Structural Health Monitoring最新文献

{"title":"Recursive demodulated synchro spline-kernelled chirplet extracting transform: a useful tool for non-linear behavior estimation of non-stationary signal and application to wind turbine fault detection","authors":"Yubo Ma, Huawei Wu, Rui Yuan, Hongyu Zhong, Hong-Yi Wu","doi":"10.1177/14759217241246094","DOIUrl":"https://doi.org/10.1177/14759217241246094","url":null,"abstract":"Non-linear behavior is widespread in many kinds of signals from nature and engineering fields. Although the high energy-concentration level of various advanced time-frequency (TF) analysis (TFA) techniques currently developed ensure a fine representation of non-linear behavior of time-varying component (TVC) of the signal, it is far from sufficient to solely consider the single aspect of energy-concentration level, because the actual signal composition is always more complicated, especially for some thorny difficulties such as strong noise interference and the early weak TVC, etc., these negative factors bring significant challenges to reveal the non-linear behavior of TVC of practical signals. A new TFA method aimed at this issue, called recursive demodulated synchro spline-kernelled chirplet extracting transform (RDSSCET), is proposed in this paper. The proposed RDSSCET is developed on the frame of synchro spline-kernelled chirplet extracting transform (SSCET) and a newly designed external-internal nested double iteration mechanism, which effectively addresses the limitation of SSCET in handling multicomponent signals while also exhibiting superior high energy concentration and noise robustness. As such, the proposed RDSSCET can yield a more favorable outcome when revealing the non-linear behavior of TVC, particularly for weak TVC with strong noise interference. Comparison analysis results in numerical simulations verified the performance of RDSSCET. Its effectiveness in real applications is fully tested via two real-world sound signals and a practical case of wind turbine fault detection.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"116 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141126458","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":"Validation of a ray-tracing-based guided Lamb wave propagation methodology in aerostructures","authors":"Fernando Sánchez Iglesias, Andrés García Serrano, Andrés Pedraza Rodriguez, Antonio Fernández López","doi":"10.1177/14759217241249056","DOIUrl":"https://doi.org/10.1177/14759217241249056","url":null,"abstract":"Accurate modeling of guided wave propagation is crucial for structural health monitoring (SHM) systems, where a large amount of information and cases are needed to cover all in-service conditions of a structure. Finite-element models have proven to be accurate enough to simulate the problem; however, they typically require substantial computational resources, and each simulation may require a significant amount of time. This article presents a comprehensive study of a ray-tracing-based wave propagation methodology applied to predict the acoustic behavior of lightweight structures. Focused on composite materials, particularly carbon fiber-reinforced plastic (CFRP), the study addresses the growing need for accurate and fast simulation tools in industries where high-strength lightweight materials play a pivotal role, such as aerospace or automotive. The study presents an examination of the ray tracing method’s effectiveness with series of experimental coupon tests, ranging from a simple metallic plate to a representative CFRP wing lower cover of the Universidad Politécnica de Madrid-LIBIS Unmanned Aerial Vehicle. The investigation spans a distribution of possible damage locations ensuring a comprehensive applicability evaluation. Results demonstrate efficacy in predicting the wave propagation characteristics, including transmission, reflection, and absorption within composite structures, and also an accurate representation of its behavior for in-service damages, both via added masses and real impact damages. The validation involves an in-detail comparison with experimental measurements, evaluating the reliability and applicability of the ray tracing approach. This research not only contributes to the advancement of predictive modeling for acoustic behavior in composite structures but also addresses the broader implications for industries relying on accurate simulations for design optimization and performance evaluation. The validated ray tracing method has proven to be a valuable tool to ensure precise predictions of wave propagation in composite materials, and its computation speed makes the methodology ideal to contribute to a training database for a possible physics-informed machine learning SHM system.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"26 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140981036","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":"Two-step vibration-based machine learning model for the fault detection and diagnosis in rotating machine and its blind application","authors":"Natalia F. Espinoza-Sepulveda, Jyoti K. Sinha","doi":"10.1177/14759217241249055","DOIUrl":"https://doi.org/10.1177/14759217241249055","url":null,"abstract":"A robust and reliable condition monitoring and fault diagnosis system is crucial for an efficient operation of industries. Because of the advances in technologies over the past few decades, there is an increased interest in developing intelligent systems to perform tasks that traditionally rely on knowledge, experience and expertise of an individual. It is known that unexpected breakdowns have wide implications in production processes. Thus, it is vital to be able to know the machine condition and detect at the earliest possible stage the defects when they occur. Aiming at an industrial application, in this study, a two-step approach is proposed for the fault detection and diagnosis of rotor-related faults. The implemented algorithm is a pattern recognition supervised artificial neural network, which through information extracted from vibration signals allows one to identify the health status of the machine. In the first step, the model identifies whether the machine is healthy or faulty. This is important information for any industry to operate the machines. Once the machine condition (healthy or faulty) is known and if it is faulty, then only faulty machine parameters are used in the second step to know the specific fault. The model is initially based on existing experimental data, and then, it is further validated with mathematically generated data. The proposed two-step approach model and the trained framework are applied blindly at a different machine speed, where the dynamics of machine is expected to be different. The excellent results obtained suggest this approach as a possibility for industrial application.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"103 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978031","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 rolling bearing fault diagnosis method based on interactive generative feature space oversampling-based autoencoder under imbalanced data","authors":"Fengfei Huang, Kai Zhang, Zhixuan Li, Qing Zheng, Guofu Ding, Minghang Zhao, Yuehong Zhang","doi":"10.1177/14759217241248209","DOIUrl":"https://doi.org/10.1177/14759217241248209","url":null,"abstract":"With the rapid development of railroads and the yearly increase in the scale of operation, the safe operation and maintenance of rail trains have become particularly important. Among them, deep learning-based bearing fault diagnosis methods have attracted more and more attention in rail train operation and maintenance. However, rail trains usually operate normally. Collecting complete fault data for deep learning model training is often difficult. Such scenarios with a large difference between the number of normal data and fault data usually affect the performance of fault diagnosis models. Here, an interactive generative feature space oversampling-based autoencoder (IGFSO-AE) is proposed to realize fault sample generation under imbalanced data. First, the original vibration signal is converted into a semantically stable amplitude–frequency signal by fast Fourier transform and input into the autoencoder; second, the order of the hidden layer space features of the autoencoder is randomly exchanged, and the interactive sample generation learning strategy trains the autoencoder; then, interpolation oversampling is used to interpolate samples in the hidden layer space where the Euclidean distance between samples is large, and is input into the decoder, the generated samples are mixed with the original samples to form a new training set, which is used to train the intelligent fault diagnosis model and output the diagnosis results. Finally, the performance of the proposed method is evaluated using the publicly available bearing dataset and the bogie-bearing fault simulation bench in our lab. The experimental results show that IGFSO-AE can generate diverse samples with incremental information and exhibits robustness and superiority in different imbalanced proportions of data.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140994170","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":"Generalized Shannon entropy sparse wavelet packet transform for fault detection of traction motor bearings in high-speed trains","authors":"Limu Qin, Gang Yang, Wen He","doi":"10.1177/14759217241245320","DOIUrl":"https://doi.org/10.1177/14759217241245320","url":null,"abstract":"An effective structural health monitoring method of traction motor bearings is a powerful guarantee for the safety operation of high-speed trains. However, it is exceptionally difficult to detect bearing fault characteristics from the vibration signals of traction motor bearings operating at high rotational speeds. In this scenario, a generalized Shannon entropy sparse wavelet packet transform (GSWPT) for fault detection of motor bearings is proposed in this paper. Firstly, a generalized Shannon entropy sparse regularization method is proposed to obtain sparse wavelet reconstruction coefficients by extending the definition of the Shannon information entropy, and the non-convex sparse regularization function is minimized by synergistic swarm optimization algorithm. Then, the wavelet node coefficients are weighted according to the second-order cyclostationarity index of the wavelet packet node to further enhance the sparsity of the reconstructed signal. Moreover, the optimal decomposition level of GSWPT is adaptively selected by the maximum sparsity and cyclostationarity criterion. Particularly, in order to verify the bearing fault detection performance of GSWPT in practical engineering, a bearing fault dynamic model of traction motor in high-speed train was established based on Hertz contact theory and the fourth-order Runge-Kutta method to obtain simulated data under strong Gaussian white noise, and a corresponding test platform was constructed to collect experimental data under different operating conditions. Finally, the applications on the simulated and experimental signals of traction motor bearings in high-speed trains demonstrate that GSWPT significantly outperforms the conventional wavelet packet transform, dual-tree complex wavelet packet transform, blind deconvolution, modal decomposition, and Infogram methods to some extent for fault detection.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140995233","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":"Polar coordinate-based guided wave beamforming imaging using scanning LDV","authors":"Hyeonwoo Nam, J. Jeon, G. Park, Chan-Yik Park","doi":"10.1177/14759217241248047","DOIUrl":"https://doi.org/10.1177/14759217241248047","url":null,"abstract":"In this paper, we propose a technique for efficiently detecting damage in plate-like structures using guided waves, polar coordinate-based beamforming imaging, and filtering techniques. The proposed technique uses a laser Doppler vibrometer and a scanning mirror-tilting device for ultrasonic wave imaging, with single-frequency excitation provided by a mounted piezoelectric transducer. Frequency-wavenumber domain filtering and directional filtering are implemented in polar coordinates for efficient detection of damage-reflected waves by removing any incident wave components. The filtered waves are then processed using a delay-and-sum beamforming algorithm within local-area laser scanning through a circular array sensing system. The polar coordinate-based laser scanning system improves the damage detection capability and processing time by removing the need for coordinate transformation in frequency-wavenumber domain filtering and directional filtering. Finally, the proposed technique is validated through experiments on composite and aluminum plates with various types of damage, where it demonstrates improved accuracy and speed when estimating the exact damage location.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140998649","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":"Incipient bearing fault detection using adaptive fast iterative filtering decomposition and modified Laplace of Gaussian filter","authors":"Yu Wei, Yongbo Li, Xianzhi Wang","doi":"10.1177/14759217241246985","DOIUrl":"https://doi.org/10.1177/14759217241246985","url":null,"abstract":"The impact components induced by faulty bearings can be readily concealed by environmental noise and other interferences due to their inherent weakness, especially during the incipient stages of fault development. A novel approach is presented in this study for the detection of incipient bearing faults, which combines an adaptive fast iterative filtering decomposition (FIFD) method with a modified Laplace of Gaussian filter. The first step involves proposing an adaptive FIFD (AFIFD) method employing improved sparrow search algorithm, enabling adaptive selection of the optimal parameter within the FIFD method. The AFIFD technique is able to adaptively decompose a complicated signal into a set of mono-components. Subsequently, a modified Laplace of Gaussian is used to highlight the fault-related cyclic impulse train from a sensitive mono-component decomposed by the AFIFD method. Finally, the envelope analysis performing on enhanced signals is applied to identify fault characteristic frequencies. Results from some case studies demonstrate that the proposed method is capable of extracting incipient fault signatures. The superiority of the proposed method is further validated through some comparative tests with recently developed fault detection methods.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"66 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141017911","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":"Multisource information fusion model for deformation safety monitoring of earth and rock dams based on deep graph feature fusion","authors":"Jichen Tian, Yanling Li, Yonghua Luo, Han Zhang, Xiang Lu","doi":"10.1177/14759217241244549","DOIUrl":"https://doi.org/10.1177/14759217241244549","url":null,"abstract":"Constructing a long-term deformation monitoring model for earth–rock dams that integrates multisource monitoring information is highly important for enhancing the safety state evaluation and monitoring effectiveness of such dams. In this paper, we propose a new health monitoring model named the deformation–seepage–water level multimeasurement point health monitoring (DSW-MPHM) model for earth–rock dams based on deep graph feature fusion. This model fuses coupled seepage, deformation, and water level features from different monitoring sites of the dam body, base, and shoulder. To achieve this goal, we first establish a new module to fuse spatial and temporal features using graph convolutional networks and long short-term memory. Seepage features and water level features are then extracted using graph attention mechanisms. Subsequently, we employ the feature fusion technique, which incorporates principal component analysis and gated fusers, to construct the DSW-MPHM model, which effectively fuses information from multiple sources. This novel approach successfully addresses the issues of information redundancy and the limited reliability of monitoring models. To verify the validity of the model, it is applied to an endoscopic deformation monitoring program of a panel rockfill dam with a height of 185.5 m. The results demonstrate the superior stability and effectiveness of the proposed method compared to those of 10 baseline prediction models. Additionally, the characterization of the seepage and water level features extracted from the model is verified for its reasonableness. Thus, our proposed model is well suited for practical engineering applications.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"26 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141022633","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 attached-spring model for damage quantification and degradation evaluation of short/mid-span bridges","authors":"Ning Wang, Can Wang, Tian-li Huang, Chuan-rui Guo","doi":"10.1177/14759217241246504","DOIUrl":"https://doi.org/10.1177/14759217241246504","url":null,"abstract":"Short/mid-span bridges are the most commonly used infrastructure in highways, railways, and other transportation projects. With service time increasing, local damage and performance degradation of short/mid-span bridges are inevitable. Therefore, the assessment of the bridge condition is essential to ensure its safe operation. However, the existing methods mostly assume the damage region of the bridge as a uniform reduction in stiffness, which is not consistent with the actual bridge. Consequently, those methods can hardly achieve the evaluation of the damage. To address this issue, this paper proposes a novel attached-spring model for damage quantification and degradation evaluation of short/mid-span bridges. By assuming the damage region as a spring, an attached-spring model is proposed to describe the nonuniform local damage of the bridge. Then, the deflection influence line is used to identify the stiffness and position of the attached-spring, which achieves the damage detection and evaluation. This approach provides a general model to describe various bridge damage and evaluate the effect of the damage. Numerical simulation and experimental tests are used to verify the feasibility and accuracy of the proposed method. By comparing the results identified from the moving load test and those measured from the static test, the performance of the method is assessed.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141021203","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 time-saving fault diagnosis using simplified fast GAN and triple-type data transfer learning","authors":"Hongyu Zhong, S. Yu, Hieu Trinh, Rui Yuan, Yong Lv, Yanan Wang","doi":"10.1177/14759217241241985","DOIUrl":"https://doi.org/10.1177/14759217241241985","url":null,"abstract":"Existing intelligent fault diagnosis approaches demand substantial data for training diagnostic models. However, factors such as the inherent characteristics of bearings, operating conditions, and privacy security make collecting comprehensive fault-bearing data very difficult. Although generating synthetic data through generative adversarial networks (GANs) is feasible, the data generation of GANs is a time-consuming process. To address these challenges, a fault diagnosis framework based on GAN and deep transfer learning (DTL) is proposed, termed the simplified fast GAN triple-type data transfer learning (SFGAN-TDTL) method. Initially, an SFGAN is proposed as a replacement for traditional GANs. The time-frequency image data generated by SFGAN serve to augment the training dataset, offering faster and higher-quality data generation compared to traditional GANs. To further reduce time consumption for GAN-based methods, the TDTL method is proposed. Differing from DTL, which utilizes synthetic data to construct a pre-trained model and conducts targeted fine-tuning with real data, TDTL employs open-source data, synthetic data, and real data to fill the weights of the task-insensitive layer, task-sensitive layer, and fully connected layer, respectively. Numerical results demonstrate that SFGAN-TDTL maintains higher diagnostic accuracy while significantly reducing time consumption.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141022240","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}