Pub Date : 2024-06-01DOI: 10.1784/insi.2024.66.6.361
Weilin Shao, Xiaohu Wu, Runkun Lu, Jinzhong Chen, Yilai Ma
Alternating current magnetic flux leakage (ACMFL) technology has been used for sizing pipeline cracks. However, traditional ACMFL methods are limited in sensitively identifying arbitrary angles of pipeline cracks due to the complexity of crack morphology. To solve the problem, a multi-frequency balanced electromagnetic field measurement (MF-BEFM) method is proposed. First, the distortion of the magnetic field and eddy current field caused by crack defects was studied. Then, the effects of the excitation frequency on balanced electromagnetic field measurement (BEFM) signals were considered to select optimal parameters. Next, the relationship between the BEFM signal and crack angle was studied. Finally, the MF-BEFM experiment was conducted to prove the superiority of the proposed method. The results show that the BEFM method cannot only overcome the deficiency of directional detection of ACMFL methods but it can also achieve high detection sensitivity for identifying arbitrary angles of cracks on the pipeline surface.
{"title":"A multi-frequency balanced electromagnetic field measurement for arbitrary angles of pipeline cracks with high sensitivity","authors":"Weilin Shao, Xiaohu Wu, Runkun Lu, Jinzhong Chen, Yilai Ma","doi":"10.1784/insi.2024.66.6.361","DOIUrl":"https://doi.org/10.1784/insi.2024.66.6.361","url":null,"abstract":"Alternating current magnetic flux leakage (ACMFL) technology has been used for sizing pipeline cracks. However, traditional ACMFL methods are limited in sensitively identifying arbitrary angles of pipeline cracks due to the complexity of crack morphology. To solve the problem, a multi-frequency\u0000 balanced electromagnetic field measurement (MF-BEFM) method is proposed. First, the distortion of the magnetic field and eddy current field caused by crack defects was studied. Then, the effects of the excitation frequency on balanced electromagnetic field measurement (BEFM) signals were considered\u0000 to select optimal parameters. Next, the relationship between the BEFM signal and crack angle was studied. Finally, the MF-BEFM experiment was conducted to prove the superiority of the proposed method. The results show that the BEFM method cannot only overcome the deficiency of directional\u0000 detection of ACMFL methods but it can also achieve high detection sensitivity for identifying arbitrary angles of cracks on the pipeline surface.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"36 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141406901","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 : 2024-06-01DOI: 10.1784/insi.2024.66.6.346
Hongwei Hu, Jinhan Huang, Duo Lyu, Wenzheng Liu, Xiaoqiang Xu
In order to address the problem of poor imaging detection of internal defects caused by the multi-layer curved surface structure and strongly attenuating material of composite insulators, the total focusing method (TFM) with two-layer curved surface correction was investigated. Virtual source (VS) technology was introduced to improve the transmitted energy of the single-element transmission, combined with the delay multiply and sum (DMAS) algorithm to improve the signal-to-noise ratio (SNR) and ultrasonic imaging detection of defects of different sizes of side-drilled holes (SDHs) in the sheath layer and core rod layer. An examination of the delamination defects at the core rod layer of the composite insulators was also carried out. The results show that the double-layer surface correction method is able to calibrate the propagation time of the ultrasonic wave to accurately localise the defects. Moreover, the VS technique is able to effectively detect SDHs and delamination defects in the core rod layer that cannot be detected using the conventional TFM. After incorporating the DMAS technique, the average SNRs of the TFM and virtual source total focusing method (VSTFM) are improved by 12.75 dB and 13.77 dB, respectively. This shows that the DMAS technique can significantly improve the SNR of detection.
{"title":"Ultrasonic total focusing method for internal defects in composite insulators","authors":"Hongwei Hu, Jinhan Huang, Duo Lyu, Wenzheng Liu, Xiaoqiang Xu","doi":"10.1784/insi.2024.66.6.346","DOIUrl":"https://doi.org/10.1784/insi.2024.66.6.346","url":null,"abstract":"In order to address the problem of poor imaging detection of internal defects caused by the multi-layer curved surface structure and strongly attenuating material of composite insulators, the total focusing method (TFM) with two-layer curved surface correction was investigated. Virtual\u0000 source (VS) technology was introduced to improve the transmitted energy of the single-element transmission, combined with the delay multiply and sum (DMAS) algorithm to improve the signal-to-noise ratio (SNR) and ultrasonic imaging detection of defects of different sizes of side-drilled holes\u0000 (SDHs) in the sheath layer and core rod layer. An examination of the delamination defects at the core rod layer of the composite insulators was also carried out. The results show that the double-layer surface correction method is able to calibrate the propagation time of the ultrasonic wave\u0000 to accurately localise the defects. Moreover, the VS technique is able to effectively detect SDHs and delamination defects in the core rod layer that cannot be detected using the conventional TFM. After incorporating the DMAS technique, the average SNRs of the TFM and virtual source total\u0000 focusing method (VSTFM) are improved by 12.75 dB and 13.77 dB, respectively. This shows that the DMAS technique can significantly improve the SNR of detection.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"39 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141410126","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 : 2024-06-01DOI: 10.1784/insi.2024.66.6.338
A. B. Wooldridge
The 1970s and 1980s marked a significant period of advancement in non-destructive testing (NDT) techniques, particularly ultrasonic testing (UT) and eddy current testing (ET). Some of the strongest drivers for this were the needs of the civil nuclear power industry. This paper explores the historical developments during this era, highlighting the challenges faced by the nuclear industry and the subsequent innovations that emerged. The paper concentrates on technical achievements of the Central Electricity Generating Board (CEGB) and the UK Atomic Energy Authority (UKAEA) in the UK, but also mentions the significant contributions from the improved regulatory framework, validation and certification, and collaborative efforts between industry and research institutions. This paper was written to mark the 60th Annual Conference of the British Institute of Non-Destructive Testing (BINDT) and mentions the role of the Institute???s Past Presidents where appropriate.
{"title":"Developments in ultrasonic and eddy current testing in the 1970s and 1980s with emphasis on the requirements of the UK nuclear power industry","authors":"A. B. Wooldridge","doi":"10.1784/insi.2024.66.6.338","DOIUrl":"https://doi.org/10.1784/insi.2024.66.6.338","url":null,"abstract":"The 1970s and 1980s marked a significant period of advancement in non-destructive testing (NDT) techniques, particularly ultrasonic testing (UT) and eddy current testing (ET). Some of the strongest drivers for this were the needs of the civil nuclear power industry. This paper explores\u0000 the historical developments during this era, highlighting the challenges faced by the nuclear industry and the subsequent innovations that emerged. The paper concentrates on technical achievements of the Central Electricity Generating Board (CEGB) and the UK Atomic Energy Authority (UKAEA)\u0000 in the UK, but also mentions the significant contributions from the improved regulatory framework, validation and certification, and collaborative efforts between industry and research institutions. This paper was written to mark the 60th Annual Conference of the British Institute of Non-Destructive\u0000 Testing (BINDT) and mentions the role of the Institute???s Past Presidents where appropriate.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"7 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141415871","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 : 2024-06-01DOI: 10.1784/insi.2024.66.6.368
Mulpuru Gopi, C. Ranga
In this present paper, a novel multi-criterion-based fuzzy logic (FL) expert system using different membership functions (MFs) is proposed to determine the overall health index (OHI) of electrical transformers. 30 oil samples from different field transformers installed at various locations in Himachal Pradesh, India, are collected for the analysis and various diagnostic tests are conducted on each of the oil samples. The diagnostic testing data are utilised for the proposed methodology. Initially, the diagnostic data are normalised using the well-known multi-criterion analysis (MCA) method. The normalised input data are grouped into three grades, ie total dissolved combustible gases (TDCGs), oil insulation and paper insulation. Furthermore, a fuzzy logic model is designed based on the three different grades. Output health indices are determined for each of the samples. Comparison and validation of the proposed model is conducted with the expert model, as well as the preknown health status of 150 transformers installed in the Gulf region. The expert model is designed with a trapezoidal membership function, whereas the proposed model considers the popular Gauss-2. From the comparison, it is observed that the accuracy of the proposed model is 98%, while the accuracy of the expert model is 96%, making the proposed model more accurate. Moreover, a plan of action for proper maintenance is also recommended for each transformer, based on the evaluated health index. The proper maintenance of transformers leads to improvements in their service life. The present work is beneficial not only for transformer utilities but also for customers. The model is straightforward to understand, even for inexperienced staff and maintenance managers.
{"title":"Multi-criterion analysis-based artificial intelligence system for condition monitoring of electrical transformers","authors":"Mulpuru Gopi, C. Ranga","doi":"10.1784/insi.2024.66.6.368","DOIUrl":"https://doi.org/10.1784/insi.2024.66.6.368","url":null,"abstract":"In this present paper, a novel multi-criterion-based fuzzy logic (FL) expert system using different membership functions (MFs) is proposed to determine the overall health index (OHI) of electrical transformers. 30 oil samples from different field transformers installed at various locations\u0000 in Himachal Pradesh, India, are collected for the analysis and various diagnostic tests are conducted on each of the oil samples. The diagnostic testing data are utilised for the proposed methodology. Initially, the diagnostic data are normalised using the well-known multi-criterion analysis\u0000 (MCA) method. The normalised input data are grouped into three grades, ie total dissolved combustible gases (TDCGs), oil insulation and paper insulation. Furthermore, a fuzzy logic model is designed based on the three different grades. Output health indices are determined for each of the samples.\u0000 Comparison and validation of the proposed model is conducted with the expert model, as well as the preknown health status of 150 transformers installed in the Gulf region. The expert model is designed with a trapezoidal membership function, whereas the proposed model considers the popular\u0000 Gauss-2. From the comparison, it is observed that the accuracy of the proposed model is 98%, while the accuracy of the expert model is 96%, making the proposed model more accurate. Moreover, a plan of action for proper maintenance is also recommended for each transformer, based\u0000 on the evaluated health index. The proper maintenance of transformers leads to improvements in their service life. The present work is beneficial not only for transformer utilities but also for customers. The model is straightforward to understand, even for inexperienced staff and maintenance\u0000 managers.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141398748","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 : 2024-06-01DOI: 10.1784/insi.2024.66.6.353
Mo He, Zhiyong Zhou, Lin Qin, Hao Yong, Chao Chen
Magnetic flux leakage (MFL) is one of the most commonly used non-destructive testing technologies for defect detection of oil and gas pipelines. Analysing the MFL signals of different defects and thus identifying the types and sizes of pipeline defects are the key and difficult points, obtaining wide attention in both academic and engineering domains. Most of the past research has focused on the MFL signals of single defects, neglecting the interference caused by adjacent defects, possibly leading to errors. As a result, this study develops a finite element method (FEM) model based on Maxwell theory for the MFL signal of adjacent defects and analyses the signal characteristics, considering both inner and outer defects. The interference distances caused by inner and outer defects are analysed and the shape and size of the defects are also considered to identify defects in multiple adjacent defects. The model results show that the interference caused by adjacent defects manifests the superposition of the leakage magnetic field in axial and radial components. The interference weakens with increasing distance between adjacent defects. To quantify the interference caused by different defects, a concept of 'interference distance' is developed using the change rate of the peak value of MFL signals. The influence of different factors on the interference distance is explored by analysing the MFL signal under different factors. Therefore, this study can support the identification of adjacent defects on steel pipelines using MFL technology, reducing the errors caused by adjacent defects.
{"title":"MFL detection of adjacent pipeline defects: a finite element simulation of signal characteristics","authors":"Mo He, Zhiyong Zhou, Lin Qin, Hao Yong, Chao Chen","doi":"10.1784/insi.2024.66.6.353","DOIUrl":"https://doi.org/10.1784/insi.2024.66.6.353","url":null,"abstract":"Magnetic flux leakage (MFL) is one of the most commonly used non-destructive testing technologies for defect detection of oil and gas pipelines. Analysing the MFL signals of different defects and thus identifying the types and sizes of pipeline defects are the key and difficult points,\u0000 obtaining wide attention in both academic and engineering domains. Most of the past research has focused on the MFL signals of single defects, neglecting the interference caused by adjacent defects, possibly leading to errors. As a result, this study develops a finite element method (FEM)\u0000 model based on Maxwell theory for the MFL signal of adjacent defects and analyses the signal characteristics, considering both inner and outer defects. The interference distances caused by inner and outer defects are analysed and the shape and size of the defects are also considered to identify\u0000 defects in multiple adjacent defects. The model results show that the interference caused by adjacent defects manifests the superposition of the leakage magnetic field in axial and radial components. The interference weakens with increasing distance between adjacent defects. To quantify the\u0000 interference caused by different defects, a concept of 'interference distance' is developed using the change rate of the peak value of MFL signals. The influence of different factors on the interference distance is explored by analysing the MFL signal under different factors. Therefore,\u0000 this study can support the identification of adjacent defects on steel pipelines using MFL technology, reducing the errors caused by adjacent defects.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141399189","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 : 2024-02-01DOI: 10.1784/insi.2024.66.2.82
Xianguo Li, Dongdong Wu, Yi Liu, Ying Chen
Existing idler fault diagnosis methods have problems in failing to fully obtain global context information and providing poor diagnostic accuracy. To address these problems, this paper investigates a new method for diagnosing faults in belt conveyor idlers, based on analysis of their acoustic signals. The method is also applied to existing databases of bearing fault data. Firstly, an eight-element microphone array sound signal collector is designed to suppress environmental noise and raise the signal-to-noise ratio of the idler sound signal. Secondly, a multi-scale feature fusion (MSFF) module is constructed to learn complementary information between features at different scales. Then, a residual mask convolutional attention (MCA) module is designed to raise the modelling capability of local features and global contextual information. Finally, the structure of the ResNet-18 network is optimised to improve model fitting performance. Experimental results on self-made and public datasets show that the suggested method outperforms other comparative methods, achieving real-time accurate detection and classification of belt conveyor idler faults and typical bearing faults.
{"title":"Belt conveyor idler fault diagnosis method based on multi-scale feature fusion and residual mask convolution attention","authors":"Xianguo Li, Dongdong Wu, Yi Liu, Ying Chen","doi":"10.1784/insi.2024.66.2.82","DOIUrl":"https://doi.org/10.1784/insi.2024.66.2.82","url":null,"abstract":"Existing idler fault diagnosis methods have problems in failing to fully obtain global context information and providing poor diagnostic accuracy. To address these problems, this paper investigates a new method for diagnosing faults in belt conveyor idlers, based on analysis of their\u0000 acoustic signals. The method is also applied to existing databases of bearing fault data. Firstly, an eight-element microphone array sound signal collector is designed to suppress environmental noise and raise the signal-to-noise ratio of the idler sound signal. Secondly, a multi-scale feature\u0000 fusion (MSFF) module is constructed to learn complementary information between features at different scales. Then, a residual mask convolutional attention (MCA) module is designed to raise the modelling capability of local features and global contextual information. Finally, the structure\u0000 of the ResNet-18 network is optimised to improve model fitting performance. Experimental results on self-made and public datasets show that the suggested method outperforms other comparative methods, achieving real-time accurate detection and classification of belt conveyor idler faults and\u0000 typical bearing faults.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"502 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139833097","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 : 2024-02-01DOI: 10.1784/insi.2024.66.2.94
Yuanling Chen, Hao Shi, Yaguang Jin, Yuan Liu
Bearing fault diagnosis plays an important part in preventing rotating equipment faults, especially in the field of ultra-low-speed bearing fault diagnosis. Due to their low fault frequency and insignificant fault characteristics, it is difficult to realise the fault diagnosis of ultra-low-speed bearings using traditional methods; therefore, based on acoustic emission (AE) signals, this study proposes an ultra-low-speed bearing recognition model with EfficientNet as the backbone feature extraction network and successfully achieves bearing fault diagnosis under small-sample variable working conditions combined with transfer learning. The coordinate attention (CA) mechanism is introduced into the EfficientNet backbone feature extraction network to improve the ability of the model to extract detailed position information. The AdamW optimisation algorithm is introduced to improve the generalisation ability of the model. Combined with the idea of transfer learning, the data under different working conditions are trained and tested to form a high-performance and lightweight small-sample variable condition bearing recognition model called EfficientNet-CA-AdamW (EfficientNet-CAA). Comparison experiments show that the EfficientNet-CAA model proposed in this study has an accuracy of 99.81% for ultra-low-speed bearing recognition when the training samples are sufficient. Furthermore, the recognition accuracy is smoother and the loss function is significantly lower compared with convolutional neural network (CNN) models such as AlexNet, VGG-16, ResNet-34, ShuffleNet-V2 and EfficientNet-B0. In small-sample variable condition fault recognition, it has more powerful advantages compared with the other models. The recognition accuracy under variable conditions can reach more than 98%, which is significantly higher than that of the other models, and effectively improves the bearing fault recognition accuracy under small-sample variable conditions. In this study, the CA mechanism and the AdamW optimisation algorithm are introduced to lessen the difficulty of extracting detailed features and address the lack of generalisation ability of the EfficientNet model, which provides an idea for the application of the deep learning model to small-sample bearing fault diagnosis under variable working conditions.
{"title":"Study on fault diagnosis of ultra-low-speed bearings under variable working conditions based on improved EfficientNet network","authors":"Yuanling Chen, Hao Shi, Yaguang Jin, Yuan Liu","doi":"10.1784/insi.2024.66.2.94","DOIUrl":"https://doi.org/10.1784/insi.2024.66.2.94","url":null,"abstract":"Bearing fault diagnosis plays an important part in preventing rotating equipment faults, especially in the field of ultra-low-speed bearing fault diagnosis. Due to their low fault frequency and insignificant fault characteristics, it is difficult to realise the fault diagnosis of ultra-low-speed\u0000 bearings using traditional methods; therefore, based on acoustic emission (AE) signals, this study proposes an ultra-low-speed bearing recognition model with EfficientNet as the backbone feature extraction network and successfully achieves bearing fault diagnosis under small-sample variable\u0000 working conditions combined with transfer learning. The coordinate attention (CA) mechanism is introduced into the EfficientNet backbone feature extraction network to improve the ability of the model to extract detailed position information. The AdamW optimisation algorithm is introduced to\u0000 improve the generalisation ability of the model. Combined with the idea of transfer learning, the data under different working conditions are trained and tested to form a high-performance and lightweight small-sample variable condition bearing recognition model called EfficientNet-CA-AdamW\u0000 (EfficientNet-CAA). Comparison experiments show that the EfficientNet-CAA model proposed in this study has an accuracy of 99.81% for ultra-low-speed bearing recognition when the training samples are sufficient. Furthermore, the recognition accuracy is smoother and the loss function is significantly\u0000 lower compared with convolutional neural network (CNN) models such as AlexNet, VGG-16, ResNet-34, ShuffleNet-V2 and EfficientNet-B0. In small-sample variable condition fault recognition, it has more powerful advantages compared with the other models. The recognition accuracy under variable\u0000 conditions can reach more than 98%, which is significantly higher than that of the other models, and effectively improves the bearing fault recognition accuracy under small-sample variable conditions. In this study, the CA mechanism and the AdamW optimisation algorithm are introduced to lessen\u0000 the difficulty of extracting detailed features and address the lack of generalisation ability of the EfficientNet model, which provides an idea for the application of the deep learning model to small-sample bearing fault diagnosis under variable working conditions.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"80 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139823157","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 : 2024-02-01DOI: 10.1784/insi.2024.66.2.74
Xianyou Zhong, Liu He, Gang Wan, Yang Zhao
Bearing fault diagnosis helps to ensure the safe operation of electromechanical equipment and reduce unnecessary losses due to downtime. The interference of noise in the signal poses a challenge in the effective identification of rolling bearing faults. To address the above problems, this paper proposes a rolling bearing fault diagnosis (RBFD) method based on generalised dispersive mode decomposition (GDMD) and an accugram. Firstly, the bearing signal is decomposed using GDMD and the optimal number of decomposition modes is chosen using a new index based on the correlation coefficient and accuracy. According to the number of determined decomposition modes, the fault signal is reconstructed. Then, the centre frequency and bandwidth of the resonant frequency are determined using an accugram. Finally, the fault signal is filtered and analysed using a square envelope spectrum to achieve rolling bearing fault diagnosis. Experimental signal analysis verifies the effectiveness and feasibility of the method. The method is applied to the early fault diagnosis of rolling bearings and compared with kurtogram and accugram results. The results show that the approach can not only effectively avoid the interference of external impacts but it can also correctly recognise the fault characteristic frequency band.
{"title":"Fault diagnosis for rolling bearings based on generalised dispersive mode decomposition and accugram","authors":"Xianyou Zhong, Liu He, Gang Wan, Yang Zhao","doi":"10.1784/insi.2024.66.2.74","DOIUrl":"https://doi.org/10.1784/insi.2024.66.2.74","url":null,"abstract":"Bearing fault diagnosis helps to ensure the safe operation of electromechanical equipment and reduce unnecessary losses due to downtime. The interference of noise in the signal poses a challenge in the effective identification of rolling bearing faults. To address the above problems,\u0000 this paper proposes a rolling bearing fault diagnosis (RBFD) method based on generalised dispersive mode decomposition (GDMD) and an accugram. Firstly, the bearing signal is decomposed using GDMD and the optimal number of decomposition modes is chosen using a new index based on the correlation\u0000 coefficient and accuracy. According to the number of determined decomposition modes, the fault signal is reconstructed. Then, the centre frequency and bandwidth of the resonant frequency are determined using an accugram. Finally, the fault signal is filtered and analysed using a square envelope\u0000 spectrum to achieve rolling bearing fault diagnosis. Experimental signal analysis verifies the effectiveness and feasibility of the method. The method is applied to the early fault diagnosis of rolling bearings and compared with kurtogram and accugram results. The results show that the approach\u0000 can not only effectively avoid the interference of external impacts but it can also correctly recognise the fault characteristic frequency band.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"52 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875215","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 : 2024-02-01DOI: 10.1784/insi.2024.66.2.103
Beomjin Kim, Younho Cho, Jeongnam Kim, Jiannan Zhang, Kyoung-Sik Jeong, Yunhyeon Baek
The integrity of heat exchanger (HE) tubes is essential for power plant safety and efficiency. Numerous tube assessment techniques have been proposed, including eddy current inspection and the guided wave method. However, they mainly evaluate from the inside of the tubes. Hence, when the interior region of a tube wall is not accessible, inspection is difficult. To overcome this issue, a new guided wave device is fabricated that inspects from the exterior of the heat exchanger tubes. Using this device, a few sample heat exchanger tubes are selected and inspected in a field evaluation during an overhaul period. Furthermore, the feasibility of the device is evaluated. This new approach is expected to contribute to the efficient evaluation of heat exchanger tubes.
{"title":"Preliminary assessment of the feasibility of field evaluation of heat exchanger tube structure using ultrasonic guided wave with exterior approach","authors":"Beomjin Kim, Younho Cho, Jeongnam Kim, Jiannan Zhang, Kyoung-Sik Jeong, Yunhyeon Baek","doi":"10.1784/insi.2024.66.2.103","DOIUrl":"https://doi.org/10.1784/insi.2024.66.2.103","url":null,"abstract":"The integrity of heat exchanger (HE) tubes is essential for power plant safety and efficiency. Numerous tube assessment techniques have been proposed, including eddy current inspection and the guided wave method. However, they mainly evaluate from the inside of the tubes. Hence, when\u0000 the interior region of a tube wall is not accessible, inspection is difficult. To overcome this issue, a new guided wave device is fabricated that inspects from the exterior of the heat exchanger tubes. Using this device, a few sample heat exchanger tubes are selected and inspected in a field\u0000 evaluation during an overhaul period. Furthermore, the feasibility of the device is evaluated. This new approach is expected to contribute to the efficient evaluation of heat exchanger tubes.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"41 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139812347","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 : 2024-02-01DOI: 10.1784/insi.2024.66.2.74
Xianyou Zhong, Liu He, Gang Wan, Yang Zhao
Bearing fault diagnosis helps to ensure the safe operation of electromechanical equipment and reduce unnecessary losses due to downtime. The interference of noise in the signal poses a challenge in the effective identification of rolling bearing faults. To address the above problems, this paper proposes a rolling bearing fault diagnosis (RBFD) method based on generalised dispersive mode decomposition (GDMD) and an accugram. Firstly, the bearing signal is decomposed using GDMD and the optimal number of decomposition modes is chosen using a new index based on the correlation coefficient and accuracy. According to the number of determined decomposition modes, the fault signal is reconstructed. Then, the centre frequency and bandwidth of the resonant frequency are determined using an accugram. Finally, the fault signal is filtered and analysed using a square envelope spectrum to achieve rolling bearing fault diagnosis. Experimental signal analysis verifies the effectiveness and feasibility of the method. The method is applied to the early fault diagnosis of rolling bearings and compared with kurtogram and accugram results. The results show that the approach can not only effectively avoid the interference of external impacts but it can also correctly recognise the fault characteristic frequency band.
{"title":"Fault diagnosis for rolling bearings based on generalised dispersive mode decomposition and accugram","authors":"Xianyou Zhong, Liu He, Gang Wan, Yang Zhao","doi":"10.1784/insi.2024.66.2.74","DOIUrl":"https://doi.org/10.1784/insi.2024.66.2.74","url":null,"abstract":"Bearing fault diagnosis helps to ensure the safe operation of electromechanical equipment and reduce unnecessary losses due to downtime. The interference of noise in the signal poses a challenge in the effective identification of rolling bearing faults. To address the above problems,\u0000 this paper proposes a rolling bearing fault diagnosis (RBFD) method based on generalised dispersive mode decomposition (GDMD) and an accugram. Firstly, the bearing signal is decomposed using GDMD and the optimal number of decomposition modes is chosen using a new index based on the correlation\u0000 coefficient and accuracy. According to the number of determined decomposition modes, the fault signal is reconstructed. Then, the centre frequency and bandwidth of the resonant frequency are determined using an accugram. Finally, the fault signal is filtered and analysed using a square envelope\u0000 spectrum to achieve rolling bearing fault diagnosis. Experimental signal analysis verifies the effectiveness and feasibility of the method. The method is applied to the early fault diagnosis of rolling bearings and compared with kurtogram and accugram results. The results show that the approach\u0000 can not only effectively avoid the interference of external impacts but it can also correctly recognise the fault characteristic frequency band.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"20 S7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139815420","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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