Insight - Non-Destructive Testing and Condition Monitoring最新文献_第4页

Target Detection of Substation Electrical Equipment from Infrared Images Using an Improved Faster Regions with Convolutional Neural Network Features Algorithm 基于改进的快速区域卷积神经网络特征算法的变电站电气设备红外图像目标检测

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-08-01 DOI: 10.1784/insi.2023.65.8.423

Tao Xue, Changdong Wu

{"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}

引用次数: 0

Microwave Transmission Method for The Detection of Water Holdup in Oil-water Mixtures Based on a Yagi Antenna 基于八木天线的油水混合物含水率微波传输检测方法

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-08-01 DOI: 10.1784/insi.2023.65.8.443

Hongwei Qin, Pei Yang, Ruirong Dang

引用次数: 0

Development of a Novel Hybrid Method to Evaluate Driving Comfort in an All-terrain Vehicle Using Transfer Path Analysis and Transfer Function Analysis 基于传递路径分析和传递函数分析的全地形车辆驾驶舒适性评价方法研究

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-08-01 DOI: 10.1784/insi.2023.65.8.450

A. Bhende, M. Satyanarayana

{"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}

引用次数: 0

Weak Local Fault Diagnosis of Gearboxes Based on Adaptive Inertia Factor Particle Swarm Independent Component Analysis 基于自适应惯性因子粒子群独立分量分析的齿轮箱弱局部故障诊断

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-08-01 DOI: 10.1784/insi.2023.65.8.415

Z. Shang, Cailu Pan, Yan Yu, Fei Liu, Maosheng Gao

{"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}

引用次数: 0

Multi-task Learning for the Bearing Based on a One-Dimensional Convolutional Neural Network with Attention Guidance Mechanism and Multi-Scale Feature Extraction 基于注意引导机制和多尺度特征提取的一维卷积神经网络轴承多任务学习

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-08-01 DOI: 10.1784/insi.2023.65.8.433

Yitong Xing, Jian Feng, Yu Yao, Keqin Li, Bowen Wang

{"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}

引用次数: 0

A review of the classification of non-ferrous metals using magnetic induction for recycling 磁感应回收有色金属分类综述

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-07-01 DOI: 10.1784/insi.2023.65.7.384

K. Williams, M. O’Toole, M. Mallaburn, A. Peyton

引用次数: 0

Methods for quantification and integration of human factors into probability of detection assessments 将人为因素量化并整合到检测概率评估中的方法

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-07-01 DOI: 10.1784/insi.2023.65.7.364

M. Bertovic, J. Given, V. Rentala, M. Wall, D. Kanzler, J. Lehleitner, T. Heckel, V. Tkachenko

引用次数: 0

Research on the metal magnetic memory effect of a steel box girder under four-point bending 四点弯曲下钢箱梁金属磁记忆效应研究

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-07-01 DOI: 10.1784/insi.2023.65.7.389

Ruize Deng, S. Su, Wen Wang, F. Zuo

引用次数: 0

Data processing for rail level dynamic inspection based on an adaptive Kalman filter 基于自适应卡尔曼滤波的钢轨液位动态检测数据处理

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-07-01 DOI: 10.1784/insi.2023.65.7.373

Jingbo Xu, Xiaohong Xu, Qiaowei Li

引用次数: 0

Classification of internal defects of gas turbine blades based on the discrimination of linear attenuation coefficients 基于线性衰减系数判别的燃气轮机叶片内部缺陷分类

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2023-06-01 DOI: 10.1784/insi.2023.65.6.335

Lei Zhang, Bing-chuan Li, Lei Chen, Zhongyu Shang, Tongkun Liu

{"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}

引用次数: 1