis and damage mechanism evaluation in laminated composites using AE monitoring. Mech. Syst. Signal. Process. 186, 109844 (2023). https://doi.org/10.1016/j.ymssp.2022.10984410.1016/j.ymssp.2022.109844 ↗
11. Martini, A., Troncossi, M., Rivola, A.: Leak detection in water-filled small-diameter polyethylene pipes by means of acoustic emission measurements. Appl. Sci. 7(2), 2 (2016). https://doi.org/10.3390/app605013510.3390/app6050135 ↗
12. Prajna, K., Mukhopadhyay, C.K.: Fractional fourier transform based adaptive filtering techniques for acoustic emission signal enhancement. J. Nondestruct Eval. 39(14) (2020). https://doi.org/10.1007/s10921-020-0658-610.1007/s10921-020-0658-6 ↗
13. Zhengjie, L., Weilei, M., Hao, N., et al.: Pressure vessel leakage detection method based on online acoustic emission signals. Int. J. Press. Vessel Pip. 65, 36–42 (2023). https://doi.org/10.1016/j.ijpvp.2023.10484910.1016/j.ijpvp.2023.104849 ↗
14. He, Y., Li, M., Meng, Z., et al.: An overview of acoustic emission inspection and monitoring technology in the key components of renewable energy systems. Mech. Syst. Signal. Process. 148, 107146 (2021). https://doi.org/10.1016/j.ymssp.2020.10714610.1016/j.ymssp.2020.107146 ↗
15. Erlinger, T., Kralovec, C., Schagerl, M.: Acoustic emission monitoring of atmospheric corrosion on aluminum aircraft structures at varying relative humidity. J. Nondestruct Eval. 41(3), 1–12 (2022). https://doi.org/10.1007/s10921-022-00906-w10.1007/s10921-022-00906-w ↗
16. Bruni, S., Carboni, M., Crivelli, D., et al.: A preliminary analysis about the application of acoustic emission and low frequency vibration methods to the structural health monitoring of railway axles. Compos. Struct. 33, 1–9 (2013). https://doi.org/10.1016/j.proeng.2013.12.05210.1016/j.proeng.2013.12.052 ↗
17. Jiang, C.-H., You, W., Wang, L.-S., et al.: Real-time monitoring of axle fracture of railway vehicles by translation invariant wavelet. Int. Conf. Mach. Learn. Cybern. 2409–2413 (2005). https://doi.org/10.1109/ICMLC.2005.152744010.1109/ICMLC.2005.1527440 ↗
18. Deng, X.J., Xui, G.J., Liu, S.Q.: Research on fatigue crack detection of rail vehicle axle based on acoustic emission. J. Mech. Eng. 51(12), 1–8 (2015). https://doi.org/10.3901/JME.2015.12.00110.3901/JME.2015.12.001 ↗
19. Marks, R., Stancu, A., Soua, S.: Fatigue Crack Growth Monitoring Using Acoustic Emission: A Case Study on Railway Axles. In: Proc. 2nd World Congr. Cond. Monit. 551–560 (2021). https://doi.org/10.1007/978-981-15-4657-3_5110.1007/978-981-15-4657-3_51 ↗
20. Castejon, C., Gomez, M.J., Corral, E.: Railway axle early fatigue crack detection through condition monitoring techniques. Sensors. 42(2), 1–12 (2023). https://doi.org/10.3390/s2302087010.3390/s23020870 ↗
21. Whetstone, G., Liu, T., Fudlallah, P., et al.: Experimental evaluation of crack evolution in rails using a phased array. J. Nondestruct Eval. 42(97) (2023). https://doi.org/10.1007/s10921-023-00997-z10.1007/s10921-023-00997-z ↗
22. Prem, P.R., Sanker, A.P., Sebastian, S., et al.: A review on application of acoustic emission testing during additive manufacturing. J. Nondestruct Eval. 42(96), 1–31 (2023). https://doi.org/10.1007/s10921-023-01005-010.1007/s10921-023-01005-0 ↗
23. Jiao, L., Zhao, J.: A survey on the new generation of deep learning in image processing. IEEE Access. 7, 172231–172263 (2019). https://doi.org/10.1109/ACCESS.2019.295589810.1109/ACCESS.2019.2955898 ↗
24. Otter, D.W., Medina, J.R., Kalita, J.K.: A survey of the usages of deep learning for natural Language processing. IEEE Trans. Neural Netw. Learn. Syst. 32(4), 604–624 (2020). https://doi.org/10.1109/TNNLS.2020.297967010.1109/TNNLS.2020.2979670 ↗
25. Subramanian, A.S., Weng, C., Watanabe, S., et al.: Deep learning based multi-source localization with source splitting and its effectiveness in multi-talker speech recognition. Comput. Speech Lang. 75, 101360 (2022). https://doi.org/10.1016/j.csl.2022.10136010.1016/j.csl.2022.101360 ↗
26. Salakhutdinov, R.: I. Murray On the quantitative analysis of deep belief networks. Proc. 25th Int. Conf. Mach. Learn. 872–879https://doi.org/10.1145/1390156.1390266 (2008)10.1145/1390156.1390266 ↗
27. Lin, L., Tang, X., Zhu, X., et al.: An acoustic emission identification model for train axle fatigue cracks based on deep belief network. Meas. Sci. Technol. 35(7), 076125 (2024). https://doi.org/10.1088/1361-6501/ad3b3010.1088/1361-6501/ad3b30 ↗
28. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature. 521(7553), 436–444 (2015). https://doi.org/10.1038/nature1453910.1038/nature14539 ↗
29. Mao, W., Feng, W., Liu, Y., et al.: A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mech. Syst. Signal. Process. 150, 107233 (2021). https://doi.org/10.1016/j.ymssp.2020.10723310.1016/j.ymssp.2020.107233 ↗
30. Yao, L., Ge, Z.: IEEE Trans. Ind. Electron. 99, 1–10 (2017). https://doi.org/10.1109/TIE.2017.2733448 Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application10.1109/TIE.2017.2733448 ↗
31. Chen, L., Ma, Y., Hu, H., et al.: An effective fault diagnosis approach for bearing using stacked de-noising auto-encoder with structure adaptive adjustment. Measurement. 214, 112774 (2023). https://doi.org/10.1016/j.measurement.2023.11277410.1016/j.measurement.2023.112774 ↗
32. Du, X., Jia, L., Haq, U.I.: Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery. Measurement. 188, 110545 (2022). https://doi.org/10.1016/j.measurement.2021.11054510.1016/j.measurement.2021.110545 ↗
33. Liu, Y., Duan, L., Yuan, Z., et al.: An intelligent fault diagnosis method for reciprocating compressors based on LMD and SDAE. Sensors. 19(5), 1041 (2019). https://doi.org/10.3390/s1905104110.3390/s19051041 ↗
34. Zhang, X., Wu, P., He, J., et al.: Floating offshore wind turbine fault diagnosis using stacked denoising autoencoder with Temporal information. Trans. Inst. Meas. Control. 40(3), 1–12 (2021). https://doi.org/10.1177/0142331221103256010.1177/01423312211032560 ↗
35. Wu, Z., Yan, H., Zhan, X., et al.: Gearbox fault diagnosis based on optimized stacked denoising auto encoder and kernel extreme learning machine. Processes. 42(1), 1–12 (2023). https://doi.org/10.3390/pr1101028010.3390/pr11010280 ↗
36. Felix, L.O., de Sá Só Martins, D.H., Monteiro, U.A.B.V., et al.: Multiple fault diagnosis in a wind turbine gearbox with autoencoder data augmentation and KPCA dimension reduction. J. Nondestruct Eval. 43, 114 (2024). https://doi.org/10.1007/s10921-024-01131-310.1007/s10921-024-01131-3 ↗
37. Huang, K., Li, S., Dai, P., et al.: SDARE: A stacked denoising autoencoder method for game dynamics network structure reconstruction. Neural Netw. 126, 143–152 (2020). https://doi.org/10.1016/j.neunet.2020.02.01810.1016/j.neunet.2020.02.018 ↗
38. Zhang, C., Hu, D., Yang, T.: Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost. Reliab. Eng. Syst. Saf. 222, 108445 (2022). https://doi.org/10.1016/j.ress.2022.10844510.1016/j.ress.2022.108445 ↗
39. Wong, M.L., Seng, K., Wong, P.K.: Cost-sensitive ensemble of stacked denoising autoencoders for class imbalance problems in business domain. Expert Syst. Appl. 141, 112918 (2020). https://doi.org/10.1016/j.eswa.2019.11291810.1016/j.eswa.2019.112918 ↗
40. Zhao, Y., Yang, X., Huang, J., et al.: Ensemble targeted stacked denoising autoencoders with mutual information constraint for rotating machinery fault diagnosis. IEEE Trans. Ind. Inf. 21(2), 1329–1340 (2025). https://doi.org/10.1109/TII.2024.123456710.1109/TII.2024.1234567 ↗
41. Ou, J., Li, H., Huang, G., et al.: Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine. Measurement. 108153 (2020). https://doi.org/10.1016/j.measurement.2020.10815310.1016/j.measurement.2020.108153 ↗
42. Xu, F., Yang, F., Fei, Z., et al.: Life prediction of lithium-ion batteries based on stacked denoising autoencoders. Reliab. Eng. Syst. Saf. 208, 107396 (2021). https://doi.org/10.1016/j.ress.2021.10739610.1016/j.ress.2021.107396 ↗
43. Zhou, P., Chen, G., Wang, M., et al.: Sediment classification of acoustic backscatter image based on stacked denoising autoencoder and modified extreme learning machine. Remote Sens. 12(24), 3762 (2020). https://doi.org/10.3390/rs1224376210.3390/rs12243762 ↗
44. Lin, S., Dong, M., Cao, X., et al.: The pre-trained explainable deep learning model with stacked denoising autoencoders for slope stability analysis. Eng. Anal. Bound. Elem. 163, 406–425 (2024). https://doi.org/10.1016/j.enganabound.2024.01.00110.1016/j.enganabound.2024.01.001 ↗
45. Song, G., Zhang, J., Zhu, K., et al.: Tool wear monitoring based on multi-kernel Gaussian process regression and stacked multilayer denoising autoencoders. Mech. Syst. Signal. Process. 186, 109851 (2023). https://doi.org/10.1016/j.ymssp.2023.10985110.1016/j.ymssp.2023.109851 ↗
46. ALICE Collaboration: Skewness and kurtosis of mean transverse momentum fluctuations at the LHC energies. Phys. Lett. B. 850, 138541 (2024). https://doi.org/10.1016/j.physletb.2024.13854110.1016/j.physletb.2024.138541 ↗
47. Sukriti, et al.: Epilepsy seizure detection using kurtosis based vmd’s parameters selection and bandwidth features. Biomed. Signal. Process. Control. 64, 102255 (2021). https://doi.org/10.1016/j.bspc.2021.10225510.1016/j.bspc.2021.102255 ↗
48. Zheng, et al.: Kurtosis and crest factor simultaneous control for non-Gaussian random vibration test. Mech. Syst. Signal. Process. 222, 111760 (2025). https://doi.org/10.1016/j.ymssp.2024.11176010.1016/j.ymssp.2024.111760 ↗
49. Vashishtha, et al.: Optimization of spectral kurtosis-based filtering through flow direction algorithm for early fault detection. Measurement. 241, 115737 (2025). https://doi.org/10.1016/j.measurement.2025.11573710.1016/j.measurement.2025.115737 ↗
50. Kot, P., Muradov, M., Gkantou, M., et al.: Recent advancements in non-destructive testing techniques for structural health monitoring. Appl. Sci. 11(6), 2750 (2021). https://doi.org/10.3390/app1106275010.3390/app11062750 ↗
51. Islam, M., Sohaib, M., Kim, J., et al.: Crack classification of a pressure vessel using feature selection and deep learning methods. Sensors. 18, 4379 (2018). https://doi.org/10.3390/s1812437910.3390/s18124379 ↗
52. Gionnouli, E., Huang, Z., Amini, A., et al.: Remote condition monitoring of railway axle bearings based on data fusion from several sensors. In: Proc. 1st World Congr. Cond. Monit. Brit. Inst. Non-Destr. Test. (2017). https://www.bindt.org/events/wccm2017/
53. Chorunetratsamee, S., Poopat, B., Jirarungsatean, C.: Feasibility study of acoustic emission monitoring of hot cracking in GTAW weld. Key Eng. Mater. 545, 236–240 (2013). https://doi.org/10.4028/www.scientific.net/KEM10.4028/www.scientific.net/KEM.545.236 ↗
54. Fu, S., Zhong, S., Lin, L., et al.: A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection. Eng. Appl. Artif. Intell. 101, 104199 (2021). https://doi.org/10.1016/j.engappai.2021.10419910.1016/j.engappai.2021.104199 ↗
55. Amini, N., Zhu, Q.: Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network. Neurocomputing. 488, 618–633 (2022). https://doi.org/10.1016/j.neucom.2021.03.14010.1016/j.neucom.2021.03.140 ↗
56. Li, J., Struzik, Z., Zhang, L., et al.: Feature learning from incomplete EEG with denoising autoencoder. Neurocomputing. 165, 23–31 (2015). https://doi.org/10.1016/j.neucom.2015.03.00610.1016/j.neucom.2015.03.006 ↗
57. Sui, D., Lang, X., Lv, Y.: Abnormal diagnosis of acoustic emission pipeline working conditions based on PSO-LSTM-DAE. Eng. Res. Express. 5(3) (2023). https://doi.org/10.1088/2631-8695/aceb2e10.1088/2631-8695/aceb2e ↗
58. Xing, C., Ma, L., Yang, X.: Stacked Denoise autoencoder based feature extraction and classification for hyperspectral images. J. Sens. 2016 (2016). https://doi.org/10.1155/2016/363294310.1155/2016/3632943 ↗
59. Hasan, B.M.S., Abdulazeez, A.M.: A review of principal component analysis algorithm for dimensionality reduction. J. Soft Comput. Data Min. 2, 20–30 (2021). https://jscdm.org.my/index.php/jscdm/article/view/40
60. Tefas, A., Pitas, I.: Principal component analysis. In: Intell. Syst. 16-11-16-19. CRC Press (2018). https://doi.org/10.1201/978042950706210.1201/9780429507062 ↗
61. Brereton, R.G., Lloyd, G.R.: Support vector machines for classification and regression. Anal. Chim. Acta. 135, 230–267 (2010). https://doi.org/10.1016/j.aca.2010.07.01310.1016/j.aca.2010.07.013 ↗
62. Suthaharan, S., Suthaharan, S.: Support vector machine. Mach. Learn. Models Algorithms Big Data Classif. 207–235 (2016). https://doi.org/10.1007/978-1-4899-7641-3_910.1007/978-1-4899-7641-3_9 ↗
63. Adhya, D., Chatterjee, S., Chakraborty, A.K.: Performance assessment of selective machine learning techniques for improved PV array fault diagnosis. Sustain. Energy Grids Netw. 29, 100582 (2022). https://doi.org/10.1016/j.segan.2021.10058210.1016/j.segan.2021.100582 ↗
64. Lei, Y., Jia, F., Lin, J., et al.: An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans. Ind. Electron. 63, 3137–3147 (2016). https://doi.org/10.1109/TIE.2016.251932510.1109/TIE.2016.2519325 ↗