Bayesian dynamic regression for reconstructing missing data in structural health monitoring

Zhang, Y. M.; Wang, Hao; Bai, Yu; Mao, Jianxiao; Xu, Yongde

doi:10.1177/14759217211053779

Cited by 59 publications

(23 citation statements)

References 48 publications

(73 reference statements)

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“…Therefore, the established two-stage CAE network architecture in the proposed method still deserves to be further improved by more unknown types and degrees of fastener looseness events. When facing datasets with more noise and establishing more complex deep learning networks, data preprocessing and hyperparameter optimization based on the Bayesian method [ 52 , 53 , 54 ] may be a viable alternative. Moreover, to prevent the risk of sudden intrusion into the subway line due to the excitation of vehicle moving load when the fasteners were in a semi-loose state, this paper only discussed the identification of the complete looseness state of the fasteners.…”

Section: Discussionmentioning

confidence: 99%

Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network

Liang

Jiang

et al. 2022

Sensors

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Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness.

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Section: Discussionmentioning

confidence: 99%

Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network

Liang

Jiang

et al. 2022

Sensors

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“…However, these statistical methods are limited to detecting a single pattern or several specific patterns and cannot handle multiple anomalies in a real SHM system due to the complexity and uncertainty of the anomalies. 11 Machine and deep learning techniques, which have a powerful ability and high efficiency in processing big data, have been widely used in sensor-based [12][13][14] and vision-based SHM. 15,16 Among them, the techniques applied to detect sensor fault patterns for SHM 17 can be divided into two categories, namely, supervised-classification-based methods [18][19][20] and one-class classification (OCC) methods.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection of sensor faults and extreme events based on support vector data description

Zhang

Wang

Ding

et al. 2022

Structural Contr & Hlth

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Structural health monitoring (SHM) systems generate a massive amount of sensing data. On one hand, sensor faults may cause the measurement data to have low fidelity. On the other hand, extreme events, such as typhoons or earthquakes, may cause the monitoring data look "abnormal." These abnormal data, however, are closely related to the structural safety condition and require special attention. This study proposes an automatic and efficient anomaly detection methodology based on support vector data description (SVDD) to simultaneously detect anomalies caused by sensor faults and extreme events. The SVDD trained by a single pattern can divide the feature space into oneversus-the rest. Several decision boundaries are defined to enclose normal data and common sensor fault patterns, forming an equivalent multi-class classifier to classify common sensor fault types and detect unknown patterns. Next, multiple sensor faults and extreme events are separated from the unknown patterns. Multi-label data are detected based on the local features, while extreme events are recognized by the correlation of different sensors. The proposed method is finally applied to datasets collected from two SHM systems. Resultsshow that the sensor anomalies in the systems are detected with high efficiency and accuracy, and extreme events are separated as a special pattern from the normal, common abnormal, and unknown patterns.

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“…The RSM that owns the appealing advantages of computational simplicity and efficiency is predominant in model updating. Kriging predictor is attractive because of its modeling flexibility and great expressive ability, but it is nontrivial to choose an appropriate kernel function that directly affects the modeling performance 7,33 . Recently, neural networks have been considered promising techniques for model updating since they could fully exploit the relationship between input and output variables.…”

Section: Introductionmentioning

confidence: 99%

“…Kriging predictor is attractive because of its modeling flexibility and great expressive ability, but it is nontrivial to choose an appropriate kernel function that directly affects the modeling performance. 7,33 Recently, neural networks have been considered promising techniques for model updating since they could fully exploit the relationship between input and output variables. For instance, Park et al 34 updated the FE model considering boundary conditions based on neural networks.…”

mentioning

confidence: 99%

A Bayesian neural network approach for probabilistic model updating using incomplete modal data

Zhang

Wang

Mao

2022

Structural Contr & Hlth

Self Cite

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Summary Finite element (FE) model updating is essential to improve the reliability of physical model‐based approaches in structural engineering applications. The surrogate model is considered an alternative to time‐consuming iterative FE analyses in performing the updating procedure. This paper presents a Bayesian neural network (BNN) as the surrogate model for probabilistic FE model updating using the measured modal data. The BNN involves high computational efficiency by introducing the approximate Gaussian inference of the posterior distribution. In practice, the modal data are usually incomplete because of the measurement noise and limited sensors. The developed BNN exploits the nonlinear relationship between the selected parameters and incomplete modal data. As opposed to the traditional surrogate‐based approach, the proposed framework uses the modal data as inputs and structural parameters to be updated as outputs. It enables uncertainty quantification of the estimated structural parameters efficiently. In particular, an adaptive sampling strategy is established to shrink the searching space of optimal updating parameters based on the truncated Gaussian distribution. Numerical examples are conducted to demonstrate the effectiveness of the presented approach. Then it is applied to the laboratory and experimental structures using the measured data. Results indicate that the proposed framework is accurate and efficient for parameter uncertainty quantification in structural model updating.

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Bayesian dynamic regression for reconstructing missing data in structural health monitoring

Cited by 59 publications

References 48 publications

Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network

Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network

Anomaly detection of sensor faults and extreme events based on support vector data description

A Bayesian neural network approach for probabilistic model updating using incomplete modal data

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