Structural Health Monitoring
Công bố khoa học tiêu biểu
Sắp xếp:
Transfer learning-based data anomaly detection for structural health monitoring The structural health monitoring (SHM) data of civil infrastructure are inevitably contaminated due to sensor faults, environmental noise interference, and data transmission failures. Anomalous data severely disturb the subsequent structural modal identification, damage identification, and condition assessment. Therefore, it is critical to detect and clean SHM data before data analysis. This paper proposes a novel approach for data anomaly detection based on transfer learning, that makes full use of the similarity of the anomalous patterns across different bridges and shares the knowledge incorporated in a deep neural network to achieve high-accuracy data anomaly identification for bridge groups. In the proposed approach, first, a multivariate database for a source bridge is built by plotting and labeling the raw sequential data. Then, a convolutional neural network (CNN) for data anomaly classification is designed and trained with the database in different conditions. The original CNN with the highest accuracy is transferred to a new bridge with enhancement training using a small part of the target bridge data. To validate the performance of the proposed method, the multivariate SHM data for two real long-span bridges are employed, including the acceleration, strain, displacement, humidity, and temperature data. The results demonstrate that transfer learning leads to a better classification capacity for the case of scarce labeled training data compared with the original network.
Structural Health Monitoring - Tập 22 Số 5 - Trang 3077-3091 - 2023
Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification Structural health monitoring relies on the repeated observation of damage-sensitive features such as strains or natural frequencies. A major problem is that regular changes in temperature, relative humidity, operational loading, and so on also influence those features. This influence is in general nonlinear and it affects different features in a different way. In this article, an improved technique based on kernel principal component analysis is developed for eliminating environmental and operational influences. It enables the estimation of a general nonlinear system model in a computationally very efficient way. The technique is output-only, which implies that only the damage-sensitive features need to be measured, not the environmental parameters. The nonlinear output-only model is identified by fitting it to the damage-sensitive features during a phase in which the structure is undamaged. Afterwards, the structure is monitored by comparing the model predictions with the observed features. The technique is validated with natural frequency data from a three-span prestressed concrete bridge, which was progressively damaged at the end of a one-year monitoring period. It is demonstrated that capturing the regular variations of the features requires a nonlinear model. Monitoring the misfit between the predictions made with this model and the observed data allows a very clear discrimination between validation data in undamaged and damaged conditions.
Structural Health Monitoring - Tập 13 Số 1 - Trang 82-93 - 2014
Active vibration-based structural health monitoring system for wind turbine blade: Demonstration on an operating Vestas V27 wind turbine
Structural Health Monitoring - Tập 16 Số 5 - Trang 536-550 - 2017
Structural health monitoring of offshore wind turbines using automated operational modal analysis This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.
Structural Health Monitoring - Tập 13 Số 6 - Trang 644-659 - 2014
Wavelet domain principal feature analysis for spindle health diagnosis This article introduces a hybrid signal processing technique for spindle health monitoring and diagnosis, through the integration of wavelet packet transform and principal feature analysis. Vibration signals measured from a spindle test system with different defect conditions are first decomposed into multiple sub-frequency bands by means of the wavelet packet transform. Statistical parameters such as energy and Kurtosis of these sub-frequency bands are then calculated. Subsequently, Principal Feature Analysis, which is an extension of the Principal Component Analysis, is performed on the statistical parameters to aid in the selection of the most representative features, which can be distinctively separated from each other, as inputs to a diagnostic classifier. Experimental analysis of sensor data measured from the spindle test system has verified the effectiveness of the developed technique.
Structural Health Monitoring - Tập 10 Số 6 - Trang 631-642 - 2011
Structural Health Monitoring in mainland China: Review and Future Trends Structural health monitoring (SHM) technology has been successfully applied to understand the loads, environment actions, and behaviors of a structure subjected to various actions through solving a reverse problem. The sensing technology is a critical part of SHM. In this article, the development of advanced sensing technology and sensors in mainland China in the past decade, such as optic fiber sensing technology, wave propagation-based piezoelectric ceramic (PZT) sensing technology, smart cement-based sensing technology, and corrosion detection technology, have been critically reviewed. In addition, the article also summarizes the application of SHM technologies in earthquake engineering, wind engineering and life-cycle performance evaluation and corresponding progress achieved in mainland China. The challenges and future trends in the development of sensing technology and SHM are put forward in this paper.
Structural Health Monitoring - Tập 9 Số 3 - Trang 219-231 - 2010
Vision-based monitoring system for evaluating cable tensile forces on a cable-stayed bridge Because of the characteristics of cable-supported bridges, the cable tensile force is considered a critical item in their maintenance. In particular, because the evaluation of the cable tensile force in a cable-stayed bridge is essential for understanding the general status of the structural system, identifying the initial values of this force in the construction of a bridge and then accurately predicting and comparing its estimated values during traffic use are very important tasks for the maintenance of a cable-stayed bridge. Therefore, in this study, a vision-based monitoring system that utilizes an image processing technique was developed to estimate the tensile force of stay cables during traffic use. A remotely controllable pan-tilt drive was installed in the developed vision-based monitoring system to estimate the forces on multiple cables using a single system. The use of a 20× electric zoom lens made it possible to achieve sufficient resolution to remotely derive the dynamic characteristics of the stay cables.
Structural Health Monitoring - Tập 12 Số 5-6 - Trang 440-456 - 2013
Wavelet Transform for Structural Health Monitoring: A Compendium of Uses and Features The strategic and monetary value of the civil infrastructure worldwide necessitates the development of structural health monitoring (SHM) systems that can accurately monitor structural response due to real-time loading conditions, detect damage in the structure, and report the location and nature of this damage. In the last decade, extensive research has been carried out for developing vibration-based damage detection algorithms that can relate structural dynamics changes to damage occurrence in a structure. In the mean time, the wavelet transform (WT), a signal processing technique based on a windowing approach of dilated ‘scaled’ and shifted wavelets, is being applied to a broad range of engineering applications. Wavelet transform has proven its ability to overcome many of the limitations of the widely used Fourier transform (FT); hence, it has gained popularity as an efficient means of signal processing in SHM systems. This increasing interest in WT for SHM in diverse applications motivates the authors to write an exposition on the current WT technologies. This article presents a utilitarian view of WT and its technologies. By reviewing the state-of-the-art in WT for SHM, the article discusses specific needs of SHM addressed by WT, classifies WT for damage detection into various fields, and describes features unique to WT that lends itself to SHM. The ultimate intent of this article is to provide the readers with a background on the various aspects of WT that might appeal to their need and sector of interest in SHM. Additionally, the comprehensive literature review that comprises this study will provide the interested reader a focused search to investigate using wavelets in SHM.
Structural Health Monitoring - Tập 5 Số 3 - Trang 267-295 - 2006
Structural Damage Detection Using Modal Curvature and Fuzzy Logic A fuzzy logic system (FLS) with a new sliding window defuzzifier is proposed for structural damage detection using modal curvatures. Changes in the modal curvatures due to damage are fuzzified using Gaussian fuzzy sets and mapped to damage location and size using the FLS. The first four modal vectors obtained from finite element simulations of a cantilever beam are used for identifying the location and size of damage. Parametric studies show that modal curvatures can be used to accurately locate the damage; however, quantifying the size of damage is difficult. Tests with noisy simulated data show that the method detects damage very accurately at different noise levels and when some modal data are missing.
Structural Health Monitoring - Tập 8 Số 4 - Trang 267-282 - 2009
Vibration-based Damage Identification Methods: A Review and Comparative Study A comprehensive review on modal parameter-based damage identification methods for beam- or plate-type structures is presented, and the damage identification algorithms in terms of signal processing are particularly emphasized. Based on the vibration features, the damage identification methods are classified into four major categories: natural frequency-based methods, mode shape-based methods, curvature mode shape-based methods, and methods using both mode shapes and frequencies, and their merits and drawbacks are discussed. It is observed that most mode shape-based and curvature mode shape-based methods only focus on damage localization. In order to precisely locate the damage, the mode shape-based methods have to rely on optimization algorithms or signal processing techniques; while the curvature mode shape-based methods are in general a very effective type of damage localization algorithms. As an implementation, a comparative study of five extensively-used damage detection algorithms for beam-type structures is conducted to evaluate and demonstrate the validity and effectiveness of the signal processing algorithms. This brief review aims to help the readers in identifying starting points for research in vibration-based damage identification and structural health monitoring and guides researchers and practitioners in better implementing available damage identification algorithms and signal processing methods for beam- or plate-type structures.
Structural Health Monitoring - Tập 10 Số 1 - Trang 83-111 - 2011
Tổng số: 11
- 1
- 2