Pub Date : 2023-08-01DOI: 10.1784/insi.2023.65.8.423
Tao Xue, Changdong Wu
The failure of substation equipment can cause incalculable losses to the economy and power consumption of the whole country. The use of infrared images is a powerful tool to obtain equipment temperature, which can then be used directly to diagnose substation equipment without stopping the operation of the equipment. In this paper, the authors focus on the correct identification of different types of electrical equipment from the infrared images. An improved faster regions with convolutional neural network features (faster R-CNN) algorithm is proposed, which shows very high detection accuracy for substation equipment. Firstly, the backbone of the faster R-CNN is optimised. A new network, the ResNet-30 network, is designed to reduce the redundancy of the ResNet-34 network and increases the proportion of residual blocks in the network in the previous stages. Next, the deep receptive field is combined with the shallow receptive field of the network and a double-shortcut structure with a large convolutional kernel is proposed. This enhances the ability of network feature extraction. A cross-channel shortcut is proposed at the channel transition of the network based on the channel number relationship between the dual-shortcut structures. Finally, the proposed method is compared with faster R-CNNs whose backbones are ResNet-50 plus a feature pyramid network (ResNet-50+FPN), you only look once v3 plus spatial pyramid pooling (YOLOv3+SPP) and a single-shot multibox detector (SSD). The results show that the improved model not only has a smaller number of parameters and low requirements for graphics processing unit (GPU) equipment, but also has the highest mean average precision (mAP) for mostly substation equipment in the test-set. This lays a foundation for fault diagnosis of substation equipment in the future.
{"title":"Target Detection of Substation Electrical Equipment from Infrared Images Using an Improved Faster Regions with Convolutional Neural Network Features Algorithm","authors":"Tao Xue, Changdong Wu","doi":"10.1784/insi.2023.65.8.423","DOIUrl":"https://doi.org/10.1784/insi.2023.65.8.423","url":null,"abstract":"The failure of substation equipment can cause incalculable losses to the economy and power consumption of the whole country. The use of infrared images is a powerful tool to obtain equipment temperature, which can then be used directly to diagnose substation equipment without stopping\u0000 the operation of the equipment. In this paper, the authors focus on the correct identification of different types of electrical equipment from the infrared images. An improved faster regions with convolutional neural network features (faster R-CNN) algorithm is proposed, which shows very high\u0000 detection accuracy for substation equipment. Firstly, the backbone of the faster R-CNN is optimised. A new network, the ResNet-30 network, is designed to reduce the redundancy of the ResNet-34 network and increases the proportion of residual blocks in the network in the previous stages. Next,\u0000 the deep receptive field is combined with the shallow receptive field of the network and a double-shortcut structure with a large convolutional kernel is proposed. This enhances the ability of network feature extraction. A cross-channel shortcut is proposed at the channel transition of the\u0000 network based on the channel number relationship between the dual-shortcut structures. Finally, the proposed method is compared with faster R-CNNs whose backbones are ResNet-50 plus a feature pyramid network (ResNet-50+FPN), you only look once v3 plus spatial pyramid pooling (YOLOv3+SPP)\u0000 and a single-shot multibox detector (SSD). The results show that the improved model not only has a smaller number of parameters and low requirements for graphics processing unit (GPU) equipment, but also has the highest mean average precision (mAP) for mostly substation equipment in the test-set.\u0000 This lays a foundation for fault diagnosis of substation equipment in the future.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128915925","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 : 2023-08-01DOI: 10.1784/insi.2023.65.8.443
Hongwei Qin, Pei Yang, Ruirong Dang
In this paper, Yagi antennas are introduced into microwave transmission measuring instruments as the transmit and receive antennas for detecting moisture content in oil-water mixtures. A Yagi antenna is designed, where the simulation results show a peak gain of 9 dBi and the reflection coefficient S11 is lower than ???10 dB in a frequency band of 2.25-4 GHz. Meanwhile, the measurement result using a vector network analyser shows that the Yagi antenna works in the frequency band of 2.9-4.15 GHz, indicating that measurement results shifted to high frequencies. Based on the Yagi antennas, a moisture content measuring system using the microwave transmission method is designed and constructed, where the measurement results show the Yagi directional high-gain microwave antenna used in this paper can achieve water holdup measurement in the range of 0-100%, where the relative error is less than ??15% and the absolute error is within ??2.341%.
{"title":"Microwave Transmission Method for The Detection of Water Holdup in Oil-water Mixtures Based on a Yagi Antenna","authors":"Hongwei Qin, Pei Yang, Ruirong Dang","doi":"10.1784/insi.2023.65.8.443","DOIUrl":"https://doi.org/10.1784/insi.2023.65.8.443","url":null,"abstract":"In this paper, Yagi antennas are introduced into microwave transmission measuring instruments as the transmit and receive antennas for detecting moisture content in oil-water mixtures. A Yagi antenna is designed, where the simulation results show a peak gain of 9 dBi and the reflection\u0000 coefficient S11 is lower than ???10 dB in a frequency band of 2.25-4 GHz. Meanwhile, the measurement result using a vector network analyser shows that the Yagi antenna works in the frequency band of 2.9-4.15 GHz, indicating that measurement results shifted to high frequencies. Based on the\u0000 Yagi antennas, a moisture content measuring system using the microwave transmission method is designed and constructed, where the measurement results show the Yagi directional high-gain microwave antenna used in this paper can achieve water holdup measurement in the range of 0-100%, where\u0000 the relative error is less than ??15% and the absolute error is within ??2.341%.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128727364","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 : 2023-08-01DOI: 10.1784/insi.2023.65.8.450
A. Bhende, M. Satyanarayana
Vehicle comfort has become a buzzword in the automobile sector and continuous research is going on in this domain. Every automobile manufacturer would like to take the lead in vehicle comfort so as to attract more customers. Noise, vibration and harshness (NVH) testing is very important for improving the driving comfort of a vehicle. Driving comfort is directly related to the driving ability and health of the driver. Many international organisations have laid down guidelines for measuring driving comfort and categorise it in a range from comfortable to extremely uncomfortable. The present study adopts an experimental approach to determine the driving comfort in all-terrain vehicles (ATVs) by measuring frequency-weighted root-mean-square (RMS) accelerations at all the driver contact points in three mutually perpendicular directions as per the guidelines laid down in ISO 2631-1:1997 and ISO 5349-1:2001. The low-amplitude high-frequency engine vibrations are attenuated by performing transfer path analysis (TPA) of the vehicle roll cage before and after design modifications. The performance of the engine isolator mount is evaluated by carrying out transfer function analysis (TFA) of the active and passive engine mount vibrations. A novel hybrid approach comprising the TPA and TFA methods is used to attenuate the engine vibrations. The test result shows the effectiveness of the design modifications at the footrest, whereas there is moderate to low effectiveness at the steering and seat, respectively.
{"title":"Development of a Novel Hybrid Method to Evaluate Driving Comfort in an All-terrain Vehicle Using Transfer Path Analysis and Transfer Function Analysis","authors":"A. Bhende, M. Satyanarayana","doi":"10.1784/insi.2023.65.8.450","DOIUrl":"https://doi.org/10.1784/insi.2023.65.8.450","url":null,"abstract":"Vehicle comfort has become a buzzword in the automobile sector and continuous research is going on in this domain. Every automobile manufacturer would like to take the lead in vehicle comfort so as to attract more customers. Noise, vibration and harshness (NVH) testing is very important\u0000 for improving the driving comfort of a vehicle. Driving comfort is directly related to the driving ability and health of the driver. Many international organisations have laid down guidelines for measuring driving comfort and categorise it in a range from comfortable to extremely uncomfortable.\u0000 The present study adopts an experimental approach to determine the driving comfort in all-terrain vehicles (ATVs) by measuring frequency-weighted root-mean-square (RMS) accelerations at all the driver contact points in three mutually perpendicular directions as per the guidelines laid down\u0000 in ISO 2631-1:1997 and ISO 5349-1:2001. The low-amplitude high-frequency engine vibrations are attenuated by performing transfer path analysis (TPA) of the vehicle roll cage before and after design modifications. The performance of the engine isolator mount is evaluated by carrying out transfer\u0000 function analysis (TFA) of the active and passive engine mount vibrations. A novel hybrid approach comprising the TPA and TFA methods is used to attenuate the engine vibrations. The test result shows the effectiveness of the design modifications at the footrest, whereas there is moderate to\u0000 low effectiveness at the steering and seat, respectively.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114869387","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 : 2023-08-01DOI: 10.1784/insi.2023.65.8.415
Z. Shang, Cailu Pan, Yan Yu, Fei Liu, Maosheng Gao
Due to the interference of surrounding noise when collecting the vibration signal of a fixed shaft gearbox, it is impossible to extract the fault features contained in the vibration signal with a high degree of accuracy and this reduces the accuracy of fault diagnosis of the gearbox. Aiming at this problem, this paper proposes a method for local weak fault diagnosis of gears based on improved independent component analysis (ICA). Firstly, for the shortcomings of ICA, such as high requirements for initial value selection, ease of falling into local extrema and the need to derive formulae in advance, this paper proposes to improve the separation performance of the algorithm by combining ICA with particle swarm optimisation (PSO). Also aiming at the shortcomings of slow convergence of PSO and decreased searchability in the later iteration, this paper proposes an adaptive inertia weight particle swarm optimisation (AIWPSO) algorithm by introducing the roulette idea into PSO. Then, combining ICA with AIWPSO, an independent component analysis method for adaptive inertia weight particle swarm optimisation (AIWPSO-ICA) is proposed to improve the signal separation performance. Finally, based on AIWPSO-ICA, a method for diagnosing weak local faults of gears is offered. The simulation signals and the real data experimental results verify the effectiveness and superiority over conventional AIWPSO-ICA.
{"title":"Weak Local Fault Diagnosis of Gearboxes Based on Adaptive Inertia Factor Particle Swarm Independent Component Analysis","authors":"Z. Shang, Cailu Pan, Yan Yu, Fei Liu, Maosheng Gao","doi":"10.1784/insi.2023.65.8.415","DOIUrl":"https://doi.org/10.1784/insi.2023.65.8.415","url":null,"abstract":"Due to the interference of surrounding noise when collecting the vibration signal of a fixed shaft gearbox, it is impossible to extract the fault features contained in the vibration signal with a high degree of accuracy and this reduces the accuracy of fault diagnosis of the gearbox.\u0000 Aiming at this problem, this paper proposes a method for local weak fault diagnosis of gears based on improved independent component analysis (ICA). Firstly, for the shortcomings of ICA, such as high requirements for initial value selection, ease of falling into local extrema and the need\u0000 to derive formulae in advance, this paper proposes to improve the separation performance of the algorithm by combining ICA with particle swarm optimisation (PSO). Also aiming at the shortcomings of slow convergence of PSO and decreased searchability in the later iteration, this paper proposes\u0000 an adaptive inertia weight particle swarm optimisation (AIWPSO) algorithm by introducing the roulette idea into PSO. Then, combining ICA with AIWPSO, an independent component analysis method for adaptive inertia weight particle swarm optimisation (AIWPSO-ICA) is proposed to improve the signal\u0000 separation performance. Finally, based on AIWPSO-ICA, a method for diagnosing weak local faults of gears is offered. The simulation signals and the real data experimental results verify the effectiveness and superiority over conventional AIWPSO-ICA.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129150137","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 : 2023-08-01DOI: 10.1784/insi.2023.65.8.433
Yitong Xing, Jian Feng, Yu Yao, Keqin Li, Bowen Wang
Fault type and fault degree identification are the main aim in the bearing multi-task learning. However, a large number of on-site accidents have shown that the bearing working condition plays an important role in bearing service life and fault diagnosis. In current studies, the bearing working condition identification task is often used for auxiliary tasks and is easily ignored. Thus, this paper studies the bearing multi-task learning, which regards the working condition identification task as an equally important task. However, simply adding the working condition identification task to the frequently used multi-task model will lead to a reduction in the overall performance of the network. To solve the network performance degradation problem, a succinct and effective multi-task one-dimensional convolutional neural network with attention guidance mechanism and multi-scale feature extraction (MAM-1DCNN) is proposed. Firstly, the time-series signal is selected as the input of the MAM-1DCNN model. Secondly, the shared network of the MAM-1DCNN model applies a double-layer multi-scale convolutional neural network structure to extract more complete information. Finally, the MAM-1DCNN applies an improved attention guidance mechanism to enhance the feature application ability of different branch tasks. Through two general bearings datasets, this paper verifies the effectiveness and generalisation ability of the MAM-1DCNN model.
{"title":"Multi-task Learning for the Bearing Based on a One-Dimensional Convolutional Neural Network with Attention Guidance Mechanism and Multi-Scale Feature Extraction","authors":"Yitong Xing, Jian Feng, Yu Yao, Keqin Li, Bowen Wang","doi":"10.1784/insi.2023.65.8.433","DOIUrl":"https://doi.org/10.1784/insi.2023.65.8.433","url":null,"abstract":"Fault type and fault degree identification are the main aim in the bearing multi-task learning. However, a large number of on-site accidents have shown that the bearing working condition plays an important role in bearing service life and fault diagnosis. In current studies, the bearing\u0000 working condition identification task is often used for auxiliary tasks and is easily ignored. Thus, this paper studies the bearing multi-task learning, which regards the working condition identification task as an equally important task. However, simply adding the working condition identification\u0000 task to the frequently used multi-task model will lead to a reduction in the overall performance of the network. To solve the network performance degradation problem, a succinct and effective multi-task one-dimensional convolutional neural network with attention guidance mechanism and multi-scale\u0000 feature extraction (MAM-1DCNN) is proposed. Firstly, the time-series signal is selected as the input of the MAM-1DCNN model. Secondly, the shared network of the MAM-1DCNN model applies a double-layer multi-scale convolutional neural network structure to extract more complete information. Finally,\u0000 the MAM-1DCNN applies an improved attention guidance mechanism to enhance the feature application ability of different branch tasks. Through two general bearings datasets, this paper verifies the effectiveness and generalisation ability of the MAM-1DCNN model.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131396184","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 : 2023-07-01DOI: 10.1784/insi.2023.65.7.384
K. Williams, M. O’Toole, M. Mallaburn, A. Peyton
Magnetic induction is widely used as a non-destructive technique to detect and classify metal objects over a range of applications. This paper applies magnetic induction spectroscopy (MIS) as a technique to classify non-ferrous metals within shredded metal waste streams on a moving conveyor. The magnetic response of the metal piece as it passes over the sensor is used to predict the metal, where the measured complex impedance components are used as features for the machine learning models. MIS performs well, even when surface contaminants are present, compared to other techniques that require the metal pieces to be cleaned; this saves time and reduces cost when large amounts of surface contamination are present in a waste stream, such as biomass incinerator metals. MIS allows for a lower cost system when compared to X-ray and sink-float methods with a high throughput, which makes it an economical approach.
{"title":"A review of the classification of non-ferrous metals using magnetic induction for recycling","authors":"K. Williams, M. O’Toole, M. Mallaburn, A. Peyton","doi":"10.1784/insi.2023.65.7.384","DOIUrl":"https://doi.org/10.1784/insi.2023.65.7.384","url":null,"abstract":"Magnetic induction is widely used as a non-destructive technique to detect and classify metal objects over a range of applications. This paper applies magnetic induction spectroscopy (MIS) as a technique to classify non-ferrous metals within shredded metal waste streams on a moving\u0000 conveyor. The magnetic response of the metal piece as it passes over the sensor is used to predict the metal, where the measured complex impedance components are used as features for the machine learning models. MIS performs well, even when surface contaminants are present, compared to other\u0000 techniques that require the metal pieces to be cleaned; this saves time and reduces cost when large amounts of surface contamination are present in a waste stream, such as biomass incinerator metals. MIS allows for a lower cost system when compared to X-ray and sink-float methods with a high\u0000 throughput, which makes it an economical approach.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125789162","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 : 2023-07-01DOI: 10.1784/insi.2023.65.7.364
M. Bertovic, J. Given, V. Rentala, M. Wall, D. Kanzler, J. Lehleitner, T. Heckel, V. Tkachenko
Human factors (HFs) are a frequently mentioned topic when talking about the reliability of non-destructive testing (NDT). However, probability of detection (POD), the commonly used measure of NDT reliability, only looks at the technical capability of an NDT system to detect a defect. After several decades of research on the influence of HFs on NDT reliability, there is still no commonly accepted approach to rendering HFs visible in reliability assessment. This paper provides an overview of possible quantitative and qualitative methods for integrating HFs into the reliability assessment. It is concluded that reliability assessment is best carried out using both quantifiable and non-quantifiable approaches to HFs.
{"title":"Methods for quantification and integration of human factors into probability of detection assessments","authors":"M. Bertovic, J. Given, V. Rentala, M. Wall, D. Kanzler, J. Lehleitner, T. Heckel, V. Tkachenko","doi":"10.1784/insi.2023.65.7.364","DOIUrl":"https://doi.org/10.1784/insi.2023.65.7.364","url":null,"abstract":"Human factors (HFs) are a frequently mentioned topic when talking about the reliability of non-destructive testing (NDT). However, probability of detection (POD), the commonly used measure of NDT reliability, only looks at the technical capability of an NDT system to detect a defect.\u0000 After several decades of research on the influence of HFs on NDT reliability, there is still no commonly accepted approach to rendering HFs visible in reliability assessment. This paper provides an overview of possible quantitative and qualitative methods for integrating HFs into the reliability\u0000 assessment. It is concluded that reliability assessment is best carried out using both quantifiable and non-quantifiable approaches to HFs.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121728896","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 : 2023-07-01DOI: 10.1784/insi.2023.65.7.389
Ruize Deng, S. Su, Wen Wang, F. Zuo
It is valuable to conduct non-destructive testing of steel box girders in order to evaluate their working status. The metal magnetic memory inspection method can effectively identify early damage and the stress state of ferromagnetic materials. However, applying this technique in the inspection of steel components is difficult due to insufficient research on magnetic memory signals under complex stress states. This study analyses the magnetic memory effect for a steel box girder under four-point bending. It is shown that the normal component Hp(y) of the magnetic signal can effectively locate the stress concentration area. The average absolute value Hm of Hp(y) can identify the yielding state and predict the occurrence of failure. Hm changes roughly quadratically with the average strain ɛm in the elastic stage of the specimen, which is consistent with the theoretical result. The ratio D of Hm to the equivalent stress σeqv changes continuously with the applied load, which can be used to estimate the stress state of the steel box girder.
{"title":"Research on the metal magnetic memory effect of a steel box girder under four-point bending","authors":"Ruize Deng, S. Su, Wen Wang, F. Zuo","doi":"10.1784/insi.2023.65.7.389","DOIUrl":"https://doi.org/10.1784/insi.2023.65.7.389","url":null,"abstract":"It is valuable to conduct non-destructive testing of steel box girders in order to evaluate their working status. The metal magnetic memory inspection method can effectively identify early damage and the stress state of ferromagnetic materials. However, applying this technique in the\u0000 inspection of steel components is difficult due to insufficient research on magnetic memory signals under complex stress states. This study analyses the magnetic memory effect for a steel box girder under four-point bending. It is shown that the normal component Hp(y) of the magnetic\u0000 signal can effectively locate the stress concentration area. The average absolute value Hm of Hp(y) can identify the yielding state and predict the occurrence of failure. Hm changes roughly quadratically with the average strain ɛm in the elastic\u0000 stage of the specimen, which is consistent with the theoretical result. The ratio D of Hm to the equivalent stress σeqv changes continuously with the applied load, which can be used to estimate the stress state of the steel box girder.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126712554","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 : 2023-07-01DOI: 10.1784/insi.2023.65.7.373
Jingbo Xu, Xiaohong Xu, Qiaowei Li
The inspection of the geometrical parameters of rail tracks is an important aspect in the daily maintenance and safe running of railways. The rail level (superelevation) is one of the important indicators susceptible to measurement noise. In this paper, the principle of the Kalman filter is studied, an adaptive Kalman filter algorithm is designed for level (superelevation) dynamic inspection, the selection principle for the filtering parameters is discussed and the performance of the algorithm is verified through simulation tests and pushing experiments using a rail inspection trolley. From analysis of the measurement data, it is concluded that the trolley speed is an important factor affecting level (superelevation) inspection and an improved algorithm including the trolley speed is proposed to further improve the filtering ability. The algorithm is easy to implement and can be extended to dynamic rail inspection.
{"title":"Data processing for rail level dynamic inspection based on an adaptive Kalman filter","authors":"Jingbo Xu, Xiaohong Xu, Qiaowei Li","doi":"10.1784/insi.2023.65.7.373","DOIUrl":"https://doi.org/10.1784/insi.2023.65.7.373","url":null,"abstract":"The inspection of the geometrical parameters of rail tracks is an important aspect in the daily maintenance and safe running of railways. The rail level (superelevation) is one of the important indicators susceptible to measurement noise. In this paper, the principle of the Kalman filter\u0000 is studied, an adaptive Kalman filter algorithm is designed for level (superelevation) dynamic inspection, the selection principle for the filtering parameters is discussed and the performance of the algorithm is verified through simulation tests and pushing experiments using a rail inspection\u0000 trolley. From analysis of the measurement data, it is concluded that the trolley speed is an important factor affecting level (superelevation) inspection and an improved algorithm including the trolley speed is proposed to further improve the filtering ability. The algorithm is easy to implement\u0000 and can be extended to dynamic rail inspection.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128118980","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 : 2023-06-01DOI: 10.1784/insi.2023.65.6.335
Lei Zhang, Bing-chuan Li, Lei Chen, Zhongyu Shang, Tongkun Liu
In the process of manufacturing and servicing gas turbine blades, various types of defect are formed and grow rapidly due to the extremely harsh working environment, which poses a huge threat to the safe operation of the gas turbines. Given that different types of defect cause varying degrees of damage to the turbine blades, it is vital to distinguish and deal with defects differently. Considering the shape of the blade (free-form surface) and the location of the defect (inside the blade), digital radiographic imaging can be used for the non-destructive testing of turbine blades. Although some types of defect (for example porosity and cracks) can be distinguished from others (for example voids and inclusions) based on differences in morphological and textural characteristics, others (for example voids and inclusions) may be misclassified due to similarities in morphological and textural characteristics. These defects with similar morphological characteristics are composed of different materials, which can be utilised as a basis for classification. This paper presents a classification method for defects with similar morphological characteristics based on the discrimination of linear attenuation coefficients. Several typical defects, including voids and inclusions, are set into a cuboidal block and into nylon blades in this work. Their corresponding linear attenuation coefficients are obtained. A binary classification of the linear attenuation coefficient enables the categorisation of voids and inclusions. Experimental results demonstrate that the proposed method has high efficiency and the judgement for voids and inclusions is accurate.
{"title":"Classification of internal defects of gas turbine blades based on the discrimination of linear attenuation coefficients","authors":"Lei Zhang, Bing-chuan Li, Lei Chen, Zhongyu Shang, Tongkun Liu","doi":"10.1784/insi.2023.65.6.335","DOIUrl":"https://doi.org/10.1784/insi.2023.65.6.335","url":null,"abstract":"In the process of manufacturing and servicing gas turbine blades, various types of defect are formed and grow rapidly due to the extremely harsh working environment, which poses a huge threat to the safe operation of the gas turbines. Given that different types of defect cause varying\u0000 degrees of damage to the turbine blades, it is vital to distinguish and deal with defects differently. Considering the shape of the blade (free-form surface) and the location of the defect (inside the blade), digital radiographic imaging can be used for the non-destructive testing of turbine\u0000 blades. Although some types of defect (for example porosity and cracks) can be distinguished from others (for example voids and inclusions) based on differences in morphological and textural characteristics, others (for example voids and inclusions) may be misclassified due to similarities\u0000 in morphological and textural characteristics. These defects with similar morphological characteristics are composed of different materials, which can be utilised as a basis for classification. This paper presents a classification method for defects with similar morphological characteristics\u0000 based on the discrimination of linear attenuation coefficients. Several typical defects, including voids and inclusions, are set into a cuboidal block and into nylon blades in this work. Their corresponding linear attenuation coefficients are obtained. A binary classification of the linear\u0000 attenuation coefficient enables the categorisation of voids and inclusions. Experimental results demonstrate that the proposed method has high efficiency and the judgement for voids and inclusions is accurate.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"322 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114609022","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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