Pub Date : 2023-02-01DOI: 10.1784/insi.2023.65.2.87
Juwei Zhang, Zengguang Zhang, Bo Liu
In order to avoid the influence of the interfering magnetic field, a wire rope magnetic memory detection platform under the excitation of a weak magnetic field is built and then the enhanced magnetic memory signal, infrared signal and visible light signal are fused to increase the recognition rate and reduce the identification error of the quantitative identification of broken wires, realising more effective defect identification and life assessment of wire ropes. The magnetic memory signal is denoised by applying intrinsic time-scale decomposition (ITD) and a wavelet algorithm to effectively remove noise such as the signal baseline and strand waves. The image fusion method based on curvelet transform is applied to realise pixel-level fusion of the defect images. The extracted fused image features are used as the input to the support vector machine optimised by the grey wolf optimiser (GWO-SVM) neural network to quantitatively identify wire rope defects. The results show that the image fusion method is better than the single detection method for broken wire identification.
{"title":"Non-destructive Testing of Steel Wire Ropes Incorporating Magnetic Memory Information","authors":"Juwei Zhang, Zengguang Zhang, Bo Liu","doi":"10.1784/insi.2023.65.2.87","DOIUrl":"https://doi.org/10.1784/insi.2023.65.2.87","url":null,"abstract":"In order to avoid the influence of the interfering magnetic field, a wire rope magnetic memory detection platform under the excitation of a weak magnetic field is built and then the enhanced magnetic memory signal, infrared signal and visible light signal are fused to increase the recognition\u0000 rate and reduce the identification error of the quantitative identification of broken wires, realising more effective defect identification and life assessment of wire ropes. The magnetic memory signal is denoised by applying intrinsic time-scale decomposition (ITD) and a wavelet algorithm\u0000 to effectively remove noise such as the signal baseline and strand waves. The image fusion method based on curvelet transform is applied to realise pixel-level fusion of the defect images. The extracted fused image features are used as the input to the support vector machine optimised by the\u0000 grey wolf optimiser (GWO-SVM) neural network to quantitatively identify wire rope defects. The results show that the image fusion method is better than the single detection method for broken wire identification.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133658258","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}
Machine vision plays an increasingly important role in industrial product quality detection. During processing, scratches, dents and other defects are inevitable on the surface of a smooth part. Although surface defects do not affect the overall performance of the product, their existence is unacceptable when a perfect product is required. The surface defect detection method based on machine vision and deep convolutional neural networks overcomes, to a certain extent, the problem of low detection efficiency, high false detection and missing detection rates in the traditional detection method. In this paper, a multistream semantic segmentation neural network is proposed to identify defects on smooth parts. Taking a seatbelt buckle as an example, the scratch and crush defects on the surface are classified. The network takes DeepLabV3+ as the framework and three types of image stream as the input of the network. In the backbone feature extraction network, the Xception structure is improved to MobilenetV2 and the convolutional block attention module (CBAM) is introduced into the decoding network, which improves the operational efficiency and accuracy. Compared with other classical networks, this network demonstrates good performance in the image dataset of the seatbelt buckle and realises fast and accurate semantic segmentation and classification of surface defects. The evaluation results of the network model have been significantly improved.
{"title":"Semantic Segmentation of Surface Defects Of Smooth Parts Based on Deep Convolutional Neural Networks","authors":"Huai-shu Hou, Runze Zhang, Chaofei Jiao, Zhifan Zhao, Xinchong Fang, Jinhao Li, Dachuan Xu","doi":"10.1784/insi.2023.65.2.103","DOIUrl":"https://doi.org/10.1784/insi.2023.65.2.103","url":null,"abstract":"Machine vision plays an increasingly important role in industrial product quality detection. During processing, scratches, dents and other defects are inevitable on the surface of a smooth part. Although surface defects do not affect the overall performance of the product, their existence\u0000 is unacceptable when a perfect product is required. The surface defect detection method based on machine vision and deep convolutional neural networks overcomes, to a certain extent, the problem of low detection efficiency, high false detection and missing detection rates in the traditional\u0000 detection method. In this paper, a multistream semantic segmentation neural network is proposed to identify defects on smooth parts. Taking a seatbelt buckle as an example, the scratch and crush defects on the surface are classified. The network takes DeepLabV3+ as the framework and three\u0000 types of image stream as the input of the network. In the backbone feature extraction network, the Xception structure is improved to MobilenetV2 and the convolutional block attention module (CBAM) is introduced into the decoding network, which improves the operational efficiency and accuracy.\u0000 Compared with other classical networks, this network demonstrates good performance in the image dataset of the seatbelt buckle and realises fast and accurate semantic segmentation and classification of surface defects. The evaluation results of the network model have been significantly improved.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":" 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113951093","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}
Pub Date : 2023-02-01DOI: 10.1784/insi.2023.65.2.80
T. V. Shyam, A. Sharma, V. Patankar, A. Kaushik, S. Sinha, L. Ferry
A compact pulsed eddy current testing probe is being developed for the online dilation measurement of test specimens, examined in the high-temperature environment of material testing reactors. The probe has to be canned with metallic clad to protect it from the corrosive sodium-potassium coolant medium present in the material testing reactor. Electromagnetic modelling of the probe was carried out to compute the distribution of time-dependent eddy current density in the vicinity of the probe. It is understood that the slope of the pulsed eddy current signals in the specific time zone where the lift-off point of intersection occurs show a good correlation to the distance between the face of the probe and the test specimen. This paper discusses the experimental study of employing different measurement methodologies at room temperature with the canned pulsed eddy current probe to evaluate its feasibility for dilation measurement. It is observed that a phenomenon of a divergence of signals, akin to the lift-off point of intersection, occurs just after the transient part of the pulsed eddy current signals. Slope analysis of these diverging pulsed eddy current signals was carried out for characterisation of the probe in the air medium as well as the metallic medium present in the gap between the probe face and the test specimen.
{"title":"Experimental Study of Different Innovative Measurement Methodologies Applied to a Canned Pulsed Eddy Current Testing Probe Suitable For Dilation Measurement of Test Specimens","authors":"T. V. Shyam, A. Sharma, V. Patankar, A. Kaushik, S. Sinha, L. Ferry","doi":"10.1784/insi.2023.65.2.80","DOIUrl":"https://doi.org/10.1784/insi.2023.65.2.80","url":null,"abstract":"A compact pulsed eddy current testing probe is being developed for the online dilation measurement of test specimens, examined in the high-temperature environment of material testing reactors. The probe has to be canned with metallic clad to protect it from the corrosive sodium-potassium\u0000 coolant medium present in the material testing reactor. Electromagnetic modelling of the probe was carried out to compute the distribution of time-dependent eddy current density in the vicinity of the probe. It is understood that the slope of the pulsed eddy current signals in the specific\u0000 time zone where the lift-off point of intersection occurs show a good correlation to the distance between the face of the probe and the test specimen. This paper discusses the experimental study of employing different measurement methodologies at room temperature with the canned pulsed eddy\u0000 current probe to evaluate its feasibility for dilation measurement. It is observed that a phenomenon of a divergence of signals, akin to the lift-off point of intersection, occurs just after the transient part of the pulsed eddy current signals. Slope analysis of these diverging pulsed eddy\u0000 current signals was carried out for characterisation of the probe in the air medium as well as the metallic medium present in the gap between the probe face and the test specimen.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125081724","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}
Pub Date : 2023-02-01DOI: 10.1784/insi.2023.65.2.72
Dongdong Wen, Songlei Wang, Zhigang Xue, Anbin Hu
Pulsed eddy current (PEC) technology serves as a popular testing method for multilayer structures and is widely used in coating and substrate thickness measurement. However, in the coating thickness measurement of a multilayer structure, the substrate thickness effect is a disturbance that needs to be eliminated urgently. In order to reduce the substrate thickness effect, in this paper a twice difference normalisation method is proposed to obtain a signal feature independently of the substrate thickness effect for measuring the coating thickness of a multilayer ferromagnetic structure. The simulation and experimental results demonstrate that a fitting line of the peak value of twice difference normalisation signals can be obtained by using the twice difference normalisation method when only the coating thickness changes. The normalisation fitting line is immune to the substrate thickness effect and can be used to measure the coating thickness of a multilayer ferromagnetic structure, which means that the twice difference normalisation method is feasible for high-precision evaluation of the coating thickness of a multilayer ferromagnetic structure when the substrate thickness changes. This study will improve the coating thickness measurement accuracy of multilayer ferromagnetic structures when the substrate thickness changes in the PEC testing.
{"title":"Coating Thickness Measurement Of Multilayer Ferromagnetic Samples Based On Pulsed Eddy Current Testing Technology","authors":"Dongdong Wen, Songlei Wang, Zhigang Xue, Anbin Hu","doi":"10.1784/insi.2023.65.2.72","DOIUrl":"https://doi.org/10.1784/insi.2023.65.2.72","url":null,"abstract":"Pulsed eddy current (PEC) technology serves as a popular testing method for multilayer structures and is widely used in coating and substrate thickness measurement. However, in the coating thickness measurement of a multilayer structure, the substrate thickness effect is a disturbance\u0000 that needs to be eliminated urgently. In order to reduce the substrate thickness effect, in this paper a twice difference normalisation method is proposed to obtain a signal feature independently of the substrate thickness effect for measuring the coating thickness of a multilayer ferromagnetic\u0000 structure. The simulation and experimental results demonstrate that a fitting line of the peak value of twice difference normalisation signals can be obtained by using the twice difference normalisation method when only the coating thickness changes. The normalisation fitting line is immune\u0000 to the substrate thickness effect and can be used to measure the coating thickness of a multilayer ferromagnetic structure, which means that the twice difference normalisation method is feasible for high-precision evaluation of the coating thickness of a multilayer ferromagnetic structure\u0000 when the substrate thickness changes. This study will improve the coating thickness measurement accuracy of multilayer ferromagnetic structures when the substrate thickness changes in the PEC testing.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127450118","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}
Pub Date : 2023-01-01DOI: 10.1784/insi.2023.65.1.43
Kaixuan Tong, Geng Zhang, Huade Huang, Aisong Qin, H. Mao
It is significant to predict the vibration trend of a hydropower generator unit (HGU) based on historical data for the stable operation of units and the maintenance of power system safety. Therefore, a novel combined model based on ensemble empirical mode decomposition (EEMD), sample entropy (SE), a Gaussian process regression (GPR) model and an autoregressive moving average model (ARMA) is proposed. Firstly, according to the non-linear and non-stationary characteristics of the vibration series, the vibration time series is decomposed into a single component and relatively stable subsequences using EEMD. Then, the SE algorithm reconstructs the subsequences with similar complexity to reduce the number of prediction sequences. Moreover, after judging the stationarity test of the reconstructed sequence, the GPR model and ARMA model are used to predict the non-stationary and stable subsequences, respectively. Finally, the predicted values of each subsequence are synthesised. Furthermore, five related methods are employed to evaluate the effectiveness of the proposed approach. The results illustrate that: (1) compared with EEMD only, EEMD combined with SE can improve prediction accuracy; (2) the reconstruction strategy based on SE can reduce the influence of false modes and improve the prediction accuracy; and (3) the prediction effect of the hybrid prediction model, which reduces the influence of accidental factors, is better than that of a single model in predicting the vibration sequence of an HGU.
{"title":"A novel combined model for vibration trend prediction of a hydropower generator unit","authors":"Kaixuan Tong, Geng Zhang, Huade Huang, Aisong Qin, H. Mao","doi":"10.1784/insi.2023.65.1.43","DOIUrl":"https://doi.org/10.1784/insi.2023.65.1.43","url":null,"abstract":"It is significant to predict the vibration trend of a hydropower generator unit (HGU) based on historical data for the stable operation of units and the maintenance of power system safety. Therefore, a novel combined model based on ensemble empirical mode decomposition (EEMD), sample\u0000 entropy (SE), a Gaussian process regression (GPR) model and an autoregressive moving average model (ARMA) is proposed. Firstly, according to the non-linear and non-stationary characteristics of the vibration series, the vibration time series is decomposed into a single component and relatively\u0000 stable subsequences using EEMD. Then, the SE algorithm reconstructs the subsequences with similar complexity to reduce the number of prediction sequences. Moreover, after judging the stationarity test of the reconstructed sequence, the GPR model and ARMA model are used to predict the non-stationary\u0000 and stable subsequences, respectively. Finally, the predicted values of each subsequence are synthesised. Furthermore, five related methods are employed to evaluate the effectiveness of the proposed approach. The results illustrate that: (1) compared with EEMD only, EEMD combined with SE can\u0000 improve prediction accuracy; (2) the reconstruction strategy based on SE can reduce the influence of false modes and improve the prediction accuracy; and (3) the prediction effect of the hybrid prediction model, which reduces the influence of accidental factors, is better than that of a single\u0000 model in predicting the vibration sequence of an HGU.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115133715","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}
Pub Date : 2023-01-01DOI: 10.1784/insi.2023.65.1.13
M. Blankschän, D. Kanzler, R. Liebich
To ensure the reliable assessment of components by visual inspection, current standards overemphasise the focus on illuminance as being the only factor of influence. Human factors or an adaptation of viewing conditions to different inspection tasks are not sufficiently considered. In previous investigations, a significant influence of illuminance, testing forged parts with highly reflective surfaces at three different illuminance levels (200 lx, 350 lx and 500 lx), could not be proven. Instead, a more complex system of different influencing factors was indicated. In this work, groups with different human characteristics were compared to investigate the correlations in indication detectability during visual inspection. A questionnaire was used to record various subjectively perceived factors (dissatisfaction, general and lighting scenariospecific disturbance) in the detection of crack-like indications, under three different lighting scenarios. From the factors studied, age, general and specific experience of the participants were found to be the most relevant. General experience in the field of visual inspection, measured in years and frequency of inspections performed in practice, was shown not to have a decisive influence. On the other hand, the influence of specific experience with the test specimen, or comparable components, and the age of the participants seem to have an influence. With increasing age, the probability of detection seems to decrease, while it seems to increase with rising component-specific experience. In the interplay of the factors investigated, specific experience can be influenced most effectively and could thus be increased by intensified and specific practical training. This could have a positive effect on the reliability of visual inspection.
{"title":"Visual testing: the influence of selected human factors on probability of detection","authors":"M. Blankschän, D. Kanzler, R. Liebich","doi":"10.1784/insi.2023.65.1.13","DOIUrl":"https://doi.org/10.1784/insi.2023.65.1.13","url":null,"abstract":"To ensure the reliable assessment of components by visual inspection, current standards overemphasise the focus on illuminance as being the only factor of influence. Human factors or an adaptation of viewing conditions to different inspection tasks are not sufficiently considered. In\u0000 previous investigations, a significant influence of illuminance, testing forged parts with highly reflective surfaces at three different illuminance levels (200 lx, 350 lx and 500 lx), could not be proven. Instead, a more complex system of different influencing factors was indicated. In this\u0000 work, groups with different human characteristics were compared to investigate the correlations in indication detectability during visual inspection. A questionnaire was used to record various subjectively perceived factors (dissatisfaction, general and lighting scenariospecific disturbance)\u0000 in the detection of crack-like indications, under three different lighting scenarios. From the factors studied, age, general and specific experience of the participants were found to be the most relevant. General experience in the field of visual inspection, measured in years and frequency\u0000 of inspections performed in practice, was shown not to have a decisive influence. On the other hand, the influence of specific experience with the test specimen, or comparable components, and the age of the participants seem to have an influence. With increasing age, the probability of detection\u0000 seems to decrease, while it seems to increase with rising component-specific experience. In the interplay of the factors investigated, specific experience can be influenced most effectively and could thus be increased by intensified and specific practical training. This could have a positive\u0000 effect on the reliability of visual inspection.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134327317","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}
Pub Date : 2023-01-01DOI: 10.1784/insi.2023.65.1.19
Jiawei Zhang, J. Jiao, Xiang Gao, Bin Wu, C. He, Changhua Chen
Ultrasonic testing of coarse-grained materials is strongly influenced by high-level scattering noise. In addition, the signalto-noise ratio (SNR) and spatial resolution of imaging by the traditional total focusing method (TFM) are relatively low. In this study, we focused on the reconstruction of high-resolution ultrasonic images from full matrix capture datasets. A weighted TFM image by combining the inverse problem-based method and traditional TFM is proposed to detect defects in coarse-grained steel. The proposed method was used to image defects with the full matrix data obtained through simulations and experiments. The simulation and experimental results show that the weighted total focusing method can significantly improve the SNR of ultrasonic imaging in coarse-grained steel and, moreover, it can improve the resolution of imaging and distinguish adjacent defects with a centre distance less than the Rayleigh criteria.
{"title":"High-resolution ultrasonic imaging of the defects in coarse-grained steel by a weighted total focusing method","authors":"Jiawei Zhang, J. Jiao, Xiang Gao, Bin Wu, C. He, Changhua Chen","doi":"10.1784/insi.2023.65.1.19","DOIUrl":"https://doi.org/10.1784/insi.2023.65.1.19","url":null,"abstract":"Ultrasonic testing of coarse-grained materials is strongly influenced by high-level scattering noise. In addition, the signalto-noise ratio (SNR) and spatial resolution of imaging by the traditional total focusing method (TFM) are relatively low. In this study, we focused on the reconstruction\u0000 of high-resolution ultrasonic images from full matrix capture datasets. A weighted TFM image by combining the inverse problem-based method and traditional TFM is proposed to detect defects in coarse-grained steel. The proposed method was used to image defects with the full matrix data obtained\u0000 through simulations and experiments. The simulation and experimental results show that the weighted total focusing method can significantly improve the SNR of ultrasonic imaging in coarse-grained steel and, moreover, it can improve the resolution of imaging and distinguish adjacent defects\u0000 with a centre distance less than the Rayleigh criteria.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134325103","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}
Pub Date : 2023-01-01DOI: 10.1784/insi.2023.65.1.28
Zijian Wang, Binkai Shi, Chen Fang
Guided waves are suitable for non-destructive testing and structural health monitoring of tube-like structures. However, the dispersion phenomenon impedes the application of guided waves. Although the finite element (FE) method can simulate the guided wave propagation and help to study the dispersion phenomenon, boundary reflections can contaminate the wave field of interest and impede the FE simulation. In this paper, damping boundaries are developed as a set of FE frames with gradually increasing damping coefficients to alleviate boundary reflections. The wave signals simulated through the FE model with the damping boundaries only contain the waves from the transmitter to the receiver, without the interferences of the boundary reflections. The energy distribution on the frequency-velocity spectrum of the simulated signals agrees well with the analytical dispersion curves, indicating that the boundary reflections are effectively alleviated. The analytical solution of the guided wave equation and the FE modelling method presented in this paper can facilitate both research and applications of guided waves for tube-like structures.
{"title":"Characterisation of guided wave dispersion in isotropic tubes based on damping finite element boundaries","authors":"Zijian Wang, Binkai Shi, Chen Fang","doi":"10.1784/insi.2023.65.1.28","DOIUrl":"https://doi.org/10.1784/insi.2023.65.1.28","url":null,"abstract":"Guided waves are suitable for non-destructive testing and structural health monitoring of tube-like structures. However, the dispersion phenomenon impedes the application of guided waves. Although the finite element (FE) method can simulate the guided wave propagation and help to study\u0000 the dispersion phenomenon, boundary reflections can contaminate the wave field of interest and impede the FE simulation. In this paper, damping boundaries are developed as a set of FE frames with gradually increasing damping coefficients to alleviate boundary reflections. The wave signals\u0000 simulated through the FE model with the damping boundaries only contain the waves from the transmitter to the receiver, without the interferences of the boundary reflections. The energy distribution on the frequency-velocity spectrum of the simulated signals agrees well with the analytical\u0000 dispersion curves, indicating that the boundary reflections are effectively alleviated. The analytical solution of the guided wave equation and the FE modelling method presented in this paper can facilitate both research and applications of guided waves for tube-like structures.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115586949","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}
Pub Date : 2023-01-01DOI: 10.1784/insi.2023.65.1.36
Liu Zhengjie, Mu Weilei, Ning Hao, Wu Mengmeng, Liu Guijie
Pressure vessel leakages cannot initially be visited directly and will gradually cause deterioration, which can result in catastrophic damage. Acoustic emission (AE) signals generated by leakage have the potential of being used for online monitoring. Unfortunately, AE signals have the characteristics of being non-stationary, wide-band and with strong noise interference, which causes the monitoring results to have low reliability. To address the poor robustness of traditional time-domain and time-frequency domain-based monitoring methods, an online monitoring method based on adaptive singular value decomposition (ASVD) is proposed in this paper. Firstly, singular value decomposition (SVD) is used to divide the signal space into a signal subspace and a noise subspace. Experiments indicate that SVD can distinguish leakages under conditions of different pressures and variable temperature, which means that SVD is sensitive to changes in signal. Subsequently, update iteration-based ASVD algorithms are proposed for long-term online health monitoring and ASVD is shown to be successful in distinguishing the different statuses of intact, leakage and repaired. To improve the robustness of ASVD, a novel energy indicator is proposed, which can identify the status change more effectively. With the proposed methodology, an online monitoring application for pressure vessel leakage detection is expected to be achievable.
{"title":"Pressure vessel leakage detection method based on online acoustic emission signals","authors":"Liu Zhengjie, Mu Weilei, Ning Hao, Wu Mengmeng, Liu Guijie","doi":"10.1784/insi.2023.65.1.36","DOIUrl":"https://doi.org/10.1784/insi.2023.65.1.36","url":null,"abstract":"Pressure vessel leakages cannot initially be visited directly and will gradually cause deterioration, which can result in catastrophic damage. Acoustic emission (AE) signals generated by leakage have the potential of being used for online monitoring. Unfortunately, AE signals have the\u0000 characteristics of being non-stationary, wide-band and with strong noise interference, which causes the monitoring results to have low reliability. To address the poor robustness of traditional time-domain and time-frequency domain-based monitoring methods, an online monitoring method based\u0000 on adaptive singular value decomposition (ASVD) is proposed in this paper. Firstly, singular value decomposition (SVD) is used to divide the signal space into a signal subspace and a noise subspace. Experiments indicate that SVD can distinguish leakages under conditions of different pressures\u0000 and variable temperature, which means that SVD is sensitive to changes in signal. Subsequently, update iteration-based ASVD algorithms are proposed for long-term online health monitoring and ASVD is shown to be successful in distinguishing the different statuses of intact, leakage and repaired.\u0000 To improve the robustness of ASVD, a novel energy indicator is proposed, which can identify the status change more effectively. With the proposed methodology, an online monitoring application for pressure vessel leakage detection is expected to be achievable.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124988860","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}
Pub Date : 2022-12-01DOI: 10.1784/insi.2022.64.12.709
Zhihui Hu, Zhihai Xu, Gongxian Wang, L. Xiang
In order to accurately extract the sensitive features representing the type and severity of gear faults through the vibration signal, a gear fault diagnosis method using moving multi-scale reconstruction-based interactive energy entropy (MMS-IEE) is proposed. The gear vibration signal is reconstructed using a multi-scale mean at different scales and adjacent data points are used to form a sliding window, which makes the information extraction from the vibration signals sufficient. The energy distributions of the original signal and the reconstructed signal under different scale channels are calculated. Compared with the traditional energy entropy (EE) method, the feature vector obtained by the interactive superposition method can more accurately represent the energy mutation of the time-series caused by the fault. Experimental results show that the proposed MMS-IEE method has a strong fault feature extraction ability and high gear fault diagnostic accuracy under different speeds and working conditions.
{"title":"A feature extraction method based on moving multi-scale reconstruction and interactive energy entropy for gear fault diagnosis","authors":"Zhihui Hu, Zhihai Xu, Gongxian Wang, L. Xiang","doi":"10.1784/insi.2022.64.12.709","DOIUrl":"https://doi.org/10.1784/insi.2022.64.12.709","url":null,"abstract":"In order to accurately extract the sensitive features representing the type and severity of gear faults through the vibration signal, a gear fault diagnosis method using moving multi-scale reconstruction-based interactive energy entropy (MMS-IEE) is proposed. The gear vibration signal\u0000 is reconstructed using a multi-scale mean at different scales and adjacent data points are used to form a sliding window, which makes the information extraction from the vibration signals sufficient. The energy distributions of the original signal and the reconstructed signal under different\u0000 scale channels are calculated. Compared with the traditional energy entropy (EE) method, the feature vector obtained by the interactive superposition method can more accurately represent the energy mutation of the time-series caused by the fault. Experimental results show that the proposed\u0000 MMS-IEE method has a strong fault feature extraction ability and high gear fault diagnostic accuracy under different speeds and working conditions.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116913759","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}
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