scholarly journals A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring

2021 ◽  
Vol 11 (21) ◽  
pp. 10072
Author(s):  
Die Liu ◽  
Yihao Bao ◽  
Yingying He ◽  
Likai Zhang
Keyword(s):  
Missing Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Measured Data ◽  
Machine Learning Algorithms ◽  
Measure Data ◽  
Measured Signal ◽  
Negative Effects ◽  
Missing Data Imputation ◽  
Structural Health

Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured data remains a significant challenge. In this study, empirical mode decomposition (EMD) combined with a bidirectional gated recurrent unit (BiGRU) is proposed for the recovery of the measured data. The proposed EMD-BiGRU converts the missing data task as predicted task of time sequence. The core of the method is to predict missing data using the raw data and decomposed subsequence as the decomposed subsequence can improve the predicted accuracy. In addition, the BiGRU in the hybrid model can extract the pre-post correlations of subsequence compared with traditional artificial neural networks. Raw acceleration data collected from a three-story structure are used to evaluate the performance of the EMD-BiGRU for missing data imputation. The recovery results of measure data show that the EMD-BiGRU exhibits excellent performance from two perspectives. First, the decomposed subsequence can improve the accuracy of the BiGRU predicted model. Second, the BiGRU outperforms other machine learning algorithms because it captures more microscopic changes of measured data. The experimental analysis suggests that the change patterns of raw measured signal data are complex, and therefore it is significant to extract the features before modeling.

scholarly journals Review of Vibration-Based Structural Health Monitoring Using Deep Learning

2020 ◽  
Vol 10 (5) ◽  
pp. 1680 ◽  
Author(s):  
Gyungmin Toh ◽  
Junhong Park
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Large Deviation ◽  
Machine Learning Algorithms ◽  
Structural Vibration ◽  
Current Status ◽  
Measured Signal ◽  
Learning Technology ◽  
Structural Health

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.

scholarly journals Structural Health Monitoring using Varied Machine Learning Algorithms

2018 ◽  
Vol 7 (3.12) ◽  
pp. 793 ◽  
Author(s):  
B Shanthi ◽  
Mahalakshmi N ◽  
Shobana M
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Classification Algorithms ◽  
Structural Health

Structural Health Monitoring is essential in today’s world where large amount of money and labour are involved in building a structure. There arises a need to periodically check whether the built structure is strong and flawless, also how long it will be strong and if not how much it is damaged. These information are needed so that the precautions can be made accordingly. Otherwise, it may result in disastrous accidents which may take away even human lives. There are various methods to evaluate a structure. In this paper, we apply various classification algorithms like J48, Naive Bayes and many other classifiers available, to the dataset to check on the accuracy of the prediction determined by all of these classification algorithms and ar-rive at the conclusion of the best possible classifier to say whether a structure is damaged or not.  

Restoration of missing structural health monitoring data using spatiotemporal graph attention networks

2021 ◽  
pp. 147592172110568
Author(s):  
Jin Niu ◽  
Shunlong Li ◽  
Zhonglong Li
Keyword(s):  
Missing Data ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Low Cost ◽  
Monitoring Data ◽  
Structural Health ◽  
Long Span ◽  
Temporal Correlations ◽  
Spatial Dependencies ◽  
Cable Forces

For structural health monitoring systems with many low-cost sensors, missing data caused by sensor faults, power supply interruptions and data transmission errors are almost inevitable, significantly affecting structural diagnosis and evaluation. Considering the inherent spatial and temporal correlations in the sensor network, this study proposes a spatiotemporal graph attention network for restoration of missing data. The proposed model was stacked with a graph convolutional layer and several spatiotemporal blocks composed of spatial and temporal layers. The monitoring data of normal sensors were first mapped to all sensors through the graph convolutional layer, and attention mechanisms were used in the spatiotemporal blocks to model the spatial dependencies of sensors and the temporal dependencies of time steps, respectively. The extracted spatiotemporal features were assembled through a fully connected layer to reconstruct the missing signals. In this study, both homogeneous and heterogeneous monitoring items were used to calculate the spatial attention coefficients. The data restoration accuracy with and without the multi-source data fusion was discussed. Application on a long-span cable-stayed bridge to restore missing cable forces demonstrates that spatiotemporal attention modelling can achieve satisfactory restoring accuracy without any prior analysis.

scholarly journals Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring

2021 ◽  
Vol 11 (6) ◽  
pp. 2750 ◽  
Author(s):  
Patryk Kot ◽  
Magomed Muradov ◽  
Michaela Gkantou ◽  
George S. Kamaris ◽  
Khalid Hashim ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Fiber Optics ◽  
Health Monitoring ◽  
Civil Engineering ◽  
Machine Learning Algorithms ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Structural Health ◽  
Testing Techniques ◽  
Non Destructive

Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks

2020 ◽  
pp. 147592172095922
Author(s):  
Xiaoming Lei ◽  
Limin Sun ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Frequency ◽  
Low Frequency ◽  
Measured Data ◽  
Data Loss ◽  
Generative Adversarial Network ◽  
Structural Health ◽  
Adversarial Network ◽  
Frequency Features

In the application of structural health monitoring, the measured data might be temporarily or permanently lost due to sensor fault or transmission failure. The measured data with a high data loss ratio undermine its ability for modal identifications and structural condition evaluations. To reconstruct the lost data in the field of structural health monitoring, this study proposes a deep convolutional generative adversarial network which includes a generator with encoder–decoder structure and an adversarial discriminator. The proposed generative adversarial network model needs to understand the content of the complete signals, as well as produce realistic hypotheses for the lost signals. Given the data stably measured before the occurrence of data loss, the generator is trained to extract the features maintained in the data set and reconstruct lost signals using the responses of the remaining functional sensors alone. The discriminator feeds back the distinguished results to the generator to improve its reconstruction accuracy. When training the model, the reconstruction loss and the adversarial loss are employed to better handle the low-frequency features and high-frequency features of the signals. The effectiveness and efficiency of the proposed method are validated by two case studies. As the number of training epoch increases, the reconstructed signals learn the features from low-frequency to high-frequency, and the amplitude of the reconstructed signals gradually increases. It can be seen that the final reconstruction signals match well with the real signals in the time domain and frequency domain. To further demonstrate the applicability of the reconstructed signals in data analysis, the reconstructed acceleration data are used to accurately identify the modal parameters in the numerical case, and the vehicle-induced responses are precisely decomposed from the reconstructed strain data in the field case. Finally, the reconstruction capacity is also investigated with the different numbers of the faulted strain gauges.

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