journal article·2025-12

Intelligent Detection of Railway Axles Fatigue Crack Using Acoustic Emission-Stacked Denoising Autoencoders

Li Lin, Shuang Zhao, Xiaowen Tang, Wei Zhao

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Non-Destructive Testing Techniques · Ultrasonics and Acoustic Wave Propagation · Machine Fault Diagnosis Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (64)

Show source-provided references
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 ↗