Structural Health Monitoring最新文献

{"title":"Health monitoring of in-cylinder sensors and fuel injectors using an external accelerometer","authors":"Woongsun Jeon, Anastasis Georgiou, Zongxuan Sun, David A Rothamer, Kenneth Kim, Chol-Bum M. Kweon, R. Rajamani","doi":"10.1177/14759217241232257","DOIUrl":"https://doi.org/10.1177/14759217241232257","url":null,"abstract":"This paper focuses on the development of a methodology to monitor the health of an engine by detecting any failures in the fuel injectors or in-cylinder pressure sensors using an accelerometer that is non-intrusively mounted on the engine block. A multi-cylinder engine with each cylinder having its own pressure sensor and injector is considered. First, a model relating the combustion component of the measured acceleration signal to the combustion component of in-cylinder pressure is proposed. Then, gains of the model are tuned to reduce the cycle-to-cycle estimation error by analyzing cycle-to-cycle variations with respect to the combustion pressure peak and engine vibration peak. Using the developed model, cylinder combustion pressures are estimated from engine vibration signals with small cycle-to-cycle estimation errors. Subsequently, a health monitoring system that can detect faults in pressure sensors, fuel injectors, and the accelerometers is proposed based on residues obtained from the difference between estimated combustion pressure and measured pressure signals. The source of the failed component can be identified uniquely by analyzing the pattern of residues. The proposed combustion pressure estimation algorithms are validated by extensive evaluation with experimental data obtained by operating a four-cylinder compression-ignition direct-injection engine with a range of experimental data. Finally, the developed health monitoring system is evaluated with various failure scenarios involving faults in the in-cylinder pressure sensor, fuel injector, and accelerometer.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"1989 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246661","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":"Early detection of steel tube welded joint failure using SPC-I nonlinear ultrasonic technique","authors":"Sehyuk Park, Imraan Bokhari, H. Alnuaimi, U. Amjad, Robert Fleischman, Tribikram Kundu","doi":"10.1177/14759217241235057","DOIUrl":"https://doi.org/10.1177/14759217241235057","url":null,"abstract":"Welding is a commonly used method for joining two or more parts together in steel construction. Various defects in weld regions such as cracks, pores, and slag inclusion can be present from the beginning, generated during the welding process, or can be developed while in service. Such defects are the weak spots that degrade the structure’s quality and can lead to structural failures. Therefore, early detection of these defects in welded joints, before visible cracks appear, is very important. Using appropriate ultrasonic non-destructive testing and evaluation (NDT&E) techniques, one can detect these defects and take remedial actions to prevent catastrophic structural failures. Guided acoustic wave-based techniques have been proven to be effective for damage detection in steel pipes and rods. Several studies have previously attempted to detect damage in steel tubes using guided ultrasonic waves. Unlike earlier attempts which mostly focused on conventional linear ultrasonic techniques, a relatively new nonlinear ultrasonic technique called sideband peak count-index (SPC-I) is carried out in this research. For this investigation, cast steel components and round hollow structural sections are welded together, and a four-point bending test is conducted under fatigue loading. The welded joints are continuously monitored in real time using strain gages and lead zirconate titanate (PZT) transducers. The PZT transducers are used to generate and receive guided acoustic waves. The signal is propagated through the specimen in a single-sided transmission mode setup. The strain gage readings and the nonlinear ultrasonic parameter, the SPC-I values, are monitored simultaneously. The results obtained from the nonlinear ultrasonic NDT&E measurements are compared with the data obtained from the strain gages to determine the robustness and reliability of the SPC-I technique for monitoring welded joints. This investigation also shows the potential effectiveness of the nonlinear ultrasonic parameter SPC-I for early detection of weld failure.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"2020 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246076","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":"SEACKgram: a targeted method of optimal demodulation-band selection for compound faults diagnosis of rolling bearing","authors":"Huibin Wang, Changfeng Yan, Yingjie Zhao, Shen Li, Jiadong Meng, Lixiao Wu","doi":"10.1177/14759217241235337","DOIUrl":"https://doi.org/10.1177/14759217241235337","url":null,"abstract":"Rolling bearing plays an important role in carrying and transmitting power in rotating machinery, and the bearing fault is easy to lead to mechanical accidents, resulting in huge losses and casualties. Therefore, the condition monitoring and diagnosis of rolling bearings are very important to improve the safety of equipment. Compound fault is a common fault evolved from the initial defect, which is characterized by randomness, coupling, concealment, and secondary. The existence of these characteristics brings great challenges to the accurate diagnosis of compound faults. In the diagnosis of compound faults, the traditional methods that select the single optimal demodulation frequency band for analysis and identification sometimes cannot completely extract multiple fault components, which are prone to miss diagnosis and misdiagnosis. In order to solve this problem, the SEACKgram method is proposed by constructing a Square Envelope Unbiased Autocorrelation Correlation Kurtosis (SEACK) index. The frequency band of the original signal is divided by the Maximal Overlap Discrete Wavelet Packet Transform, and the SEACK index is used to quantitatively describe the fault signals of different frequency bands. According to the different fault periods, the resonant frequency bands of the maximum SEACK value are selected, then the resonance band signal is analyzed by square envelope spectrum, and the fault type is identified according to the fault characteristic frequency. The simulated and experimental vibration signals of rolling bearings with compound faults are used to verify the feasibility of the proposed method. The results show that the proposed SEACKgram can improve the accuracy of compound faults identification and would be applied in engineering practice to a certain extent.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"35 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140249912","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":"Vibrational resonance mechanism in the high-order-degradation bistable system and its application in fault diagnosis","authors":"Haitao Xu, Shengxi Zhou","doi":"10.1177/14759217241229610","DOIUrl":"https://doi.org/10.1177/14759217241229610","url":null,"abstract":"Bearings play an important role in the rotating machinery. Timely fault detection and maintenance can prevent catastrophic incidents caused by bearing faults. As one of the advanced techniques to extract the weak characteristics of bearing fault, the methods based on the vibrational resonance (VR) mechanism can effectively amplify the weak characteristics. However, first, the effect of the barrier width and the barrier height on the VR mechanism has not been investigated. Second, the application of the VR mechanism requires accurate prior knowledge, which limits its application. In this paper, the high-order-degradation bistable potential is decoupled so that the effect of the barrier width or the barrier height on the VR mechanism can be easily analyzed. Then, the initial frequency detection technique based on the 1.5-dimension envelope spectrum and the logical operation of sets is proposed, which helps to adaptively provide accurate prior knowledge for the application of the VR mechanism in the high-order-degradation bistable system (HBSVR). Finally, experimental results show that the proposed method is more advantageous in detecting single and compound faults compared with other state-of-the-art methods.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"33 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253995","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":"Multi-objective SHM sensor path optimisation for damage detection in large composite stiffened panels","authors":"L. Morse, Ilias N. Giannakeas, Vincenzo Mallardo, Z. Sharif-Khodaei, M. Aliabadi","doi":"10.1177/14759217241231701","DOIUrl":"https://doi.org/10.1177/14759217241231701","url":null,"abstract":"This work proposes a novel methodology for the automatic multi-objective optimisation of sensor paths in structural health monitoring (SHM) sensor networks using archived multi-objective simulated annealing. Using all of the sensor paths within a sensor network may not always be beneficial during damage detection. Many sensor paths may experience significant signal noise, attenuation, and wave mode conversion due to the presence of features, such as stiffeners, and hence impair the detection accuracy of the overall system. Many paths will also contribute little to the overall coverage level or damage detection accuracy of the network and can be ignored, reducing complexity. Knowing which paths to include, and which to exclude, can require significant prior expert knowledge, which may not always be available. Furthermore, even when expert knowledge is considered, the optimum path selection might not be achieved. Therefore, this work proposes a novel automatic procedure for optimising the sensor paths of an SHM sensor network to maximise coverage level, maximise damage detection accuracy and minimise the overall signal noise in the network due to geometric features. This procedure was tested on a real-world large composite stiffened panel with many geometric features in the form of frames and stiffeners. Compared to using all of the available sensor pairs, the optimised network exhibits superior performance in terms of detection accuracy and overall noise. It was also found to provide very similar performance, in terms of coverage level and overall signal noise, to a sensor path network designed based on prior expert knowledge but provided up to 35% higher damage detection accuracy. As a result, the novel procedure proposed in this work has the capability to design high-performing SHM sensor path networks for structures with complex geometries but without the need for prior expert knowledge, making SHM more accessible to the engineering community.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254750","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":"Damage detection and location using a simulated annealing-artificial hummingbird algorithm with an improved objective function","authors":"Zhen Chen, Yikai Wang, Kun Zhang, T. H. Chan, Zhihao Wang","doi":"10.1177/14759217241233733","DOIUrl":"https://doi.org/10.1177/14759217241233733","url":null,"abstract":"Swarm intelligence algorithms and finite element model update technology are important issues in the field of structural damage detection. However, the complexity of engineering structural models normally leads to low computational efficiency and large detection errors in structural damage detection. To solve these problems, a simulated annealing-artificial hummingbird algorithm (SA-AHA) is proposed based on the artificial hummingbird algorithm (AHA). The Sobol sequence is used to improve the identification efficiency by optimizing the initial population distribution of the AHA. Then, the simulated annealing strategy is introduced to improve the detection accuracy by enhancing the global search ability of the AHA. In addition, a novel objective function is presented by combining modal flexibility residual, natural frequency residual, and trace sparse constraint of the structural model. Numerical simulations of a simply supported beam and a two-story rigid frame are carried out to verify the superiority of the proposed SA-AHA and the objective function. Simulation results demonstrate that the SA-AHA is better than the AHA in terms of damage computational efficiency and damage identification accuracy. Moreover, the new objective function can be more excellently applied to the SA-AHA than the previous one, which can be effectively used to locate and estimate the damage of the proposed SA-AHA in structure. Finally, experimental studies are carried out to verify the proposed method.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"134 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251261","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":"Multi-frequency probabilistic imaging fusion for impact localization on aircraft composite structures","authors":"Deshuang Deng, Xu Zeng, Zhengyan Yang, Yu Yang, Sheng Zhang, Shuyi Ma, Hao Xu, Lei Yang, Zhanjun Wu","doi":"10.1177/14759217241233181","DOIUrl":"https://doi.org/10.1177/14759217241233181","url":null,"abstract":"Since the internal barely visible damage of aircraft composite structures caused by the impact is a critical problem, impact monitoring is essential for the integrity and reliability of aircraft composite structures. This paper presents a multi-frequency probabilistic imaging fusion method for localizing impacts on aircraft composite structures. To capture the impact signals, a network of distributed sensors is mounted on the structure. The impact signals are then processed using the continuous wavelet transform (CWT) to extract the multi-frequency narrowband Lamb wave signals. The time difference of arrival (TDOA), a key feature of the impact source, is measured using averaging techniques employed in the normalized variance sequence. Subsequently, a probabilistic imaging function is established, and the TDOA of narrowband Lamb wave signals at each frequency is used as the feature input to generate the multi-frequency probabilistic imaging results. To determine the performance of the imaging results at each frequency, an efficiency index is introduced, allowing for the retention or abandonment of the imaging results. By utilizing the retained multi-frequency probabilistic imaging results, the proposed method achieves impact localization through imaging fusion. Experimental verification is conducted on a stiffened aircraft composite panel, and a comparison is made with two existing methods: the hyperbolic locus imaging method and the virtual time reversal imaging method. The results show that the proposed method can significantly improve localization accuracy compared to the existing methods, and is effective even in the presence of measurement noise.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"31 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252259","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":"Thermo-oxidative aging state detection of rubber sandwich structure using synchrosqueezing transform-assisted feature extraction and customized detection indicator","authors":"Xujun Zhao, Ye Tian, Dalong Han, Yue Si, Meng Zhang, Liandi He","doi":"10.1177/14759217241233711","DOIUrl":"https://doi.org/10.1177/14759217241233711","url":null,"abstract":"Rubber sandwich structures (RSSs) are used extensively in mechanical engineering. The aging state detection of such structures is urgently required to avoid disastrous accidents. However, this is still a challenging task owing to the weakness of the rubber layer aging feature information contained in the vibration signal of the RSS and the lack of effective aging feature information extraction techniques. Thus, an aging state detection method for the RSS using synchrosqueezing transform (SST)-assisted feature extraction and a customized detection indicator was proposed in this study. First, the SST was used to decompose the vibration signal of the RSS, and a time-frequency (TF) spectrum with an enhanced aging state feature was obtained. Second, a TF bandpass filter was constructed and used to filter the information unrelated to the aging state feature from the TF spectrum. Subsequently, a hard threshold denoising method was applied to reduce noise in the filtered TF spectrum. Then, the aging state signal was reconstructed using the inverse SST. Finally, a customized detection indicator was constructed and its value was calculated to detect the aging state of the RSS. A thermo-oxidative aging dataset of the RSS from Xi’an Jiaotong University was used to validate the proposed method. The experimental results showed that the proposed method was more effective than other methods.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"41 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259147","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":"Defect detection and localisation using guided wave images from array data processed by nonlinear autoregressive exogenous model and Gamma statistical operator","authors":"Kangwei Wang, Jie Zhang, Yang Xiao, A. Croxford, Yong Yang","doi":"10.1177/14759217241231498","DOIUrl":"https://doi.org/10.1177/14759217241231498","url":null,"abstract":"Guided wave structural health monitoring (GWSHM) systems, using the delay-and-sum imaging algorithm, are an efficient solution to detect and localise defects in industrial structures. However, the image artifacts caused by either imperfect detection or sensor lay-out limitations make it difficult to identify and locate defects accurately. In order to enhance the performance of defect detection and localisation in GWSHM systems, this paper proposes a three-step procedure for post-processing guided wave signals prior to image formation. In the first step, the signals are processed using the nonlinear autoregressive exogenous model to suppress noise from benign features. The second step calculates the probability of defect presence based on the rescaled Gamma cumulative distribution function. This probabilistic threshold is then determined from the quantile mapping. Finally, a guide wave image is formed using the delay-and-sum imaging algorithm. The experimental validation was performed to inspect a 6 mm-diameter through-thickness circular hole on an aluminium plate and the defects were further scaled as simulated datasets to test its detectability under various amplitudes. In the second procedure step, the detection and localisation performance of the proposed procedure was compared with that of using the signal difference coefficient and the Rayleigh maximum likelihood estimator. It is shown that the proposed procedure can enhance the contrast between damaged and undamaged regions, providing more reliable and accurate guided wave images.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"39 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259371","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":"Interpretable Siamese dual attention enhancement transfer compound diagnostic model for unbalanced samples","authors":"Kun Xu, Shunming Li, Xiaodong Miao, Hua Wang, Ranran Li","doi":"10.1177/14759217241232985","DOIUrl":"https://doi.org/10.1177/14759217241232985","url":null,"abstract":"The intelligent transfer diagnosis model is used to address the issue of feature drift caused by the changing working conditions of rotating parts in engineering. However, few models can perform transfer diagnosis on multiple unbalanced samples of rotating parts simultaneously, and even fewer models can visually enhance the domain-invariant features, making them more interpretable. To address these issues, we propose a novel interpretable Siamese dual attention enhancement transfer compound diagnosis model for unbalanced samples. The model can diagnose multiple rotating parts simultaneously and consists of a channel feature attention enhancement (CFAE) network, a fragment feature attention enhancement (FFAE) network, and a Siamese feature fusion (SFF) network. The CFAE network enhances features of different convolutional channels, the FFAE network improves segment features in various frequency domains, and the SFF network extracts domain-invariant features of diverse rotating components under varying working conditions. The model is validated using bearing fault data collected under different loads and planetary gear fault data obtained at varying speeds. Its diagnostic accuracy remains above 96.4%, and the diagnostic variance is controlled within 1.0%. The model has good interpretability for imbalanced sample domain-invariant features, providing an effective tool for interpretable transfer diagnosis in this compound engineering situation.","PeriodicalId":515545,"journal":{"name":"Structural Health Monitoring","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260044","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}