Structural Health Monitoring
1475-9217
1741-3168
Anh Quốc
Cơ quản chủ quản: SAGE Publications Ltd
Lĩnh vực:
Mechanical EngineeringBiophysics
Phân tích ảnh hưởng
Thông tin về tạp chí
Structural Health Monitoring publishes peer-reviewed papers that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring. The journal has a broad topical coverage and it serves as a primary reference for the structural health monitoring of aeronautical, mechanical, civil, electrical, and other systems. The multidisciplinary nature of the journal is intended to foster the intersection of different technologies to address the varied needs and applications for structural health monitoring. Papers are sought that explore the following issues and areas related to structural health monitoring: self-diagnostics, prognostics, condition-based maintenance and performance vibration and wave propagation methods for damage assessment advanced signal processing and interpretation techniques for monitoring and diagnostics sensor design, self-powered and low power sensors data mining, data management the use of smart materials and new sensor materials monitoring of composite, metallic, new, and aging structures and infrastructure monitoring of structural repairs sensor network design, data transmission, wired and wireless communication embedding technology, sensor/structure integration technology development of self-repairable structures monitoring of survivability and readiness assessment structural integrity and remaining life predictions based on sensor management design of multifunctional structures and integration of structural health monitoring and control sensors for high temperature applications, in-situ sensors monitoring of biomechanical, electromechanical, and thermal systems fault diagnosis of avionics, propulsion, power, and electronic systems structural health monitoring system integration and validation, etc.
Các bài báo tiêu biểu
Structural Damage Detection using Independent Component Analysis This paper presents a novel approach to detect structural damage based on combining independent component analysis (ICA) extraction of time domain data and artificial neural networks (ANN). The advantage of using time history measurements is that the original vibration information is used directly. However, the volume of data, measurement noise and the lack of reliable feature extraction tools are the major obstacles. To circumvent them, the independent component analysis technique is applied to represent the measured data with a linear combination of dominant statistical independent components and the mixing matrix [ A]. Such a representation captures the essential structure of the measured vibration data. The vibration features represented by the mixing matrix provide the relationship between the measured vibration response and the independent components and are then employed to build the simplified neural network model for damage detection. Two examples are included to demonstrate the effectiveness of the method. First, a truss structure with simulated displacement data was used, and the results show that healthy and damage states located in the nine elements may be classified. Second, a bookshelf structure together with measured time history data from 24 piezoelectric single axis accelerometers was used to demonstrate the approach on a physical structure. The results show the successful detection of the undamaged and damaged states with very good accuracy and repeatability.
Tập 3 Số 1 - Trang 69-83 - 2004
Structural Health Monitoring in the Railway Industry: A Review Wayside detection monitors critical parameters relating to the condition of in-service railway vehicles. Economic decisions about the maintenance of vehicles can be made, and servicing can occur when a particular vehicle is likely to cause even small amounts of damage to the track, to itself, or when the cost of damage is significant, such as in catastrophic failure. Vehicles with poorly performing axle bearings, out-of-round (skidded or spalled) wheels, vehicles which exhibit transient lateral motion (‘hunting’), and vehicles with poorly performing brakes are all likely to fall into the category of requiring maintenance, in order to save the track and the vehicle owner's money. In the present paper, the parameters that define vehicle condition and their measurable effects are stated. There are frequently a number of wayside detection methods of inspecting a vehicle for the same vehicle condition and each of these is described in detail. This investigation reveals the need for further research to enable rollingstock owners to make better decisions about the cost of operating their vehicles, based on the output from wayside detectors and the observed trends in wheel impact.
Tập 4 Số 1 - Trang 81-93 - 2005
Distribution adaptation deep transfer learning method for cross-structure health monitoring using guided waves Deep learning algorithm can effectively obtain damage information using labeled samples, and has become a promising feature extraction tool for ultrasonic guided wave detection. But it is difficult to apply the monitoring expertise of structure A to structure B in most cases due to the differences in the dispersion and receiving modes of different waveguides. For multi-structure monitoring at the system level, how to transfer a trained structural health monitoring model to another different structure remains a major challenge. In this article, a cross-structure ultrasonic guided wave structural health monitoring method based on distribution adaptation deep transfer learning is proposed to solve the feature generalization problem in different monitoring structures. First, the joint distribution adaptation method is employed to adapt both the marginal distribution and conditional distribution of the guided wave signals from different structures. Second, convolutional long short-term memory network is constructed to learn the mapping relationship from adapted training samples in source domain. Batch normalization layer is implemented to balance the input tensors of each sample to the same distribution. Finally, the multi-sensor damage indexes are utilized to visually present the damage by probability imaging. The experimental results show that proposed method can utilize the single-sensor monitoring data in one structure to implement the multi-sensor damage monitoring in another structure and achieve the damage imaging visualization. The imaging performance is significantly superior to the existing principal component analysis, transfer component analysis, and other state-of-art comparison methods.
Tập 21 Số 3 - Trang 853-871 - 2022
Vibration Based Diagnosis of a Centrifugal Pump The centrifugal pump has a long history of frequent failure of anti-friction bearing since its commissioning in 1985. Vibration based conventional condition monitoring has regularly been used to identify the progressive nature of the bearing failure, but has failed to identify the root cause. Modal tests have been conducted on the pump assembly to understand the dynamics of the complete assembly. A typical case of the resonance of the bearing pedestals with 2X component (two times the pump RPM) of the response during pump operation mainly due to nonlinear interaction between the pump foundation and the concrete floor has been identified as the main source of the bearing failure. The results, observations, and the diagnosis to identify the root cause are discussed here.
Tập 5 Số 4 - Trang 325-332 - 2006
An effective health indicator for bearing using corrected conditional entropy through diversity-driven multi-parent evolutionary algorithm The bearing vibration signal possesses nonlinear and non-stationary characteristics; hence; it is difficult to diagnosis the faults in the bearing under different working conditions. In this article, a new scheme has been proposed based on complete ensemble empirical mode decomposition with adaptive noise and corrected conditional entropy to recognize the different class of faults in bearing. The mode with minimum corrected conditional entropy is treated as a prominent mode from which sensitive features are extracted. A filter-based feature selection scheme is used for the same and for ranking the features based on variance to reduce the redundancy of data set. This data set is made input to support vector machine. The performance of the support vector machine classifier is improved by optimizing its parameters to obtain maximum classification accuracy. To address this issue, an evolutionary algorithm (diversity-driven multi-parent evolutionary algorithm) is used. With optimized support vector machine parameters, the support vector machine is trained to build a classification model with 10-fold cross-validation. After training, the built model is tested against test data set for fitness evaluation. The support vector machine classifier gives 100% accuracy at regularization and kernel parameter’s value of 1.3343 and of 782.6329, respectively, with 27.93 s of training time for a single iteration.
Tập 20 Số 5 - Trang 2525-2539 - 2021
Real-time distortion monitoring during fused deposition modeling via acoustic emission Fused deposition modeling is a popular technique for three-dimensional prototyping since it is cost-effective, convenient to operate, and environment-friendly. However, the low quality of its printed products jeopardizes its future development. Distortion, also known as warping deformation, which is caused by many factors such as inappropriate process parameters and process drifts, is one of the most common defects in the fused deposition modeling process. Rapid detection of such part distortion during the printing process is beneficial for improving the production efficiency and saving materials. In this article, a real-time part distortion monitoring method based on acoustic emission is presented. Our work is to identify distortion defects and understand the condition of the distortion area through sensing and digital signal processing techniques. In our experiments, both the acoustic emission hits and original signals were acquired during the fused deposition modeling process. Then, the acoustic emission hits were analyzed. Ensemble empirical mode decomposition was utilized to eliminate noise and extract features from the original acoustic emission signal to further analyze the acoustic emission signal in the case of part distortion. Furthermore, the root mean square of the reconstructed signals was calculated, and the prediction results are strongly correlated with the ground truth printing states. This work provides a promising method for the quality diagnosis of printing parts.
Tập 19 Số 2 - Trang 412-423 - 2020
Laser ultrasonic visualization technique using a fiber-optic Bragg grating ultrasonic sensor with an improved adhesion configuration In this research, we attempt to establish a reliable structural health monitoring technique for composite materials by combining phase-shifted fiber-optic Bragg grating sensing with the laser ultrasonic visualization technology. In the first part of this article, a novel cross-adhesion configuration is designed to resolve the directionality problem of the phase-shifted fiber-optic Bragg grating ultrasonic sensing. In the adhesion configuration, Lamb waves are guided by an orthogonally bonded optical fiber from the adhesion point to the phase-shifted fiber-optic Bragg grating sensor. The analysis of the ultrasonic measurement results reveals that the proposed adhesion method enables us to use one sensor to receive Lamb waves in all in-plane directions with similar magnitude because two wave components propagating along with the two orthogonal directions are guided to the phase-shifted fiber-optic Bragg grating sensor and exhibit a linear superposition in the sensor. This simplified configuration gives our method an advantage over the existing approaches, such as the rosette configuration in which three or more phase-shifted fiber-optic Bragg grating sensors are required to relieve the sensing directionality. The phase-shifted fiber-optic Bragg grating ultrasonic sensor with the proposed adhesion configuration is then applied to visualize the propagation of ultrasonic waves in aluminum plates and carbon fiber–reinforced plastic laminates. Those verification experiments also show us that the new adhesion configuration is effective at protecting the phase-shifted fiber-optic Bragg grating ultrasonic measurement from the sensing directionality. Meanwhile, the broad bandwidth of the phase-shifted fiber-optic Bragg grating sensor enables us to visualize the propagation behavior of various Lamb wave modes over a broad frequency range. Finally, we also validate that the ultrasonic visualization technique merged with the phase-shifted fiber-optic Bragg grating ultrasonic sensing can be used to identify the hidden damage in the carbon fiber–reinforced plastic composite.
Tập 20 Số 1 - Trang 303-320 - 2021
The use of electromechanical impedance conductance signatures for detection of weak adhesive bonds of carbon fibre–reinforced polymer The joints between structural elements should ensure safe usage of the structure. One of the joining method is based on adhesive bonding. However, adhesive bonding has not replaced riveting yet. Rivets are still present even in newest composite aircraft AIRBUS 350. The reliability of the adhesive bonding limits the use of adhesive bonding for primary aircraft structures and there is a search for new non-destructive testing tools allowing to (1) assessment of the surfaces before bonding and (2) assessment of the adhesive bond. The performance of adhesive bonds depends on the physico-chemical properties of the bonded surfaces. The contamination leading to weak bonds may have various origin and be caused by contamination (moisture, release agent, hydraulic fluid and fuel) or poor curing of adhesive. In this work, the research is focused on the development of the method for assessment of the adhesive bonds. Bonded carbon fibre–reinforced polymer samples were considered. Electromechanical impedance technique was proposed. The technique is based on electrical impedance measurements of a piezoelectric transducer attached to the investigated structure. The piezoelectric effect causes the electrical response of a piezoelectric transducer to be related to mechanical response of the structure. The indexes for comparison of the conductance spectra were proposed. Three different cases of possible weak bonds were selected for the investigation. The same cases were investigated by destructive methods by other authors. Such approach allows for direct comparison of the obtained results. It was shown that the proposed method allows for clear separation of weak bond cases from the cases for other samples and free sensors. In terms of weak bond assessment, the frequency change with weak bond level (contamination and level of poor curing) was observed. The obtained results are promising and encourage to future research.
Tập 14 Số 4 - Trang 332-344 - 2015
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.
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.
Tập 13 Số 1 - Trang 82-93 - 2014