Pub Date : 2023-08-02DOI: 10.1177/14759217231189972
Á. González-Jiménez, L. Lomazzi, Rafael Junges, M. Giglio, A. Manes, F. Cadini
Damage diagnosis of thin-walled structures has been successfully performed through methods based on tomography and machine learning-driven methods. According to traditional approaches, diagnostic signals are excited and sensed on the structure through a permanently installed network of sensors and are processed to obtain information about the damage. Good performance characterizes methods that process Lamb waves, which are described by long propagation distances and high sensitivity to anomalies. Most of the methods require extracting damage-sensitive features from the diagnostic signals to drive the damage diagnosis task. However, this process can lead to loss of information, and the choice of the specific feature to extract may introduce biases that hamper damage diagnosis. Furthermore, traditional approaches do not perform well when composites are considered, due to the anisotropy, inhomogeneity, and complex damage mechanisms shown by this type of material. To boost the performance of methods for damage diagnosis of composite plates, this work proposes a convolutional neural network (CNN)-based algorithm that localizes damage by processing Lamb waves. Different from other methods, the proposed method does not require extracting features from the acquired signals and allows localizing damage through the regression approach. The method was tested against experimental observations of Lamb waves propagating in two composite panels and in a hybrid panel, and the performance of two different sensor arrays was investigated. The pseudo-damage approach was used to generate large enough datasets for training the CNNs, and the performance of the framework was evaluated by localizing pseudo-damage and real damage determined by low-velocity impacts. The CNN-driven method accurately localized damage in all the considered scenarios, and it also outperformed traditional damage indices-based approaches, such as the reconstruction algorithm for probabilistic inspection of defects.
{"title":"Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach","authors":"Á. González-Jiménez, L. Lomazzi, Rafael Junges, M. Giglio, A. Manes, F. Cadini","doi":"10.1177/14759217231189972","DOIUrl":"https://doi.org/10.1177/14759217231189972","url":null,"abstract":"Damage diagnosis of thin-walled structures has been successfully performed through methods based on tomography and machine learning-driven methods. According to traditional approaches, diagnostic signals are excited and sensed on the structure through a permanently installed network of sensors and are processed to obtain information about the damage. Good performance characterizes methods that process Lamb waves, which are described by long propagation distances and high sensitivity to anomalies. Most of the methods require extracting damage-sensitive features from the diagnostic signals to drive the damage diagnosis task. However, this process can lead to loss of information, and the choice of the specific feature to extract may introduce biases that hamper damage diagnosis. Furthermore, traditional approaches do not perform well when composites are considered, due to the anisotropy, inhomogeneity, and complex damage mechanisms shown by this type of material. To boost the performance of methods for damage diagnosis of composite plates, this work proposes a convolutional neural network (CNN)-based algorithm that localizes damage by processing Lamb waves. Different from other methods, the proposed method does not require extracting features from the acquired signals and allows localizing damage through the regression approach. The method was tested against experimental observations of Lamb waves propagating in two composite panels and in a hybrid panel, and the performance of two different sensor arrays was investigated. The pseudo-damage approach was used to generate large enough datasets for training the CNNs, and the performance of the framework was evaluated by localizing pseudo-damage and real damage determined by low-velocity impacts. The CNN-driven method accurately localized damage in all the considered scenarios, and it also outperformed traditional damage indices-based approaches, such as the reconstruction algorithm for probabilistic inspection of defects.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49579813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Optimal sensor placement for timber architecture heritage poses a significant challenge due to the unique structural types and complex monitoring purposes. In this study, a three-stage method is proposed, taking a courtyard-style heritage, built 133 years ago, as an example. First, a finite element model that accounted for the parameter randomness and initial damage was constructed using a genetic algorithm (GA) and experimental results. Second, a new weighted fitness function of logarithmic type was developed for multi-type sensors and multi-objective monitoring. Third, a novel genetic algorithm, Meta-GA, was proposed, introducing competition group mechanisms and gene libraries to improve optimal capability while maintaining computational efficiency. The Meta-GA is then compared to the other two optimization modes using seven indexes. Finally, damage detection capability was tested for the proposed three schemes at noise levels of 0%, 5%, and 10%. The results reveal that the proposed three-stage method with Meta-GA can provide the best solution.
{"title":"Optimal placement method of multi-objective and multi-type sensors for courtyard-style timber historical buildings based on Meta-genetic algorithm","authors":"Chengwen Zhang, Qing Chun, J. Leng, Yijie Lin, Yuchong Qian, Guang-qiang Cao, Qingchong Dong","doi":"10.1177/14759217231181724","DOIUrl":"https://doi.org/10.1177/14759217231181724","url":null,"abstract":"Optimal sensor placement for timber architecture heritage poses a significant challenge due to the unique structural types and complex monitoring purposes. In this study, a three-stage method is proposed, taking a courtyard-style heritage, built 133 years ago, as an example. First, a finite element model that accounted for the parameter randomness and initial damage was constructed using a genetic algorithm (GA) and experimental results. Second, a new weighted fitness function of logarithmic type was developed for multi-type sensors and multi-objective monitoring. Third, a novel genetic algorithm, Meta-GA, was proposed, introducing competition group mechanisms and gene libraries to improve optimal capability while maintaining computational efficiency. The Meta-GA is then compared to the other two optimization modes using seven indexes. Finally, damage detection capability was tested for the proposed three schemes at noise levels of 0%, 5%, and 10%. The results reveal that the proposed three-stage method with Meta-GA can provide the best solution.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":"1 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65887284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-25DOI: 10.1177/14759217231188141
Haiying Liang, Chencheng Zhao, Yang Liu, Chunyue Gao, Ningyuan Cui, C. Sbarufatti, M. Giglio
The rotor system during its operation is susceptible to various faults such as unbalance, rub-impact, crack, and misalignment, which usually induce the rotor system to exhibit nonlinear behavior. Some linear diagnosis methods are unable to extract nonlinear characteristics of the faulty rotor system. However, existing nonlinear fault diagnosis methods can describe the nonlinear characteristics but cannot quantitatively indicate the severity of rub-impact faults. To address this issue, this study combines the nonlinear output frequency response functions weighted contribution rate (WNOFRFs) and JS divergence to develop an improved fault diagnosis approach, WNOFRFs based on the JS divergence (WNOFRFs-JS). And a superior NOFRFs-associated index JSRm is developed to indicate the severity of faults. In addition, a sensitive factor is defined to evaluate the sensitivity of the index. The performance of this approach is verified by an established dynamic model and a rotor rub-impact experimental rig. The results prove the effectiveness and merits of this approach for the identification of rotor rub-impact. JSRm is especially sensitive to rub-impact and can also quantitatively detect the severity of faults. The present approach can accurately and quantitatively identify the rub-impact rotor system. These advantages enable the improved WNOFRFs to be applied in the fault diagnosis and condition monitoring of rotating machinery and even other nonlinear systems.
{"title":"Research on a quantitative fault diagnosis method for rotor rub-impact","authors":"Haiying Liang, Chencheng Zhao, Yang Liu, Chunyue Gao, Ningyuan Cui, C. Sbarufatti, M. Giglio","doi":"10.1177/14759217231188141","DOIUrl":"https://doi.org/10.1177/14759217231188141","url":null,"abstract":"The rotor system during its operation is susceptible to various faults such as unbalance, rub-impact, crack, and misalignment, which usually induce the rotor system to exhibit nonlinear behavior. Some linear diagnosis methods are unable to extract nonlinear characteristics of the faulty rotor system. However, existing nonlinear fault diagnosis methods can describe the nonlinear characteristics but cannot quantitatively indicate the severity of rub-impact faults. To address this issue, this study combines the nonlinear output frequency response functions weighted contribution rate (WNOFRFs) and JS divergence to develop an improved fault diagnosis approach, WNOFRFs based on the JS divergence (WNOFRFs-JS). And a superior NOFRFs-associated index JSRm is developed to indicate the severity of faults. In addition, a sensitive factor is defined to evaluate the sensitivity of the index. The performance of this approach is verified by an established dynamic model and a rotor rub-impact experimental rig. The results prove the effectiveness and merits of this approach for the identification of rotor rub-impact. JSRm is especially sensitive to rub-impact and can also quantitatively detect the severity of faults. The present approach can accurately and quantitatively identify the rub-impact rotor system. These advantages enable the improved WNOFRFs to be applied in the fault diagnosis and condition monitoring of rotating machinery and even other nonlinear systems.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47879525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-22DOI: 10.1177/14759217231182044
Zhuang Kang, Min Zhang, Wenming Cheng, Ruohui Hu
The brake pads of high-speed trains operate under complex and variable conditions, and the collected brake signals are easily affected by noise, making monitoring the health status of brake pads more difficult. A multi-representation adaptation network for online monitoring the health status of high-speed train brake pads, which are affected by noise under variable working conditions, is proposed in this study. First, a parameter-sharing deep residual network is used to extract the friction block features of the source and target domain data. Then, the features are mapped to different low-dimensional feature spaces through the inception adaptation module, and multiple representations are obtained. The network applies conditional maximum mean discrepancy to align representations of the source and target domains, thus learning multiple domain-invariant representations. Hence, the network acquires more knowledge of the friction block status and attenuates the interference of noise signals on the status monitoring. The friction block vibration data were collected from various brake disc speeds, and variable working condition-transfer experiments under the influence of noise were performed on the brake friction and bearing datasets. The results show that the proposed network outperforms other transfer methods, which can better extract and identify the status features of the friction block under the noise interference.
{"title":"Health status monitoring of high-speed train brake pads considering noise under variable working conditions","authors":"Zhuang Kang, Min Zhang, Wenming Cheng, Ruohui Hu","doi":"10.1177/14759217231182044","DOIUrl":"https://doi.org/10.1177/14759217231182044","url":null,"abstract":"The brake pads of high-speed trains operate under complex and variable conditions, and the collected brake signals are easily affected by noise, making monitoring the health status of brake pads more difficult. A multi-representation adaptation network for online monitoring the health status of high-speed train brake pads, which are affected by noise under variable working conditions, is proposed in this study. First, a parameter-sharing deep residual network is used to extract the friction block features of the source and target domain data. Then, the features are mapped to different low-dimensional feature spaces through the inception adaptation module, and multiple representations are obtained. The network applies conditional maximum mean discrepancy to align representations of the source and target domains, thus learning multiple domain-invariant representations. Hence, the network acquires more knowledge of the friction block status and attenuates the interference of noise signals on the status monitoring. The friction block vibration data were collected from various brake disc speeds, and variable working condition-transfer experiments under the influence of noise were performed on the brake friction and bearing datasets. The results show that the proposed network outperforms other transfer methods, which can better extract and identify the status features of the friction block under the noise interference.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42632843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-21DOI: 10.1177/14759217231186357
Shun Wang, Yongbo Li, Jiacong Zhang, Zheng Liu, Zichen Deng
Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information, and poor robustness to noises. Thus, this work proposes a symbol-scale analysis method to deal with the above problems. In one aspect, to capture fault features from the time series over multiple time scales, time-delay process of different intervals is utilized to obtain long-term features and short-term features. In the other aspect, symbol-scale analysis introduces a symbolization procedure and maps time series into a corresponding sequence of symbols to overcome the limitation of weak fault extraction under a low-signal-to-noise ratio environment. Moreover, the symbol-scale entropy approach is developed by integrating with diversity entropy, called symbol-scale diversity entropy. The effectiveness of the proposed strategy is intensively validated using two simulated signals and experimental cases. Results demonstrate its advantages in dynamic change tracking ability and calculation efficiency by comparing it with other state-of-the-art entropy methods. Apart from diversity entropy, the versatility of incorporating the proposed symbol-scale analysis and other entropy methods is also verified using experimental data.
{"title":"A novel feature extraction method based on symbol-scale diversity entropy and its application for fault diagnosis of rotary machines","authors":"Shun Wang, Yongbo Li, Jiacong Zhang, Zheng Liu, Zichen Deng","doi":"10.1177/14759217231186357","DOIUrl":"https://doi.org/10.1177/14759217231186357","url":null,"abstract":"Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information, and poor robustness to noises. Thus, this work proposes a symbol-scale analysis method to deal with the above problems. In one aspect, to capture fault features from the time series over multiple time scales, time-delay process of different intervals is utilized to obtain long-term features and short-term features. In the other aspect, symbol-scale analysis introduces a symbolization procedure and maps time series into a corresponding sequence of symbols to overcome the limitation of weak fault extraction under a low-signal-to-noise ratio environment. Moreover, the symbol-scale entropy approach is developed by integrating with diversity entropy, called symbol-scale diversity entropy. The effectiveness of the proposed strategy is intensively validated using two simulated signals and experimental cases. Results demonstrate its advantages in dynamic change tracking ability and calculation efficiency by comparing it with other state-of-the-art entropy methods. Apart from diversity entropy, the versatility of incorporating the proposed symbol-scale analysis and other entropy methods is also verified using experimental data.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47229794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fault diagnosis of rotating machine is essential to maintain its operational safety and avoid catastrophic accidents. The vibration signals collected from the varying speed rotating machinery are non-stationary, and time–frequency analysis (TFA) is a feasible method for varying speed fault diagnosis by revealing time-varying instantaneous frequency (IF) information in signals. However, most conventional TFA methods are not specifically designed for rotating machinery vibration signals and may not be able to handle these signals, especially in the presence of noise. Therefore, this paper develops a unique TFA method designated as synchroextracting frequency synchronous chirplet transform (SEFSCT) for vibration signal analysis and fault diagnosis of rotating machinery. In the proposed method, the frequency synchronous chirplet transform (FSCT) that utilizes the frequency synchronous chirp rate is first introduced, which takes fully into account the intrinsic proportional relationship of time-varying IFs of the signal. Then, to further concentrate the time–frequency representation (TFR) of FSCT, the synchroextracting operator is constructed based on the Gaussian modulated linear chirp model and the SEFSCT is naturally developed by integrating the FSCT and synchroextracting operator. With the proposed SEFSCT, a high-quality TFR can be generated, thus the time-varying IFs and mechanical failure can be accurately identified. The SEFSCT is employed to deal with synthetic and actual signals, and the results illustrate its efficacy in handling non-stationary signals and diagnosing the mechanical failure.
{"title":"Synchroextracting frequency synchronous chirplet transform for fault diagnosis of rotating machinery under varying speed conditions","authors":"Chuancang Ding, Weiguo Huang, Changqing Shen, Xingxing Jiang, J. Wang, Zhongkui Zhu","doi":"10.1177/14759217231181308","DOIUrl":"https://doi.org/10.1177/14759217231181308","url":null,"abstract":"The fault diagnosis of rotating machine is essential to maintain its operational safety and avoid catastrophic accidents. The vibration signals collected from the varying speed rotating machinery are non-stationary, and time–frequency analysis (TFA) is a feasible method for varying speed fault diagnosis by revealing time-varying instantaneous frequency (IF) information in signals. However, most conventional TFA methods are not specifically designed for rotating machinery vibration signals and may not be able to handle these signals, especially in the presence of noise. Therefore, this paper develops a unique TFA method designated as synchroextracting frequency synchronous chirplet transform (SEFSCT) for vibration signal analysis and fault diagnosis of rotating machinery. In the proposed method, the frequency synchronous chirplet transform (FSCT) that utilizes the frequency synchronous chirp rate is first introduced, which takes fully into account the intrinsic proportional relationship of time-varying IFs of the signal. Then, to further concentrate the time–frequency representation (TFR) of FSCT, the synchroextracting operator is constructed based on the Gaussian modulated linear chirp model and the SEFSCT is naturally developed by integrating the FSCT and synchroextracting operator. With the proposed SEFSCT, a high-quality TFR can be generated, thus the time-varying IFs and mechanical failure can be accurately identified. The SEFSCT is employed to deal with synthetic and actual signals, and the results illustrate its efficacy in handling non-stationary signals and diagnosing the mechanical failure.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43403216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-17DOI: 10.1177/14759217231183663
Haoyu Zhang, Stephen Wu, Yong Huang, Hui Li
In structural health monitoring (SHM), there is an increasing demand for real-time image-based damage detection. Such a technology is essential for minimizing hazard loss caused by delayed emergency response after earthquakes or other natural disasters, or service interruption during structural inspection. Compressive sampling (CS) is a promising solution to achieve such a goal by greatly reducing the power consumption on high-resolution image transmission when using wireless devices. However, conventional CS failed to achieve high enough compression ratios, while existing generative-model-based CS requires laboriously training a high-quality generator with many large-scale images. To overcome such a bottleneck that hinders the practical use of CS in SHM, we propose a multitask CS algorithm that only relies on existing generators trained by low-pixel crack images. By exploiting the new discovery that similar crack images share a similar sparsity pattern in their latent vectors mapped by the generator, our algorithm achieves higher crack detection accuracy and robustness within a much shorter time when using a high data compression ratio. We verify the effectiveness of the proposed CS algorithm using synthetic and real image data. The results demonstrate that this work has moved a step closer toward successful implementation of operational CS-based crack detection systems in real-time SHM.
{"title":"Robust multitask compressive sampling via deep generative models for crack detection in structural health monitoring","authors":"Haoyu Zhang, Stephen Wu, Yong Huang, Hui Li","doi":"10.1177/14759217231183663","DOIUrl":"https://doi.org/10.1177/14759217231183663","url":null,"abstract":"In structural health monitoring (SHM), there is an increasing demand for real-time image-based damage detection. Such a technology is essential for minimizing hazard loss caused by delayed emergency response after earthquakes or other natural disasters, or service interruption during structural inspection. Compressive sampling (CS) is a promising solution to achieve such a goal by greatly reducing the power consumption on high-resolution image transmission when using wireless devices. However, conventional CS failed to achieve high enough compression ratios, while existing generative-model-based CS requires laboriously training a high-quality generator with many large-scale images. To overcome such a bottleneck that hinders the practical use of CS in SHM, we propose a multitask CS algorithm that only relies on existing generators trained by low-pixel crack images. By exploiting the new discovery that similar crack images share a similar sparsity pattern in their latent vectors mapped by the generator, our algorithm achieves higher crack detection accuracy and robustness within a much shorter time when using a high data compression ratio. We verify the effectiveness of the proposed CS algorithm using synthetic and real image data. The results demonstrate that this work has moved a step closer toward successful implementation of operational CS-based crack detection systems in real-time SHM.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47858151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To more accurately predict remaining useful life (RUL) and quantitatively evaluate the uncertainty of the predicted results, a performance degradation assessment framework based on semi-analytical solution of self-similar stable distribution process is proposed. The established performance degradation model based on adaptive fractional Lévy stable motion (AFLSM) is more flexible in revealing the long-range dependence, non-Gaussian, and heavy-tailed distribution properties of the incremental behavior. The corresponding stable distribution parameters are estimated through characteristic function method, and Hurst exponent is calculated based on the generalized Hurst exponent approach with narrower confidence interval. Aiming at the difficulties in solving the exact analytical solution and the excessive computation of the numerical solution in the whole process, based on Mellin-Stieltjes transform and direct integration, a semi-analytical solution of RUL distribution function is proposed, which can be readily implemented in practical equipment operations. The proposed performance degradation assessment framework is validated by the novel truck transmission dataset and the benchmark rolling bearing dataset. Experimental results indicate that the developed framework is more effective and superior than other state-of-the-art approaches in terms of RUL prediction.
{"title":"Performance degradation assessment for mechanical system based on semi-analytical solution of self-similar stable distribution process","authors":"Qiang Li, Hongkun Li, Zhenhui Ma, Xuejun Liu, X. Guan, Xiaoli Zhang","doi":"10.1177/14759217231181678","DOIUrl":"https://doi.org/10.1177/14759217231181678","url":null,"abstract":"To more accurately predict remaining useful life (RUL) and quantitatively evaluate the uncertainty of the predicted results, a performance degradation assessment framework based on semi-analytical solution of self-similar stable distribution process is proposed. The established performance degradation model based on adaptive fractional Lévy stable motion (AFLSM) is more flexible in revealing the long-range dependence, non-Gaussian, and heavy-tailed distribution properties of the incremental behavior. The corresponding stable distribution parameters are estimated through characteristic function method, and Hurst exponent is calculated based on the generalized Hurst exponent approach with narrower confidence interval. Aiming at the difficulties in solving the exact analytical solution and the excessive computation of the numerical solution in the whole process, based on Mellin-Stieltjes transform and direct integration, a semi-analytical solution of RUL distribution function is proposed, which can be readily implemented in practical equipment operations. The proposed performance degradation assessment framework is validated by the novel truck transmission dataset and the benchmark rolling bearing dataset. Experimental results indicate that the developed framework is more effective and superior than other state-of-the-art approaches in terms of RUL prediction.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44087887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-14DOI: 10.1177/14759217231181679
Zewen Zhou, Bingyan Chen, B. Huang, Weihua Zhang, F. Gu, A. Ball, Xue Gong
Blind deconvolution (BD) has proven to be an effective approach to detecting repetitive transients caused by bearing faults. However, BD suffers from instability issues including excessive sensitivity of kurtosis-guided BD methods to the single impulse and high computational time cost of the eigenvector algorithm-aided BD methods. To address these critical issues, this paper proposed a novel BD method maximizing negative entropy (NE), shortened as maximum negative entropy deconvolution (MNED). MNED utilizes NE instead of kurtosis as the optimization metric and optimizes the filter coefficients through the objective function method. The effectiveness of MNED in enhancing repetitive transients is illustrated through a simulation case and two experimental cases. A quantitative comparison with three existing BD methods demonstrates the advantages of MNED in fault detection and computational efficiency. In addition, the performance of the four methods under different filter lengths and external shocks is compared. MNED exhibits lower sensitivity to random impulse noise than the kurtosis-guided BD methods and higher computational efficiency than the BD methods based on the eigenvalue algorithm. The simulation and experimental results demonstrate that MNED is a robust and cost-effective method for bearing fault diagnosis and condition monitoring.
{"title":"Maximum negative entropy deconvolution and its application to bearing condition monitoring","authors":"Zewen Zhou, Bingyan Chen, B. Huang, Weihua Zhang, F. Gu, A. Ball, Xue Gong","doi":"10.1177/14759217231181679","DOIUrl":"https://doi.org/10.1177/14759217231181679","url":null,"abstract":"Blind deconvolution (BD) has proven to be an effective approach to detecting repetitive transients caused by bearing faults. However, BD suffers from instability issues including excessive sensitivity of kurtosis-guided BD methods to the single impulse and high computational time cost of the eigenvector algorithm-aided BD methods. To address these critical issues, this paper proposed a novel BD method maximizing negative entropy (NE), shortened as maximum negative entropy deconvolution (MNED). MNED utilizes NE instead of kurtosis as the optimization metric and optimizes the filter coefficients through the objective function method. The effectiveness of MNED in enhancing repetitive transients is illustrated through a simulation case and two experimental cases. A quantitative comparison with three existing BD methods demonstrates the advantages of MNED in fault detection and computational efficiency. In addition, the performance of the four methods under different filter lengths and external shocks is compared. MNED exhibits lower sensitivity to random impulse noise than the kurtosis-guided BD methods and higher computational efficiency than the BD methods based on the eigenvalue algorithm. The simulation and experimental results demonstrate that MNED is a robust and cost-effective method for bearing fault diagnosis and condition monitoring.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48670085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-10DOI: 10.1177/14759217231183143
V. Toufigh, Iman Ranjbar
This study presented an unsupervised anomaly detection-based framework for distributed damage detection in concrete using ultrasonic response signals. A deep fully connected auto-encoder was employed to reconstruct the ultrasonic response signals. This model was trained on the intact specimen’s responses. The auto-encoder demonstrated a relatively high prediction error encountering the damaged specimen’s responses. Two time-domain features (mean squared error and reconstructed-to-original signal ratio) and one frequency-domain feature (fundamental amplitude ratio) were defined to measure the reconstruction error of the auto-encoder (the damage-sensitive features). Finally, the Isolation Forest algorithm was implemented for anomaly (damage) detection. The beauty of this framework is that it requires a few numbers of data only from the intact specimen for training the auto-encoder and collecting the binary decision trees of the Isolation Forest. The framework was successfully implemented for damage detection in five geopolymer concrete specimens with different mix proportions. Using all three introduced damage-sensitive features, the framework demonstrated an average prediction accuracy of 95.0% and 93.0% for damaged and intact stages, respectively.
{"title":"Unsupervised deep learning framework for ultrasonic-based distributed damage detection in concrete: integration of a deep auto-encoder and Isolation Forest for anomaly detection","authors":"V. Toufigh, Iman Ranjbar","doi":"10.1177/14759217231183143","DOIUrl":"https://doi.org/10.1177/14759217231183143","url":null,"abstract":"This study presented an unsupervised anomaly detection-based framework for distributed damage detection in concrete using ultrasonic response signals. A deep fully connected auto-encoder was employed to reconstruct the ultrasonic response signals. This model was trained on the intact specimen’s responses. The auto-encoder demonstrated a relatively high prediction error encountering the damaged specimen’s responses. Two time-domain features (mean squared error and reconstructed-to-original signal ratio) and one frequency-domain feature (fundamental amplitude ratio) were defined to measure the reconstruction error of the auto-encoder (the damage-sensitive features). Finally, the Isolation Forest algorithm was implemented for anomaly (damage) detection. The beauty of this framework is that it requires a few numbers of data only from the intact specimen for training the auto-encoder and collecting the binary decision trees of the Isolation Forest. The framework was successfully implemented for damage detection in five geopolymer concrete specimens with different mix proportions. Using all three introduced damage-sensitive features, the framework demonstrated an average prediction accuracy of 95.0% and 93.0% for damaged and intact stages, respectively.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":" ","pages":""},"PeriodicalIF":6.6,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46018002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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