Review of Vibration-Based Structural Health Monitoring Using Deep Learning
Source abstract
Abstract
With the rapid progress in the deep learning technology, it is being used for vibration-based structural health monitoring. When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure. The measured vibration responses show large deviation in spectral and transient characteristics for systems to be monitored. Consequently, the diagnosis using vibration requires complete understanding of the extracted features to discard the influence of surrounding environments or unnecessary variations. The deep-learning-based algorithms are expected to find increasing application in these complex problems due to their flexibility and robustness. This review provides a summary of studies applying machine learning algorithms for fault monitoring. The vibration factors were used to categorize the studies. A brief interpretation of deep neural networks is provided to guide further applications in the structural vibration analysis.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Structural Health Monitoring Techniques · Machine Fault Diagnosis Techniques · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Hanyang University, Seoul 04763, Korea
References (100+)
Show source-provided references
1. The application of machine learning to structural health monitoring10.1098/rsta.2006.1938 ↗
2. Rytter, A. (1993). Vibrational Based Inspection of Civil Engineering Structures, Department of Building Technology and Structural Engineering, Aalborg University.
3. Deep learning: Methods and applications10.1561/2000000039 ↗
4. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform10.1016/j.jsv.2007.08.023 ↗
5. Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine10.1016/S0378-7796(03)00154-8 ↗
6. Konar, P., Panigrahy, P.S., and Chattopadhyay, P. (2016, January 9–11). Tri-Axial Vibration Analysis Using Data Mining for Multi Class Fault Diagnosis in Induction Motor. Proceedings of the Conference on Mining Intelligence and Knowledge Exploration, Hyderabad, India.10.1007/978-3-319-26832-3_52 ↗
7. Scalable and Unsupervised Feature Engineering Using Vibration-Imaging and Deep Learning for Rotor System Diagnosis10.1109/TIE.2017.2752151 ↗
8. Structural vibration-based classification and prediction of delamination in smart composite laminates using deep learning neural network10.1016/j.compositesb.2018.12.118 ↗
9. A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm10.1016/j.tafmec.2019.102240 ↗
10. Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm10.1016/j.engfracmech.2018.09.032 ↗
11. Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization10.1016/j.crme.2017.11.008 ↗
12. Damage detection and localization in composite beam structures based on vibration analysis
13. Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm10.1016/j.compstruct.2017.12.058 ↗
14. Multiple Damage Detection in Composite Beams Using Particle Swarm Opttmization and Genetic Algorithm
15. Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis10.1016/j.jsv.2019.02.017 ↗
16. A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm10.1016/j.crme.2018.09.003 ↗
17. Damage assessment in structures using combination of a modified Cornwell indicator and genetic algorithm10.1016/j.engstruct.2018.09.070 ↗
18. Damage detection in concrete by Fourier and wavelet analyses10.1061/(ASCE)0733-9399(2003)129:5(571) ↗
19. Gearbox health monitoring through multiresolution Fourier transform of vibration and current signals10.1177/1475921706058002 ↗
20. Non-stationary signal processing for bearing health monitoring10.1504/IJMR.2006.010701 ↗
21. Wang, Y., Loh, K.J., Lynch, J.P., Fraser, M., Law, K., and Elgamal, A. (2006, January 16–17). Vibration monitoring of the Voigt Bridge using wired and wireless monitoring systems. Proceedings of the 4th China-Japan-US symposium on structural control and monitoring, Hangzhou, China.
22. EMD-based stochastic subspace identification of structures from operational vibration measurements10.1016/j.engstruct.2005.04.016 ↗
23. Rotational machine health monitoring and fault detection using EMD-based acoustic emission feature quantification10.1109/TIM.2011.2179819 ↗
24. Structural health monitoring using empirical mode decomposition and the Hilbert phase10.1016/j.jsv.2005.10.024 ↗
25. Cepstrum Analysis: An Advanced Technique in Vibration Analysis of Defects in Rotating Machinery10.14429/dsj.44.4151 ↗
26. Damage identification based on response-only measurements using cepstrum analysis and artificial neural networks10.1177/1475921714542890 ↗
27. Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network10.1007/s12206-009-0730-8 ↗
28. Gearbox fault diagnosis based on Cepstrum and Envelop demodulation
29. A structural health monitoring strategy using cepstral features10.1016/j.jsv.2014.04.062 ↗
30. An approach to vibration analysis using wavelets in an application of aircraft health monitoring10.1016/j.ymssp.2006.06.008 ↗
31. Yen, G.G., and Lin, K.-C. (1999, January 7–10). Conditional health monitoring using vibration signatures. Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No. 99CH36304), Čačak, Serbia.
32. Enabling health monitoring approach based on vibration data for accurate prognostics10.1109/TIE.2014.2327917 ↗
33. Wavelet-based approach for structural damage detection10.1061/(ASCE)0733-9399(2000)126:7(677) ↗
34. Application of ANN, fuzzy logic and wavelet transform in machine fault diagnosis using vibration signal analysis10.1108/13552511011048922 ↗
35. Greitzer, F.L., Kangas, L.J., Terrones, K.M., Maynard, M.A., Wilson, B.W., Pawlowski, R.A., Sisk, D.R., and Brown, N.B. (September, January 30). Gas turbine engine health monitoring and prognostics. Proceedings of the International Society of Logistics (SOLE) 1999 Symposium, Las Vegas, NA, USA.
36. Health monitoring and diagnosis for flexible structures with PVDF piezoelectric film sensor array10.1106/Q7GH-HRAM-C89X-9BRA ↗
37. MUSIC-ANN analysis for locating structural damages in a truss-type structure by means of vibrations10.1111/j.1467-8667.2012.00777.x ↗
38. Structural health monitoring of bridges: A model-free ANN-based approach to damage detection10.1007/s13349-017-0252-5 ↗
39. Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)10.1016/j.asoc.2011.03.014 ↗
40. Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression10.1109/TIM.2014.2330494 ↗
41. A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box10.1016/j.eswa.2007.08.026 ↗
42. Bulut, A., Singh, A.K., Shin, P., Fountain, T., Jasso, H., Yan, L., and Elgamal, A. (2006, January 1–2). Real-time nondestructive structural health monitoring using support vector machines and wavelets. Proceedings of the Advanced Sensor Technologies for Nondestructive Evaluation and Structural Health Monitoring, San Diego, CA, USA.10.1117/12.597685 ↗
43. Electro-mechanical impedance-based wireless structural health monitoring using PCA-data compression and k-means clustering algorithms10.1177/1045389X07077400 ↗
44. Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis10.1016/j.jsv.2005.11.017 ↗
45. PCA-based feature selection scheme for machine defect classification10.1109/TIM.2004.834070 ↗
46. Golinval, J.-C., De Boe, P., Yan, A., and Kerschen, G. (2004, January 25–30). Structural damage detection based on PCA of vibration measurements. Proceedings of the 58th Meeting of the Society for Machinery, Failure Prevention Tech, Virginia Beach, Virginia.
47. Tian, J., Azarian, M.H., and Pecht, M. (2020, January 1–3). Anomaly detection using self-organizing maps-based k-nearest neighbor algorithm. Proceedings of the European Conference of the Prognostics and Health Management Society, Turin, Italy.
48. A clustering approach for structural health monitoring on bridges10.1007/s13349-016-0160-0 ↗
49. Rolling element bearing fault detection in industrial environments based on a K-means clustering approach10.1016/j.eswa.2010.08.083 ↗
50. A spectral-based clustering for structural health monitoring of the Sydney Harbour Bridge10.1016/j.ymssp.2016.10.033 ↗
51. Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection10.1016/j.ymssp.2011.06.011 ↗
52. Damage detection in composite materials using frequency response methods10.1016/S1359-8368(01)00050-6 ↗
53. Damage detection using finite element and laser operational deflection shapes10.1016/S0168-874X(01)00061-0 ↗
54. ART-based multiple neural networks for monitoring offshore platforms10.1016/0141-1187(96)00024-7 ↗
55. Damage assessment of jacketed RC columns using vibration tests10.1061/(ASCE)0733-9445(1999)125:3(265) ↗
56. Damage identification in a ship’s structure using neural networks10.1016/S0029-8018(01)00077-4 ↗
57. Drilling wear detection and classification using vibration signals and artificial neural network10.1016/S0890-6955(03)00023-3 ↗
58. Induction machine condition monitoring using neural network modeling10.1109/TIE.2006.888786 ↗
59. Intelligent condition monitoring of a gearbox using artificial neural network10.1016/j.ymssp.2006.08.005 ↗
60. Neural networks in mechanics of structures and materials–new results and prospects of applications10.1016/S0045-7949(01)00083-9 ↗
61. Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor10.1006/mssp.1997.0090 ↗
62. Roy, S., Chang, F., Lee, S., Pollock, P., and Janapati, V. (2013). A novel machine-learning approach for structural state identification using ultrasonic guided waves. Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures, CRC Press.10.1201/b16387-48 ↗
63. Roy, S., Lonkar, K., Janapati, V., and Chang, F.-K. (2017, January 5–8). Physics based temperature compensation strategy for structural health monitoring. Proceedings of the 8th International Workshop on Structural Health Monitoring, Brisbane, Australia.
64. Structural health monitoring system based on diffracted Lamb wave analysis by multiresolution processing10.1088/0964-1726/10/3/312 ↗
65. Zhan, C., Ji, S., Liu, Y., Zhu, L., Shi, Y., and Ren, F. (2018, January 23–26). Winding Mechanical Fault Diagnosis Technique of Power Transformer Based on Time-Frequency Vibration Analysis. Proceedings of the 2018 Condition Monitoring and Diagnosis (CMD), Perth, Australia.10.1109/CMD.2018.8535963 ↗
66. Hilbert–Huang Transform-Based Vibration Signal Analysis for Machine Health Monitoring10.1109/TIM.2006.887042 ↗
67. Fault Diagnosis of On-Load Tap-Changer in Converter Transformer Based on Time–Frequency Vibration Analysis10.1109/TIE.2016.2524399 ↗
68. Wang, J., and Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural. Netw. Vis. Recognit., 11. Available online: https://s3.amazonaws.com/academia.edu.documents/55344950/data_augmentation.pdf?response-content-disposition=inline%3B%20filename%3DThe_Effectiveness_of_Data_Augmentation_i.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWOWYYGZ2Y53UL3A%2F20200302%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20200302T044054Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=8dcde12bdd37417c6a738530f006a6e13ffc872cea7a2543a24a96ec9f4ccaef.
69. Gan, Z., Henao, R., Carlson, D., and Carin, L. (2016, January 9–11). Learning deep sigmoid belief networks with data augmentation. Proceedings of the Artificial Intelligence and Statistics, Cadiz, Spain.
70. Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2017). Random erasing data augmentation. arXiv.
71. Antoniou, A., Storkey, A., and Edwards, H. (2017). Data augmentation generative adversarial networks. arXiv.
72. Toh, G., Gwon, J., and Park, J. (2019). Determination of Clamping Force Using Bolt Vibration Responses during the Tightening Process. Appl. Sci., 9.10.3390/app9245379 ↗
73. Principal component analysis and exploratory factor analysis10.1177/096228029200100105 ↗
74. Müller, A.C., and Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists, O’Reilly Media, Inc.
75. Müllner, D. (2011). Modern hierarchical, agglomerative clustering algorithms. arXiv.
76. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2–4). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Kdd, Portland, OR, USA.
77. Spatio-temporal outlier detection in large databases10.2498/cit.2006.04.04 ↗
78. Patterson, J., and Gibson, A. (2017). Deep Learning: A Practitioner’s Approach, O’Reilly Media, Inc.
79. Ng, A. (2020, February 27). Sparse Autoencoder. Available online: http://ailab.chonbuk.ac.kr/seminar_board/pds1_files/sparseAutoencoder.pdf.
80. Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008, January 5–9). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th international conference on Machine learning, Helsinki, Finland.10.1145/1390156.1390294 ↗
81. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
82. Rifai, S., Vincent, P., Muller, X., Glorot, X., and Bengio, Y. (July, January 28). Contractive auto-encoders: Explicit invariance during feature extraction. Proceedings of the 28th International Conference on International Conference on Machine Learning, Bellevue, WA, USA.
83. Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv.
84. Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv.
85. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Paul Smolley, S. (2005, January 17–121). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Beijing, China.
86. Zhao, J., Mathieu, M., and LeCun, Y. (2016). Energy-based generative adversarial network. arXiv.
87. Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan. arXiv.
88. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
89. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (December, January 3). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in neural information processing systems, Stateline, NV, USA.
90. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
91. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (June, January 24). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.
92. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
93. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
94. Long short-term memory10.1162/neco.1997.9.8.1735 ↗
95. Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv.
96. Deep learning and its applications to machine health monitoring10.1016/j.ymssp.2018.05.050 ↗
97. A hydraulic fault diagnosis method based on sliding-window spectrum feature and deep belief network10.21595/jve.2017.18549 ↗
98. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method10.1109/TIE.2017.2774777 ↗
99. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification10.1016/j.sigpro.2016.07.028 ↗
100. Szabó, J.Z., and Bakucz, P. (July, January 30). Identification of nonlinearity in knocking vibration signals of large gas engine by deep learning. Proceedings of the 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES), Budapest, Hungary.10.1109/INES.2016.7555146 ↗