Structural Health Monitoring最新文献

{"title":"Deep learning neural networks with input processing for vibration-based bearing fault diagnosis under imbalanced data conditions","authors":"J. Prawin","doi":"10.1177/14759217241246508","DOIUrl":"https://doi.org/10.1177/14759217241246508","url":null,"abstract":"Deep learning (DL) networks, such as convolutional neural networks (CNNs) and long short-term memory (LSTM), have gained popularity for bearing fault diagnosis utilizing raw vibration signals. However, their accuracy and stability are compromised when facing imbalanced real-world datasets. This research investigates the impact of imbalanced datasets and explores the potential of signal processing techniques on network inputs compared to the direct use of raw vibration signals. The DL techniques studied include LSTM, one-dimensional CNN, and two-dimensional (2D) CNN, and a novel hybrid 2DCNNLSTM algorithm, incorporating signal processing methods such as Fourier transform and continuous wavelet transform while maintaining nearly equal parameters and the same base architecture. The proposed hybrid 2DCNNLSTM algorithm combines the strengths of LSTM and CNN, allowing for improved bearing diagnosis by capturing both spatial and temporal information in vibration signals. The proposed 2DCNNLSTM algorithm also considers multi-channel input augmenting raw vibration signal, mean, and variance channels to extract meaningful features and enhance classification efficiency. The publicly available Case Western Reserve University benchmark-bearing test rig dataset with ten fault classes, the Paderborn University dataset with three fault classes, and NASA Centre for Intelligent Maintenance Systems bearing datasets with five fault classes are utilized to test the proposed deep learning networks’ accuracy, effectiveness, robustness, and stability. The studies reveal that the hybrid 2DCNNLSTM-based networks outperform both CNN and LSTM networks, even without input processing. Further, utilizing multi-channel input by augmenting the 2D raw signal with mean and variance value channels proves to be more efficient in handling imbalanced and complex datasets while employing a 2DCNNLSTM-based network.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"12 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141021995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative detection of typical bridge surface damages based on global attention mechanism and YOLOv7 network","authors":"You-Hao Ni, Hao Wang, J. Mao, Zhuofan Xi, Zhen-Yi Chen","doi":"10.1177/14759217241246953","DOIUrl":"https://doi.org/10.1177/14759217241246953","url":null,"abstract":"Surface damages of reinforced concrete and steel bridges, for example, crack and corrosion, are usually regarded as indicators of internal structural defects, hence can be used to assess the structural health condition. Quantitative segmentation of these surface damages via computer vision is important yet challenging due to the limited accuracy of traditional semantic segmentation methods. To overcome this challenge, this study proposes a modified semantic segmentation method that can distinguish multiple surface damages, based on you only look once version 7 (YOLOv7) and global attention mechanism (GAM), namely, YOLOv7-SEG-GAM. Initially, the extended efficient layer aggregation network in the backbone network of YOLOv7 was substituted with GAM, followed by the integration of a segmentation head utilizing the three-scale feature map, thus establishing a segmentation network. Subsequently, graphical examples depicting five types of reinforced concrete and steel bridge surface damages, that is, concrete cracks, steel corrosion, exposed rebar, spalling, and efflorescence, are gathered and meticulously labeled to create a semantic segmentation dataset tailored for training the network. Afterwards, a comparative study is undertaken to analyze the effectiveness of GAM, squeeze-and-excitation networks, and convolutional block attention module in enhancing the network’s performance. Ultimately, a calibration device was developed utilizing a laser rangefinder and a smartphone to enable quantitative assessment of bridge damages in real size. Based on the identical dataset, the evaluated accuracy of YOLOv7-SEG-GAM was compared with DeepLabV3+, BiSeNet, and improved semantic segmentation networks. The results indicate that the mean pixel accuracy and mean intersection over union values achieved by YOLOv7-SEG-GAM were 0.881 and 0.782, respectively, surpassing those of DeepLabV3+ and BiSeNet. This study successfully enables pixel-level segmentation of bridge damages and offers valuable insights for quantitative segmentation.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"14 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141047886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian data-driven framework for structural health monitoring of composite structures under limited experimental data","authors":"Leonardo de Paula S. Ferreira, R. Teloli, Samuel da Silva, Eloi Figueiredo, Nuno Maia, C. A. Cimini","doi":"10.1177/14759217241236801","DOIUrl":"https://doi.org/10.1177/14759217241236801","url":null,"abstract":"Ultrasonic-guided waves can be used to monitor the health of thin-walled structures. However, the run of experimental damage tests on materials like carbon fiber-reinforced plastics can be impractical and costly. Instead, numerical models can be used to create hybrid datasets to train machine learning algorithms, integrating data from numerical and experimental tests. This paper presents a Bayesian-driven framework to compensate for limited experimental data regarding Lamb wave propagation in composite plates. Using Bayesian inference, the framework updates a numerical finite element model, considering observed uncertainties by sampling posterior probability density functions for input parameters using Markov–Chain Monte Carlo simulations with the Metropolis-Hastings algorithm. A neural network surrogate model speeds-up these simulations, leading to a model that replicates the uncertain experimental setup. This model then generates data to augment true experimental data. Finally, a one-dimensional convolutional neural network is trained on a three different datasets to analyze Lamb wave signals and assess damage. Comparing training strategies shows the hybrid dataset augmented by samples generated by the updated FE model gives the most accurate damage size predictions.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"43 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140662904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel procedure for cable damage identification of cable-stayed bridge using particle swarm optimization and machine learning","authors":"Van-Thanh Pham, Duc‐Kien Thai, Seung-Eock Kim","doi":"10.1177/14759217241246501","DOIUrl":"https://doi.org/10.1177/14759217241246501","url":null,"abstract":"The cables are crucial components in the ensuring safety of the stayed-cable bridges. The early identification and quantification of cable damage based on the inherent structural health monitoring (SHM) system is a priority to prevent disasters. In this study, a procedure is proposed to identify the cable damage in the cable-stayed bridges using the particle swarm optimization (PSO) and the categorical gradient boosting (CatBoost) algorithm. The PSO-based finite element model updating method is implemented to establish the baseline model while a practical advanced analysis program is used to generate simulation data. As an efficient and up-to-date machine learning (ML) algorithm, CatBoost is utilized to capture the complex nonlinear correlations between the vibration characteristics and the cable damages. A case study of a benchmark bridge where cable damage has been identified is considered to evaluate the efficiency of the proposed procedure. The fivefold cross-validation and grid search methods are used to find the optimal model. The accuracy of the proposed cable damage identification model using CatBoost is also verified through the comparison with three existing ML methods: random forest, decision tree, and extreme gradient boosting. The identification results of both simulation and real cases of cable damage demonstrate that the proposed procedure is a novel and powerful approach for cable damage identification of the cable-stayed bridge using measurement data of the existing SHM system.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"32 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140661118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based three-dimensional crack damage detection method using point clouds without color information","authors":"Yujie Lou, Shiqiao Meng, Ying Zhou","doi":"10.1177/14759217241236929","DOIUrl":"https://doi.org/10.1177/14759217241236929","url":null,"abstract":"Automated high-precision crack detection on building structures under poor lighting conditions poses a significant challenge for traditional image-based methods. Overcoming this challenge is crucial to enhance the practical applicability of structural health monitoring and rapid damage assessment, especially in post-disaster scenarios like earthquakes. To address this challenge, this paper presents a deep learning-based three-dimensional crack detection method that utilizes light detection and ranging (LiDAR) point cloud data. Our method is specifically designed to address crack detection without relying on color information input, resulting in high-precision and robust apparent damage detection. The key contribution of this paper is the NL-3DCrack model, which enables automated three-dimensional crack semantic segmentation. This model comprises a feature embedding module, an incomplete neighbor feature extraction module, a decoder, and morphological filtering. Notably, we introduce an innovative incomplete neighbor mechanism to effectively mitigate the impact of outliers. To validate the effectiveness of our proposed method, we establish two three-dimensional crack detection datasets, namely the Luding dataset and the terrestrial laser scanner dataset, which are based on earthquake disasters. Experimental results demonstrate that our method achieves remarkable performance, with an intersection-over-union of 39.62% and 51.33% on the respective test sets, surpassing existing point cloud-based semantic segmentation models. Ablation experiments further confirm the effectiveness of our approach. In summary, our method showcases exceptional crack detection performance on LiDAR data using only XYZI channels. With its high precision and reliable results, it offers significant utility in real-world applications, contributing to improved structural health monitoring and rapid damage assessment after disasters, particularly in post-earthquake scenarios.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"38 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140667324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Loose bolt localization and torque prediction in a bolted joint using lamb waves and explainable artificial intelligence","authors":"Muping Hu, Nan Yue, R. Groves","doi":"10.1177/14759217241241976","DOIUrl":"https://doi.org/10.1177/14759217241241976","url":null,"abstract":"With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of AI models. In this work, the results of explainable artificial intelligence (XAI) algorithms are used to reduce the input size of a one-dimensional convolutional neural network (1D-CNN), hence simplifying the CNN structure. To select the most accurate XAI algorithm for this purpose, we propose a new evaluation method, feature sensitivity (FS). Utilizing XAI and FS, a reduced dimension 1D-CNN regression model (FS-X1D-CNN) is proposed to locate and predict the torque of loose bolts in a 16-bolt connected aluminum plate under varying temperature conditions. The results were compared with 1D CNN with raw input vector (RI-1D-CNN) and deep autoencoders-1D-CNN (DAE-1D-CNN). It is shown that FS-X1D-CNN achieves the highest prediction accuracy with 5.95 mm in localization and 0.54 Nm in torque prediction, and converges 10 times faster than RI-1D-CNN and 15 times faster than DAE-1D-CNN, while only using a single lamb wave signal path.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-order iterative rearrangement transform for time–frequency characterization of bearing fault impact","authors":"Dezun Zhao, Xiaofan Huang, Lingli Cui","doi":"10.1177/14759217241242997","DOIUrl":"https://doi.org/10.1177/14759217241242997","url":null,"abstract":"Time–frequency analysis (TFA) can effectively characterize features of non-stationary signals. Traditional TFA algorithms construct signal models in the time domain and make the assumption that the instantaneous characteristics of each component are continuous. However, the instantaneous frequency (IF) of the transient signal is discontinuous in the time domain and exhibits a multifaceted relationship with time, such as shock, vibration wave, damped sound wave, etc. Additionally, in most existing TFA methods, low-order group delay (GD) is used to describe transient signals, which leads to unsatisfactory energy concentration and calculation accuracy. To address about issues, a novel TFA technique, termed high-order iterative rearrangement transform (HOIRT), is developed in this research. First, the signal model is defined within the frequency domain, and the frequency ridge of the transient signal is described by a high-order GD (HOGD), which is similar to the IF. Second, a HOGD-based iterative synchrosqueezing operator is defined to reassign time–frequency coefficients into the GD trajectories along the time direction. Finally, the HOGD-based frequency extraction operator is constructed to only retain the target time–frequency information of the transient signal from the rearranged results, such that the noise interference is eliminated and the energy-concentrated TFR is obtained. A simulation signal with nonlinear GDs is employed to illustrate the effectiveness of the HOIRT. Compared with the other seven typical TFA algorithms, the developed technique has the smallest calculation error and Rényi entropy, showing that the HOIRT has the highest accuracy and energy concentration. Analysis result of the bearing fault impact signal shows that the proposed HOIRT can display the time when pulses occur while ensuring high time–frequency resolution, making it suitable for detecting bearing faults.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"34 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140674615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urban dark fiber distributed acoustic sensing for bridge monitoring","authors":"Julie Rodet, Benoit Tauzin, Mohammad Amin Panah, Philippe Guéguen, Destin Nziengui Bâ, Olivier Coutant, Stéphane Brûlé","doi":"10.1177/14759217241231995","DOIUrl":"https://doi.org/10.1177/14759217241231995","url":null,"abstract":"Distributed acoustic sensing (DAS) technology applied to telecommunication optical fiber networks offers new possibilities for structural health monitoring. The dynamic responses of five bridges are extracted along a 24-km long optical fiber crossing the Lyon metropolitan area in France. From their characteristics signals, three physical parameters informing on the health of structures have been determined: vibration frequencies, damping and modal shapes. The fiber measurements are in agreement with velocimetric data serving as reference. The telecom optical fiber records the dynamic response of bridges in several directions and thus allows the reconstruction of 3D deformation modes using their orthogonality properties. Time tracking of frequencies, commonly used to assess structural integrity, shows that the average values of natural frequencies vary cyclically between day and night. The increase in frequencies during the night does not exceed 2% and probably reflects an overall stiffening of the structures due to the drop in temperature. The telecom fiber allows to obtain deformation and damping identity of structures, highlighting soil-structure coupling between the bridge and underlying soil. This study shows that it is possible to assess the spatial and temporal variability of bridge dynamic response from DAS data using existing fiber networks.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 586","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140682553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of damaged member in a truss structure using acoustic emission technique aided by SVM","authors":"Parikshit Roy, Gudipati Bhanu Kiran, Neetika Saha, Pijush Topdar","doi":"10.1177/14759217241242517","DOIUrl":"https://doi.org/10.1177/14759217241242517","url":null,"abstract":"Structures are prone to damage, and detecting them at their very initiation is extremely important for taking corrective measures. Truss is a very important civil engineering structure having a complex geometry: width and thickness being very small compared to the length of a member and the presence of discontinuities in the form of joints. Most of the existing techniques identify damages after they have grown to a substantial degree. For early detection of damages, the acoustic emission (AE) technique may be used effectively as the initiation of damage itself results in the emission of AE wave, analysis of which may lead to detection of the damage. Additionally, signature behavior of the virgin structure is not necessary, unlike many other nondestructive testing methods. Existing studies largely use the time of arrival (TOA) of AE wave at the sensor location(s) in the formulation for damage localization. However, TOA is significantly affected by reflected waves from the edges of truss members and attenuation of signal strength during its passage through joints. In addition, TOA suffers from a lack of objectivity in prescribing the relevant threshold value. Accordingly, damage localization may be affected. In this context, a machine learning approach is very promising if appropriate signal features are used for training. Accordingly, in the present study, an effort is made to develop a support vector machine model for the localization of a truss member, where damage has been initiated. The model is trained and then tested using different sets of experimental data, collected from a laboratory-scale truss. The results of the localization, as predicted by the model, are found to be encouraging when compared with the physical observation.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"42 S198","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated instance segmentation of asphalt pavement patches based on deep learning","authors":"Anzheng He, Allen A. Zhang, Xinyi Xu, Yue Ding, Hang Zhang, Zishuo Dong","doi":"10.1177/14759217241242428","DOIUrl":"https://doi.org/10.1177/14759217241242428","url":null,"abstract":"The location and pixel-level information of the patch are all critical data for the quantitative evaluation of pavement conditions. However, obtaining both parch location and pixel-level information simultaneously is a challenge in intelligent pavement patch surveys. This paper proposes a deep-learning-based patch instance segmentation network (PISNet) that employs you only look once (YOLO)v5 as the baseline and adds a semantic segmentation branch to provide an effective solution for this challenge. The proposed PISNet replaces the original backbone CSPDarknet53 and neck of YOLOv5 with a novel feature extractor named symmetrical pyramid network (SPN). The proposed SPN aims at repeating fusion and transfer of shallow semantic features and deep spatial localization features in the order of “FPN-PAN-FPN” such that the multi-scale semantic expression and localization ability of the feature map could be enhanced. Moreover, a modified feature selection module is also incorporated into the SPN as a skip connection to aggregate more spatial details of the feature map while suppressing redundant features. Experimental results show that compared with Mask region convolutional neural network (R-CNN), You only look at coefficients (YOLACT), YOLACT++, EfficientDet, Fully convolutional one stage object detector (FCOS), You only look once version 5m (YOLOv5m), U-Net, DeepLabv3+, and High resolution network-object contextual representations (HRNet-OCR), the proposed PISNet has the best detection performance. Meanwhile, the proposed PISNet achieves superior accuracy/frames per second trade-offs compared to Mask R-CNN, YOLACT, and YOLACT++. Particularly, the proposed PISNet has certain promising potential in supporting pavement patch detection in real-time scenarios and potentially degraded pavement patch detection. Moreover, the proposed PISNet can yield superior segmentation results compared with Mask R-CNN, YOLACT, YOLACT++, U-Net, HRNet-OCR, and DeepLabv3+ on public CRACK500 datasets. Code has been made available at: https://github.com/716HAZ/PISNet .","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"7 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140697716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}