1 |
Ni, Y.Q., Xia, Y., Liao, W.Y. and Ko, J.M. (2009), "Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower", Struct. Control Health Monitor., 16(1), 73-98. https://doi.org/10.1002/stc.303
DOI
|
2 |
Rabatel, J., Bringay, S. and Poncelet, P. (2011), "Anomaly detection in monitoring sensor data for preventive maintenance", Expert Syst. Applicat., 38(6), 7003-7015. https://doi.org/10.1016/j.eswa.2010.12.014
DOI
|
3 |
Smarsly, K. and Law, K.H. (2014), "Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy", Adv. Eng. Software, 73, 1-10. https://doi.org/10.1016/j.advengsoft.2014.02.005
DOI
|
4 |
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2014), "Dropout: a simple way to prevent neural networks from overfitting", J. Mach. Learn. Res., 15(1), 1929-1958.
|
5 |
Tang, Z., Chen, Z., Bao, Y. and Li, H. (2019), "Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring", Struct. Control Health Monitor., 26(1), p. e2296. https://doi.org/10.1002/stc.2296
DOI
|
6 |
Thiyagarajan, K., Kodagoda, S. and Van Nguyen, L. (2017), "Predictive analytics for detecting sensor failure using autoregressive integrated moving average model", Proceedings of 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, Cambodia, June.
|
7 |
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. and Polosukhin, I. (2017), "Attention is all you need", arXiv. https://doi.org/10.48550/arXiv.1706.03762
DOI
|
8 |
Wan, H.P. and Ni, Y.Q. (2018), "Bayesian modeling approach for forecast of structural stress response using structural health monitoring data", J. Struct. Eng., 144(9). p. 04018130. https://doi.org/10.1061/(asce)st.1943-541x.0002085
DOI
|
9 |
Wu, B., Huang, Y. and Li, H. (2015), Sparse reconstruction of flaw signal from noisy ultrasonic data: A bayesian framework.
|
10 |
Xia, Y., Chen, B., Zhou, X.Q. and Xu, Y.L. (2013), "Field monitoring and numerical analysis of Tsing Ma Suspension Bridge temperature behavior", Struct. Control Health Monitor., 20(4), 560-575. https://doi.org/10.1002/stc.515
DOI
|
11 |
Yi, T.H., Li, H.N., Song, G. and Guo, Q. (2016), "Detection of shifts in GPS measurements for a long-span bridge using CUSUM chart", Int. J. Struct. Stabil. Dyn., 16(04), p. 1640024. https://doi.org/10.1142/s0219455416400241
DOI
|
12 |
Yuen, K.V. and Mu, H.Q. (2012), "A novel probabilistic method for robust parametric identification and outlier detection", Probabil. Eng. Mech., 30, 48-59. https://doi.org/10.1016/j.probengmech.2012.06.002
DOI
|
13 |
Bao, Y., Chen, Z., Wei, S., Xu, Y., Tang, Z. and Li, H. (2019), "The state of the art of data science and engineering in structural health monitoring", Engineering, 5(2), 234-242. https://doi.org/10.1016/j.eng.2018.11.027
DOI
|
14 |
Abdelghani, M. and Friswell, M.I. (2004), "Sensor validation for structural systems with additive sensor faults", Struct. Health Monitor., 3(3), 265-275. https://doi.org/10.1177/1475921704045627
DOI
|
15 |
Arul, M. and Kareem, A. (2020), "Data anomaly detection for structural health monitoring of bridges using shapelet transform". https://doi.org/10.48550/arXiv.2009.00470
DOI
|
16 |
Bao, Y., Tang, Z., Li, H. and Zhang, Y. (2018), "Computer vision and deep learning-based data anomaly detection method for structural health monitoring", Struct. Health Monitor., 18(2), 401-421. https://doi.org/10.1177/1475921718757405
DOI
|
17 |
Chen, W.H., Lu, Z.R., Lin, W., Chen, S.H., Ni, Y.Q., Xia, Y. and Liao, W.Y. (2011), "Theoretical and experimental modal analysis of the Guangzhou New TV Tower", Eng. Struct., 33(12), 3628-3646. https://doi.org/10.1016/j.engstruct.2011.07.028
DOI
|
18 |
Chenglin, Z., Xuebin, S., Songlin, S. and Ting, J. (2011), "Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine", Expert Syst. Applicat., 38(8), 9908-9912. https://doi.org/10.1016/j.eswa.2011.02.043
DOI
|
19 |
Fan, G., Li, J. and Hao, H. (2019), "Lost data recovery for structural health monitoring based on convolutional neural networks", Struct. Control Health Monitor., 26(10), p. e2433. https://doi.org/10.1002/stc.2433
DOI
|
20 |
Cross, E.J., Koo, K.Y., Brownjohn, J.M.W. and Worden, K. (2013), "Long-term monitoring and data analysis of the Tamar Bridge", Mech. Syst. Signal Process., 35(1), 16-34. https://doi.org/10.1016/j.ymssp.2012.08.026
DOI
|
21 |
Fan, G., Li, J. and Hao, H. (2021a), "Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks", Struct. Health Monitor., 20(4), 1373-1391. https://doi.org/10.1177/1475921720916881
DOI
|
22 |
Fan, G., Li, J., Hao, H. and Xin, Y. (2021b), "Data driven structural dynamic response reconstruction using segment based generative adversarial networks", Eng. Struct., 234, 111970. https://doi.org/10.1016/j.engstruct.2021.111970
DOI
|
23 |
Fu, Y., Peng, C., Gomez, F., Narazaki, Y. and Spencer Jr, B.F. (2019), "Sensor fault management techniques for wireless smart sensor networks in structural health monitoring", Struct. Control Health Monitor., 26(7), p. e2362. https://doi.org/10.1002/stc.2362
DOI
|
24 |
Hou, J., Jiang, H., Wan, C., Yi, L., Gao, S., Ding, Y. and Xue, S. (2022), "Deep learning and data augmentation based data imputation for structural health monitoring system in multi-sensor damaged state", Measurement, 196, 111206. https://doi.org/10.1016/j.measurement.2022.111206
DOI
|
25 |
Huang, Y., Beck, J.L., Wu, S. and Li, H. (2016), "Bayesian compressive sensing for approximately sparse signals and application to structural health monitoring signals for data loss recovery", Probabil. Eng. Mech., 46, 62-79. https://doi.org/10.1016/j.probengmech.2016.08.001
DOI
|
26 |
Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q. (2017), "Densely connected convolutional networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, July.
|
27 |
Kullaa, J. (2013), "Detection, identification, and quantification of sensor fault in a sensor network", Mech. Syst. Signal Process., 40(1), 208-221. https://doi.org/10.1016/j.ymssp.2013.05.007
DOI
|
28 |
Ibarguengoytia, P.H., Sucar, L.E. and Vadera, S. (2007), "Real time intelligent sensor validation", IEEE Power Eng. Rev., 21(9), 63-64. https://doi.org/10.1109/MPER.2001.4311630
DOI
|
29 |
Kerschen, G., De Boe, P., Golinval, J.C. and Worden, K. (2004), "Sensor validation using principal component analysis", Smart Mater. Struct., 14(1), p. 36. https://doi.org/10.1088/0964-1726/14/1/004
DOI
|
30 |
Krawczyk, B. (2016), "Learning from imbalanced data: open challenges and future directions", Progress Artif. Intell., 5(4), 221-232. https://doi.org/10.1007/s13748-016-0094-0
DOI
|
31 |
Lei, X., Xia, Y., Wang, A., Jian, X., Zhong, H. and Sun, L. (2023), "Mutual information based anomaly detection of monitoring data with attention mechanism and residual learning", Mech. Syst. Signal Process., 182, 109607. https://doi.org/10.1016/j.ymssp.2022.109607
DOI
|
32 |
Lin, Y.Z., Nie, Z.H. and Ma, H.W. (2017), "Structural damage detection with automatic feature-extraction through deep learning", Comput.-Aided Civil Infrastr. Eng., 32(12), 1025-1046. https://doi.org/10.1111/mice.12313
DOI
|
33 |
Lo, C., Bai, Y., Liu, M. and Lynch, J.P. (2015), "Efficient Sensor Fault Detection Using Group Testing", ArXiv, abs/1501.04152. https://doi.org/10.1109/DCOSS.2013.57
DOI
|
34 |
Mao, J., Wang, H. and Spencer Jr, B.F. (2020), "Toward data anomaly detection for automated structural health monitoring: Exploiting generative adversarial nets and autoencoders", Struct. Health Monitor., 20(4), 1609-1626. https://doi.org/10.1177/1475921720924601
DOI
|