Adaptive reference updating for vibration-based structural health monitoring under varying environmental conditions

Jin, Seung-Seop; Cho, Su Jin; Jung, Hyung‐Jo

doi:10.1016/j.compstruc.2015.06.001

Cited by 26 publications

(13 citation statements)

References 18 publications

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“…Adaptive principal component analysis (APCA) is one of the most representative ASPMs. The underlying principle of APCA is that older data cannot represent the current normal condition 12 and thus the influence of the older data is reduced to describe the normal condition. The APCA can be classified into two algorithms: 29,31 (1) recursive principal component analysis (RPCA) using recursive adaptation techniques 17–19,27,30 and (2) Moving window linear PCA (MWPCA) using moving window techniques.…”

Section: Proposed Methods Using Online Learning Algorithm With Variablmentioning

confidence: 99%

“…The APCA can be classified into two algorithms: 29,31 (1) recursive principal component analysis (RPCA) using recursive adaptation techniques 17–19,27,30 and (2) Moving window linear PCA (MWPCA) using moving window techniques. 12,15,18,23,28–31 The MWPCA uses the moving window along the sequential data stream to adapt the intrinsic change of the normal condition. On the other hand, the RPCA has an advanced version of the IPCA.…”

Section: Proposed Methods Using Online Learning Algorithm With Variablmentioning

confidence: 99%

“…However, the CPV is a subjective approach, so that the value of the CPV was typically determined from 80% to 99% in the previous studies. 3,12,17–19 Rather than using the heuristic choice of the CPV, this study uses the eigengap technique 20–23 for the optimal number of PCs. When eigenvalues are ranked in descending order, the eigengap is calculated as…”

Section: Pca-based Spmmentioning

confidence: 99%

“…Monitoring performance may be quite disastrous, 11,12 if the training data is not sufficiently informative about potential undamaged states under the environmental variability. Typically, environmental variability on DSFs is incompletely understood.…”

Section: Introductionmentioning

confidence: 99%

“…The feasibility of the ASPM with the fixed moving window size was numerically evaluated for output-only SHM. 12 However, the limitations of the previous method are as follows: (1) the previous method cannot work well, if the non-linear dependencies among features are observed; and (2) using the fixed moving window size is not suitable to reflect the change rate of the normal condition. The proposed method of this study is an improved version of the ASPM to address these limitations.…”

Section: Introductionmentioning

confidence: 99%

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Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

Jin

Jung

2017

Structural Health Monitoring

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Damage-sensitive features such as natural frequencies are widely used for structural health monitoring; however, they are also influenced by the environmental condition. To address the environmental effect, principal component analysis is widely used. Before performing principal component analysis, the training data should be defined for the normal condition (baseline model) under environmental variability. It is worth noting that the natural change of the normal condition may exist due to an intrinsic behavior of the structural system. Without accounting for the natural change of the normal condition, numerous false alarms occur. However, the natural change of the normal condition cannot be known in advance. Although the description of the normal condition has a significant influence on the monitoring performance, it has received much less attention. To capture the natural change of the normal condition and detect the damage simultaneously, an adaptive statistical process monitoring using online learning algorithm is proposed for output-only structural health monitoring. The novelty aspect of the proposed method is the adaptive learning capability by moving the window of the recent samples (from normal condition) to update the baseline model. In this way, the baseline model can reflect the natural change of the normal condition in environmental variability. To handle both change rate of the normal condition and non-linear dependency of the damage-sensitive features, a variable moving window strategy is also proposed. The variable moving window strategy is the block-wise linearization method using k-means clustering based on Linde–Buzo–Gray algorithm and Bayesian information criterion. The proposed method and two existing methods (static linear principal component analysis and incremental linear principal component analysis) were applied to a full-scale bridge structure, which was artificially damaged at the end of the long-term monitoring. Among the three methods, the proposed method is the only successful method to deal with the non-linear dependency among features and detect the structural damage timely.

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Section: Proposed Methods Using Online Learning Algorithm With Variablmentioning

confidence: 99%

Section: Proposed Methods Using Online Learning Algorithm With Variablmentioning

confidence: 99%

Section: Pca-based Spmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

Jin

Jung

2017

Structural Health Monitoring

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On the use of mode shape curvatures for damage localization under varying environmental conditions

Shokrani

Dertimanis

Chatzi

et al. 2018

Struct Control Health Monit

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SummaryA novel damage localization method is introduced in this study, which exploits mode shape curvatures as damage features, while accounting for operational variability. The developed framework operates in an output-only regime,that is, it does not assume availability of records from the influencing environmental/operational quantities but rather from response quantities alone. The introduced tool comprises 3 stages pertaining to training, validation, and diagnostics. During the training stage, a representation of the healthy, or baseline, structural state is acquired over varying operational conditions. A data matrix is formulated, whose individual columns correspond to mode shape curvatures at distinct operational conditions, and principal component analysis (PCA) is applied for extraction of the imprints of separate operational sources on these curvatures. To this end, a residual matrix between the original and the PCA mapped data is formed serving for statistical characterization of each mode.Subsequently, during the validation and diagnostics stages, the mode shape curvature matrices for the currently inspected structural state are assembled and the same PCA mapping is enforced. A typical hypothesis test and a corresponding damage index are then adopted in order to firstly detect damage, and to secondly localize damage, should this exist. The implementation of the proposed method in 2 numerical case studies confirms its effectiveness and the encouraging results suggest further investigation on operating structural systems.

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Eliminating environmental and operational effects on structural modal frequency: A comprehensive review

Wang

Yang

et al. 2022

Structural Contr & Hlth

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Modal frequencies are widely used for vibration-based structural health monitoring (SHM) and for capturing the dynamics of a monitored structure to reveal possible failures. However, changing environmental and operational conditions (i.e., temperature, humidity, wind load, and traffic load) may submerge the modal variability induced by structural damage, thereby falsely identifying damage of interest. This paper presents a comprehensive summary review of SHM for the prediction of modal frequency and the elimination of environment-induced masking effects based on the data normalization method. The influence mechanisms of external variations on modal frequencies extensively reported in the literature are first described. Next, the research progress in predicting and eliminating the operational modal variability is reviewed emphatically; this progress can be primarily divided into an inputoutput method that focuses on the establishment of the relationship model between structural frequency and environmental conditions and an outputonly method that separates the embedded environmental variable-induced changes depending on whether the environmental measurements are measured. Finally, the conclusions and future studies are summarized and discussed. As an overview, the major contribution of this paper is to provide objective technical references for engineers and owners and to further evaluate structural safety conditions more effectively and in a timely manner.

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Adaptive reference updating for vibration-based structural health monitoring under varying environmental conditions

Cited by 26 publications

References 18 publications

Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

Vibration-based damage detection using online learning algorithm for output-only structural health monitoring

On the use of mode shape curvatures for damage localization under varying environmental conditions

Eliminating environmental and operational effects on structural modal frequency: A comprehensive review

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