Maintenance, Reliability and Condition Monitoring最新文献_第2页

Impact of climate change on railway operation and maintenance in Sweden: A State-of-the-art review 气候变化对瑞典铁路运营和维护的影响:最新研究综述

Maintenance, Reliability and Condition Monitoring Pub Date : 2021-08-06 DOI: 10.21595/mrcm.2021.22136

A. Thaduri, A. Garmabaki, U. Kumar

{"title":"Impact of climate change on railway operation and maintenance in Sweden: A State-of-the-art review","authors":"A. Thaduri, A. Garmabaki, U. Kumar","doi":"10.21595/mrcm.2021.22136","DOIUrl":"https://doi.org/10.21595/mrcm.2021.22136","url":null,"abstract":"Increased intensity and frequency of extreme weather conditions caused by climate change can have a negative impact on rail service performance and also increases total ownership costs. Research has shown that adverse weather conditions are responsible for 5 to 10 % of total failures and 60 % of delays on the railway infrastructure in Sweden. The impact of short-term and long-term effects of climate change and extreme weather events depends on the design characteristics of the railway assets, geographical location, operational profile, maturity of the climate adaptation, etc. These extreme events will have major consequences such as traffic disruption, accidents, and higher maintenance costs during the operation and maintenance (O&M) phase. Therefore, a detailed assessment of the effects of climate change on the O&M phase requires a more comprehensive review of the previous studies reported from different parts of the world. The paper provides a state-of-the-art review of the effects of extreme weather events and their impacts on the operation and maintenance of railway infrastructure. This paper also provides a list of vulnerable railway assets that can have an impact due to extreme weather events.","PeriodicalId":285529,"journal":{"name":"Maintenance, Reliability and Condition Monitoring","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125728854","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: 14

Crack identification for bridge condition monitoring using deep convolutional networks trained with a feedback-update strategy 基于反馈更新策略训练的深度卷积网络在桥梁状态监测中的裂缝识别

Maintenance, Reliability and Condition Monitoring Pub Date : 2021-08-06 DOI: 10.21595/mrcm.2021.22032

J. Hua, Tong Tong, Jing Lin, Fei Gao, Han Zhang

{"title":"Crack identification for bridge condition monitoring using deep convolutional networks trained with a feedback-update strategy","authors":"J. Hua, Tong Tong, Jing Lin, Fei Gao, Han Zhang","doi":"10.21595/mrcm.2021.22032","DOIUrl":"https://doi.org/10.21595/mrcm.2021.22032","url":null,"abstract":"Orthotropic steel bridge decks and steel box girders are key structures of long-span bridges. Fatigue cracks often occur in these structures due to coupled factors of initial material flaws and dynamic vehicle loads, which drives the need for automating crack identification for bridge condition monitoring. With the use of unmanned aerial vehicle (UAV), the acquirement of bridge surface pictures is convenient, which facilitates the development of vision-based bridge condition monitoring. In this study, a combination of convolutional neural network (CNN) with fully convolutional network (FCN) is designed for crack identification and bridge condition monitoring. Firstly, 120 images are cropped into small patches to create a basic dataset. Subsequently, CNN and FCN models are trained for patch classification and pixel-level crack segmentation, respectively. In patch classification, some non-crack patches that contain complicated disturbance information, such as handwriting and shadow, are often mistakenly identified as cracks by directly using the CNN model. To address this problem, we propose a feedback-update strategy for CNN training, in which mistaken classification results of non-crack data are selected to update the training set to generate a new CNN model. By that analogy, several different CNN models are obtained and the accuracy of patch classification could be improved by using all models together. Finally, 80 test images are processed by the feedback-update CNN models and FCN model with a sliding window technique to generate crack identification results. Intersection over union (IoU) is calculated as an index to quantificationally evaluate the accuracy of the proposed method.","PeriodicalId":285529,"journal":{"name":"Maintenance, Reliability and Condition Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129010586","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: 2

A numerical study of rotor eccentricity and dynamic load in induction machines for motor current analysis based diagnostics 基于电机电流分析诊断的感应电机转子偏心和动负载数值研究

Maintenance, Reliability and Condition Monitoring Pub Date : 2021-08-06 DOI: 10.21595/mrcm.2021.22145

F. Gu, Haiyang Li, Zhexiang Zou, Xiuquan Sun, A. Ball

{"title":"A numerical study of rotor eccentricity and dynamic load in induction machines for motor current analysis based diagnostics","authors":"F. Gu, Haiyang Li, Zhexiang Zou, Xiuquan Sun, A. Ball","doi":"10.21595/mrcm.2021.22145","DOIUrl":"https://doi.org/10.21595/mrcm.2021.22145","url":null,"abstract":"The asymmetry in the manufacturing and assembling is the common issue of rotor systems. Different degrees of errors are inevitable in alternating current (AC) motors, which causes degraded performances. Furthermore, around 80 % of mechanical faults link to rotor eccentricity. The eccentricity faults (EFs) generate excessive mechanical stress and then lead to fatigue in the other parts of the motor. Motor current signal analysis (MCSA) can be used to diagnose induction machine (IM) faults. As the EF leads to an unequal air gap when the rotor rotates, the inductance of IM also responds to the EF. Moreover, the dynamic load is a typical situation due to residual dynamic unbalance and misalignment. To study how EFs and dynamic load affect the stator current. The current model of symmetrical motor, asymmetrical motors with three-level EFs and with dynamic load are investigated numerically. The correctness of models is verified through experimental study. The results show the level of EF affects the sideband peak values significantly in the stator current spectrum. These findings will provide a foundation for the accurate diagnosis of motor health conditions.","PeriodicalId":285529,"journal":{"name":"Maintenance, Reliability and Condition Monitoring","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117150794","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

PM4 SMP model proposed for system reliability criticality assessment and maintainability improvement 提出了PM4 SMP模型，用于系统可靠性关键性评估和可维护性改进

Maintenance, Reliability and Condition Monitoring Pub Date : 2021-06-30 DOI: 10.21595/mrcm.2021.22111

Ogechukwu Akaeje, Martin Billington, J. Sinha

引用次数: 0