Abnormal data recognition is of great importance in structural health monitoring. Most of the existing studies focused on detecting obvious abnormal data, which has obvious abnormal time-domain waveform. Pseudo normal data, which is normally looking in time domain but chaotic in frequency domain, did not receive enough attention and were likely to be misclassified as normal data. As a result, structural performance may be incorrectly evaluated because pseudo normal data are not recognized and eliminated from the monitoring data. This study developed a novel quality evaluation framework for monitoring data of bridge dynamic response. The main novelty of the proposed framework is that the frequency-domain information is used to characterize the quality of the monitoring data so that the normal data, obvious abnormal data, and pseudo normal data can be accurately distinguished. In the framework, the frequency-domain information was obtained by fast Fourier transform (FFT), and the Gramian angular field images converted from FFT results were used to train a designed convolutional neural network (CNN). The cable acceleration data of the Waitan cable-stayed bridge were taken as an example to verify the accuracy of the proposed framework. Compared with the CNN models based on time-domain images and time-frequency stacked images, this framework can better recognize pseudo normal data from the monitoring data. In large-scale testing, the classification accuracy of all channels was more than 96%. Finally, two cable acceleration sensors of another cable-stayed bridge were used to demonstrate the feasibility of the framework in cross-object application. The results show that the framework has good accuracy and robustness in large-scale monitoring data quality evaluation and cross-object application.
JamaliSChanTHNguyenA, et al. Reliability-based load-carrying capacity assessment of bridges using structural health monitoring and nonlinear analysis. Struct Health Monit2019; 18(1): 20–34.
2.
ZhouYSunL. Insights into temperature effects on structural deformation of a cable-stayed bridge based on structural health monitoring. Struct Health Monit2019; 18(3): 778–791.
3.
MeiQGülM. A crowdsourcing-based methodology using smartphones for bridge health monitoring. Struct Health Monit2019; 18(5–6): 1602–1619.
4.
ZhangLZhaoHObrienEJ, et al. Virtual monitoring of orthotropic steel deck using bridge weigh-in-motion algorithm: case study. Struct Health Monit2019; 18(2): 610–620.
5.
FengSJiaJ. Acceleration sensor placement technique for vibration test in structural health monitoring using microhabitat frog-leaping algorithm. Struct Health Monit2018; 17(2): 169–184.
6.
NieZLinJLiJ, et al. Bridge condition monitoring under moving loads using two sensor measurements. Struct Health Monit2020; 19(3): 917–937.
7.
CanteroDØisethORønnquistA. Indirect monitoring of vortex-induced vibration of suspension bridge hangers. Struct Health Monit2018; 17(4): 837–849.
8.
TanXSunXChenW, et al. Investigation on the data augmentation using machine learning algorithms in structural health monitoring information. Struct Health Monit2021; 20(4): 2054–2068.
9.
ChaYJWangZ. Unsupervised novelty detection-based structural damage localization using a density peaks-based fast clustering algorithm. Struct Health Monit2018; 17(2): 313–324.
10.
BaoYTangZLiH, et al. Computer vision and deep learning-based data anomaly detection method for structural health monitoring. Struct Health Monit2019; 18(2): 401–421.
11.
TangZChenZBaoY, et al. Convolutional neural network-based data anomaly detection method using multiple information for structural health monitoring. Struct Control Health Monit2019; 26(1): e2296.
12.
ZhangYLeiY. Data anomaly detection of bridge structures using convolutional neural network based on structural vibration signals. Symmetry2021; 13(7): 1186.
13.
MaoJWangHSpencerBF. Toward data anomaly detection for automated structural health monitoring: exploiting generative adversarial nets and autoencoders. Struct Health Monit2021; 20(4): 1609–1626.
14.
AbdelghaniMFriswellMI. Sensor validation for structural systems with additive sensor faults. Struct Health Monit2004; 3(3): 265–275.
15.
WangHBahMJHammadM. Progress in outlier detection techniques: a survey. IEEE Access2019; 7: 107964–108000.
16.
GulMCatbasFN. Statistical pattern recognition for structural health monitoring using time series modeling: theory and experimental verifications. Mech Syst Signal Process2009; 23(7): 2192–2204.
17.
JiZ. Advancements of outlier detection: a survey. ICST Trans Scalable Inf Syst2013; 13(1): e2.
18.
YuenKVMuHQ. A novel probabilistic method for robust parametric identification and outlier detection. Probabilistic Eng Mech2012; 30(4): 48–59.
19.
WanHPNiYQ. Bayesian modeling approach for forecast of structural stress response using structural health monitoring data. J Struct Eng2018; 144(9): 04018130.
20.
ZhangYMWangHWanHP, et al. Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model. Struct Health Monit2021; 20(6): 2936–2952.
21.
YangXLateckiLJPokrajacD. Outlier detection with globally optimal exemplar-based GMM. In: Proceedings of the 2009 SIAM international conference on data mining (SDM), SIAM 2009, Sparks, NV, USA, 2009, pp. 145–154.
22.
TangXMYuanRXChenJ. Outlier detection in energy disaggregation using subspace learning and gaussian mixture model. Int J Control Autom2015; 8(8): 161–170.
23.
ChandolaVBanerjeeAKumarV. Anomaly detection: a survey. ACM Comput Surv2009; 41(3): 15.
24.
WangCZhangQTianQ, et al. Learning mobile manipulation through deep reinforcement learning. Sensors2020; 20(3): 939.
25.
PomerleauD. Efficient training of artificial neural networks for autonomous navigation. Neural Comput2014; 3(1): 88–97.
26.
PamuncakAGuoWSolimanKA, et al. Deep learning for bridge load capacity estimation in post-disaster and -conflict zones. R Soc Open Sci2019; 6: 190227.
27.
GuJGulMWuX. Damage detection under varying temperature using artificial neural networks. Struct Control Health Monit2017; 24: e1998.
28.
ZhouYLMaiaNMMSampaioRPC, et al. Structural damage detection using transmissibility together with hierarchical clustering analysis and similarity measure. Struct Health Monit2017; 16(6): 711–731.
29.
Zavala-MondragonLALamichhaneBZhangL., et al. CNN-SkelPose: a CNN-based skeleton estimation algorithm for clinical applications. J Ambient Intell Hum Comput2020; 11: 2369–2380.
30.
BhattDPatelCTalsaniaH, et al. CNN variants for computer vision: history, architecture, application, challenges and future scope. Electronics2021; 10(20): 2470.
31.
LinYZNieZHMaHW. Structural damage detection with automatic feature-extraction through deep learning. Comput-Aided Civ Infrastruct Eng2017; 32: 1025–1046.
32.
KangDChaYJ. Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging. Comput-Aided Civ Infrastruct Eng2018; 33: 885–902.
33.
KongSYFanJSLiuYF, et al. Automated crack assessment and quantitative growth monitoring. Comput-Aided Civ Infrastruct Eng2021; 36: 656–674.
34.
LiuGYNiuYBZhaoWJ, et al. Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN. Smart Struct Syst2022; 29(1): 53–62.
35.
NiFZhangJNooriMN. Deep learning for data anomaly detection and data compression of a long-span suspension bridge. Comput-Aided Civ Infrastruct Eng2020; 35: 685–700.
36.
WangZOatesT. Encoding time series as images for visual inspection and classification using tiled convolutional neural networks. In: Workshops at the twenty-ninth AAAI conference on artificial intelligence, Austin, TX, USA, 25–30 January 2015.
37.
OhBKGlisicBKimY, et al. Convolutional neural network-based wind induced response estimation model for tall buildings. Comput-Aided Civ Infrastruct Eng2019; 34: 843–858.
38.
ChaYJChoiWSuhG, et al. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civ Infrastruct Eng2018; 33: 731–747.
39.
TongZGaoJShaA, et al. Convolutional neural network for asphalt pavement surface texture analysis. Comput-Aided Civ Infrastruct Eng2018; 33: 1056–1072.
40.
ZhangSZhouLChenX, et al. Network-wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition. Comput-Aided Civ Infrastruct Eng2020; 35: 1132–1147.
41.
MeiQGülM. Multi-level feature fusion in densely connected deep-learning architecture and depth-first search for crack segmentation on images collected with smartphones. Struct Health Monit2020; 19(6): 1726–1744.
42.
ParkSBangSKimH, et al. Patch-based crack detection in black box images using convolutional neural networks. J Comput Civ Eng2019; 33(3): 04019017.1–04019017.11.
43.
FuMXuPLiX, et al. Fast crowd density estimation with convolutional neural networks. Eng Appl Artif Intell2015; 43: 81–88.
44.
ZhuYZhangCZhouD, et al. Traffic sign detection and recognition using fully convolutional network guided proposals. Neurocomputing2016; 214: 758–766.
45.
DragosKTheilerMMagalhaesF, et al. On-board data synchronization in wireless structural health monitoring systems based on phase locking. Struct Control Health Monit2018; 25: e2248.
46.
ChenGWOmenzetterPBeskhyrounS. Operational modal analysis of an eleven-span concrete bridge subjected to weak ambient excitations. Eng Struct2017; 151(15): 839–860.
47.
HouSDongBFanJ, et al. Variational mode decomposition based time-varying force identification of stay cables. Appl Sci2021; 11(3): 1254.
48.
SunZHYangP. Cable tension measurement of cable stayed bridge based on frequency method. Appl Mech Mater2013; 405–408: 1587–1592.
49.
ZhangAWangKCPLiB, et al. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput-Aided Civ Infrastruct Eng2017; 32(10): 805–819.
50.
HanYMiLHShenL, et al. A short-term wind speed prediction method utilizing novel hybrid deep learning algorithms to correct numerical weather forecasting. Appl Energy2022; 312(15): 118777.
51.
Kapociute-DzikieneJBalodisKSkadinsR. Intent detection problem solving via automatic DNN hyperparameter optimization. Appl Sci Basel2020; 10(21): 7426.
52.
HuWFHeYHLiuZY, et al. Toward a digital twin: time series prediction based on a hybrid ensemble empirical mode decomposition and BO-LSTM neural networks. J Mech Des2021; 143(5): 051705.
