Structural Health Monitoring最新文献_第8页

Spall size estimation for wind turbine pitch bearings: observation, signal processing method and experiments 风力涡轮机变桨轴承的剥落尺寸估算：观测、信号处理方法和实验

Structural Health Monitoring Pub Date : 2024-04-14 DOI: 10.1177/14759217241243108

Chao Zhang, Long Zhang

引用次数: 0

Transmission tower bolt-loosening time–frequency analysis and localization method considering time-varying characteristics 考虑时变特性的输电塔螺栓松动时频分析和定位方法

Structural Health Monitoring Pub Date : 2024-04-09 DOI: 10.1177/14759217241242032

Long Zhao, Guanru Wen, Jingyao Wang, Zhicheng Liu, Xinbo Huang

{"title":"Transmission tower bolt-loosening time–frequency analysis and localization method considering time-varying characteristics","authors":"Long Zhao, Guanru Wen, Jingyao Wang, Zhicheng Liu, Xinbo Huang","doi":"10.1177/14759217241242032","DOIUrl":"https://doi.org/10.1177/14759217241242032","url":null,"abstract":"To address the issues of high concealment and difficult positioning of loose bolts in transmission towers, this paper proposes a new method for locating loose bolts in transmission towers. In this method, we divide the vibration response of the transmission tower into low-frequency signals of 2–25 Hz and high-frequency signals of 25–75 Hz. For the low-frequency signals, the single signal component is obtained by adaptive Chirp mode decomposition and uses the general demodulation transformation to deeply denoise the non-modal information. Since frequency characteristics themselves do not contain time information, considering the importance of time information for positioning, we propose a low-frequency time-varying frequency feature that preserves time characteristics based on synchronous wavelet transform and peak search. For the high-frequency signals, we use singular value decomposition to remove signal outliers caused by pulse excitation and eliminate forced vibrations through wavelet packet transform. Without altering its inherent characteristics, this method enables high-frequency time-domain signals to better represent the nonlinear characteristics of transmission towers. Furthermore, based on the powerful capabilities of Timesnet and Transformer in dealing with time series data, we propose a fault diagnosis model, which ultimately achieves the positioning of loose bolts in transmission towers. The bolt node model proves that this approach can better represent the loose bolt characteristics, and the transmission tower model verifies the effectiveness of this approach in locating loose bolts in transmission towers. Finally, bolt-loosening tests were conducted on a 110 kV transmission tower, and the accuracy of the positioning results reached 92.8%, demonstrating the effectiveness and efficiency of this method in practical positioning applications.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"43 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726604","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}

引用次数: 0

Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learning 基于迭代修剪损失最小化和人在环学习的结构健康监测数据异常分类开发

Structural Health Monitoring Pub Date : 2024-04-09 DOI: 10.1177/14759217241242031

Shieh-Kung Huang, Tian-Xun Lin

{"title":"Development of data anomaly classification for structural health monitoring based on iterative trimmed loss minimization and human-in-the-loop learning","authors":"Shieh-Kung Huang, Tian-Xun Lin","doi":"10.1177/14759217241242031","DOIUrl":"https://doi.org/10.1177/14759217241242031","url":null,"abstract":"Huge amounts of data can be generated during long-term monitoring performed by structural health monitoring (SHM) and structural integrity management applications. Monitoring data can be corrupted, and the presence of abnormal data can distort information during signal processing, extract incorrect characteristics during system identification, produce false conclusions during damage detection, and ultimately lead to misjudgment of structural conditions during diagnosis and prognosis. Therefore, developing effective techniques to autonomously detect and classify anomalies becomes necessary and significant. Generally, conventional physics-based strategies can be straightforward, but their performance highly depends on prior knowledge of measurement. Recently, data-driven methods leveraging machine learning (ML) have been used to directly handle the task. This study proposes an ML-based classifier and improves it by incorporating the human-in-the-loop (HITL) learning. The classifier is built on a shallow neural network with high performance to address potential online or real-time applications for long-term monitoring. First, a field monitoring dataset is introduced, and various anomalies are defined to investigate the effectiveness. To further enhance the performance of the proposed classifier, the mislabels in the monitoring dataset are examined, and a correction technique is performed. Additionally, HITL ML is developed to overcome the disadvantages of the conventional correction technique. As a result, the proposed procedure can improve both the classifier and the field dataset, and the proposed classifier can now function as a fundamental component in achieving a continuous and autonomous SHM system.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140726646","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}

引用次数: 0

Damage detection of thin plates by fusing variational mode decomposition and spectral entropy 通过融合变异模式分解和光谱熵检测薄板损伤

Structural Health Monitoring Pub Date : 2024-04-06 DOI: 10.1177/14759217241239989

Guangtao Lu, Zhiwei Zhou, Longyun Wu, Yangtao Wang, Tao Wang, Dandan Yang

{"title":"Damage detection of thin plates by fusing variational mode decomposition and spectral entropy","authors":"Guangtao Lu, Zhiwei Zhou, Longyun Wu, Yangtao Wang, Tao Wang, Dandan Yang","doi":"10.1177/14759217241239989","DOIUrl":"https://doi.org/10.1177/14759217241239989","url":null,"abstract":"This paper presents a new approach for damage detection in thin plates by fusing variational mode decomposition and spectral entropy (VMD-SE). In this method, after the received signal is decomposed into some intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the spectral entropy ratio of the first and last IMFs is calculated for optimizing the VMD’s parameters and improving its decomposition performance. Moreover, the cross-correlation coefficient between the decomposed IMFs and the reference signal is computed to separate the desired IMF, which contains more damage information. Finally, the spectral entropy of the obtained IMF is calculated as an indicator for assessing the damage’s severity. The comparative analysis of the simulated signal clearly shows that only the proposed method can successfully separate the damage-related and reference signals. To verify the VMD-SE method, damage detection of two different types of damage on aluminum and composite fiber-reinforced polymer (CFRP) plates is conducted by using this new approach. The experimental results demonstrate that the parameters of VMD affect greatly its decomposition performance, and the best parameters are selected. The results also indicate that the normalized spectral entropy monotonically increases when the diameter of the through-hole or the length of the scratch increases. In addition, the correlation coefficients of the fitting lines of the plates are larger than 0.998. The experimental results of aluminum specimens demonstrate that the damage’s location has an influence on the normalized spectral entropy. At last, based on the linear relationship, the severity of damage in the fourth specimen is identified. The identification results demonstrate that the relative error of the aluminum and CFRP plates is less than 7.34%, which indicates that this new algorithm by fusing VMD and spectral entropy can detect the damage size in thin plates accurately and efficiently.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"4 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734990","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}

引用次数: 0

Phase shift-based resonance assessment for in-service high-speed railway bridges 基于相移的在役高速铁路桥梁共振评估

Structural Health Monitoring Pub Date : 2024-04-06 DOI: 10.1177/14759217241239988

Seunghoo Jeong, Hyunmin Kim, Sung Il Kim, K. Lee, Junhwa Lee

引用次数: 0

MS-DenseNet-GRU tool wear prediction method based on attention mechanism 基于注意力机制的 MS-DenseNet-GRU 工具磨损预测方法

Structural Health Monitoring Pub Date : 2024-04-06 DOI: 10.1177/14759217241240663

Yaonan Cheng, Jing Xue, M. Lu, Shilong Zhou, Xiaoyu Gai, R. Guan

{"title":"MS-DenseNet-GRU tool wear prediction method based on attention mechanism","authors":"Yaonan Cheng, Jing Xue, M. Lu, Shilong Zhou, Xiaoyu Gai, R. Guan","doi":"10.1177/14759217241240663","DOIUrl":"https://doi.org/10.1177/14759217241240663","url":null,"abstract":"Tool wear was an inevitable physical phenomenon in the cutting procedure. Serious tool wear has a direct effect on the level of processing quality and the effectiveness of production, and it even leads to abnormal cutting processes and a series of safety problems. Effective tool wear prediction can provide a basis for the rational use and replacement of tools to improve tool efficiency and ensure the stable operation of the machining process. Therefore, a tool wear prediction method combining multiple deep learning modules was proposed. To begin, the vibration signal was broken up using the complete ensemble empirical mode decomposition with adaptive noise algorithm. Then, the intrinsic mode functions with a strong correlation with the original signal were screened out according to the Pearson correlation coefficient for signal reconstruction. Additionally, the DenseNet module, the gate recurrent unit (GRU) module and the efficient channel attention module were deeply integrated to build a multi-scale DenseNet-GRU tool wear prediction model with attention mechanisms by learning the relationship of mapping between signal features and tool wear. Finally, the model was trained and tested using milling experimental data. The experiments’ outcomes demonstrated that the suggested method can accurately and reliably estimate the tool wear value. Compared with the DenseNet model, convolutional neural network–long short-term memory model, and DenseNet-GRU model, it further shows that it had superior performance in prediction accuracy and generalization ability. The research results can provide certain technical support for the prediction of tool wear intelligently, which is vital to raising the quality of processing, reducing production costs, and promoting the manufacturing industry’s intelligent development.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"21 97","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734798","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}

引用次数: 0

Generalized method for distributed detection and quantification of cracks in bridges 分布式检测和量化桥梁裂缝的通用方法

Structural Health Monitoring Pub Date : 2024-04-06 DOI: 10.1177/14759217241240129

Chengwei Wang, M. Morgese, T. Taylor, Mahmoud Etemadi, Farhad Ansari

{"title":"Generalized method for distributed detection and quantification of cracks in bridges","authors":"Chengwei Wang, M. Morgese, T. Taylor, Mahmoud Etemadi, Farhad Ansari","doi":"10.1177/14759217241240129","DOIUrl":"https://doi.org/10.1177/14759217241240129","url":null,"abstract":"The development of a generalized machine learning approach based on distributed detection and quantification of cracks by optical fibers is described in this article. A Brillouin scattering optical fiber sensor system was employed to develop, test, and verify the method. The main components of the approach described herein consist of an unsupervised crack identification module based on the iForest algorithm and a crack quantification component by the one-dimensional convolutional neural network method. The main attribute of this model is the versatility for application in various types of structures. The proposed method does not require further application-dependent training or calibration as long as the structural applications employ the same optical fiber type and installation adhesives. The effectiveness of the proposed method was verified by two experiments involving a 15-m steel beam in the laboratory and monitoring a twin set of 332-m-long, five-span continuous box girder concrete bridges. Regarding crack detection capabilities, it was possible to detect 107 out of 112 cracks in the laboratory beam and 20 out of the 21 in the bridges. The resolution of crack opening displacements for the steel beam and concrete bridges were 20.6 and 21.7 µm, respectively. The verification experiments further indicated the generality of the approach in applications to various types of structures and materials.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"45 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140734092","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}

引用次数: 0

Monitoring model group of seepage behavior of earth-rock dam based on the mutual information and support vector machine algorithms 基于互信息和支持向量机算法的土石坝渗流行为监测模型组

Structural Health Monitoring Pub Date : 2024-03-28 DOI: 10.1177/14759217241240130

Zhenxiang Jiang

{"title":"Monitoring model group of seepage behavior of earth-rock dam based on the mutual information and support vector machine algorithms","authors":"Zhenxiang Jiang","doi":"10.1177/14759217241240130","DOIUrl":"https://doi.org/10.1177/14759217241240130","url":null,"abstract":"The establishment of a high-precision piezometric water level monitoring model ensures the safe operation of earth-rock dams. The hysteresis effect of the upstream water level and rainfall should be considered during modeling. In the traditional method, the average factors are used to express this effect, and linear regression modeling is adopted. These factors reduce the accuracy of the model. In this paper, the mutual information (MI) and support vector machine (SVM) algorithms are proposed. MI has a powerful correlation analysis capability, and it is innovatively used to address hysteresis effects. SVM has a strong nonlinear modeling ability, and it is used as a modeling algorithm. During this study, it was found that the lag time of rainfall varied. In view of this characteristic, the concept of an innovative model group, which is an important extension of the traditional single model, is proposed. In the example, the mean square error (MSE) is used as the precision index. Compared with the traditional single model established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 60.98%–68.75%. Compared with the model group established by linear regression, the MSE of the MI–SVM model group can be reduced by approximately 41.28%–45.45%. The new method effectively improves the accuracy of the model and can precisely monitor the seepage state of the dam. Moreover, it is beneficial for improving the level of dam safety management and can be extended to other fields involving hysteresis effects and nonlinear modeling.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"118 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140370233","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}

引用次数: 0

An integrated deep neural network model combining 1D CNN and LSTM for structural health monitoring utilizing multisensor time-series data 结合一维 CNN 和 LSTM 的集成深度神经网络模型，用于利用多传感器时间序列数据进行结构健康监测

Structural Health Monitoring Pub Date : 2024-03-26 DOI: 10.1177/14759217241239041

Mohammadreza Ahmadzadeh, S. M. Zahrai, M. Bitaraf

{"title":"An integrated deep neural network model combining 1D CNN and LSTM for structural health monitoring utilizing multisensor time-series data","authors":"Mohammadreza Ahmadzadeh, S. M. Zahrai, M. Bitaraf","doi":"10.1177/14759217241239041","DOIUrl":"https://doi.org/10.1177/14759217241239041","url":null,"abstract":"Introducing deep learning algorithms into the field of structural health monitoring (SHM) has contributed to the automatic extraction of damage-sensitive features, but the type and architecture of these algorithms are still in dispute. This paper proposes a hybrid deep learning framework entitled time-distributed one-dimensional convolutional neural network (1D CNN) long short-term memory (LSTM) model, which utilizes raw multisensor time histories to detect structural damages. Using a sliding window that moves along the temporal dimension, the multisensor data are first segmented into subsequences. The 1D CNN layers are simultaneously applied to each subsequence to extract damage-sensitive features from row data samples. These features are then fed into the LSTM layers to extract temporal features between subsequences. As the final step, these extracted features are classified using fully connected layers. In order to assess the performance of this model, a numerical model of a high-rise frame with nonlinear members is used. This hybrid model is assumed to identify the location of damages to this frame. In order to assess the proposed model with a real-world structure, a well-known benchmark building is employed to identify damage patterns by this deep hybrid neural network. A set of metrics related to the performance of the model is measured and evaluated. It is found that the model has an average accuracy of above 96.6% in localizing damage in the numerical structure and above 99.6% in detecting each damage pattern in the experimental building. The results indicate that the proposed model can be applied effectively to the SHM of different structural systems with different damage patterns.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"104 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380632","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}

引用次数: 0

A feature vector with insensitivity to the position of the outer race defect and its application in rolling bearing fault diagnosis 对外圈缺陷位置不敏感的特征向量及其在滚动轴承故障诊断中的应用

Structural Health Monitoring Pub Date : 2024-03-25 DOI: 10.1177/14759217241236884

Jianqun Zhang, Qing Zhang, Wenzong Feng, X. Qin, Yuantao Sun

{"title":"A feature vector with insensitivity to the position of the outer race defect and its application in rolling bearing fault diagnosis","authors":"Jianqun Zhang, Qing Zhang, Wenzong Feng, X. Qin, Yuantao Sun","doi":"10.1177/14759217241236884","DOIUrl":"https://doi.org/10.1177/14759217241236884","url":null,"abstract":"The fault diagnosis of rolling bearings is very important in industrial applications, which can avoid accidents and reduce operation and maintenance costs. Although the position of the bearing outer race defect has a significant impact on rolling bearing vibration response, most existing intelligent bearing fault diagnosis methods do not take this into account. In this paper, we establish a dynamic model of rolling bearing to clarify the influence of the outer race defect position on the dynamic response, and propose a feature vector that is insensitive to the outer race defect positions. First, the vibration characteristics of the outer race faults with different defect positions are analyzed, and the impact is evaluated using six indicators. Second, three indicators of insensitivity to the bearing outer race defect positions are constructed as the feature vector for bearing fault diagnosis. Finally, a bearing fault diagnosis method considering the positions of outer race defect is proposed based on the constructed feature vector and K nearest neighbor classifier. The diagnosis results of three datasets formed by experimental signals show that the constructed feature vector can separate different bearing states. Compared with the existing two diagnosis methods, the proposed diagnosis method obtains higher recognition accuracy, in the case of different outer race defect positions of the training set and the testing set. The above research results are expected to provide a reference for rolling bearing fault diagnosis, especially when considering the influence of the outer race defect positions.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"5 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140381598","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}

引用次数: 0