{"title":"煤矿用大容量矿用电动卡车系统可靠性分析","authors":"Sedat Toraman","doi":"10.13052/jrss0974-8024.1614","DOIUrl":null,"url":null,"abstract":"In today’s world where competition is increasing, the focus of all efforts on meeting production targets at the lowest possible cost has increased the need for maximum benefit from existing machinery/equipment. In this context, availability, reliability and maintainability analysis have become the basic tools to be used in meeting this need.\nIn this study, reliability, maintainability and availability values were determined using the operation and repair data (2019) of large capacity electric trucks used in the Manisa-Soma coal field. First of all, each truck was analyzed and then the analysis of the entire truck system was performed. In this context, RAM analysis of all trucks was performed and then the general reliability of the entire truck system was calculated by considering the working order in the enterprise. According to this; The truck with the highest reliability is the truck with door number 555 and the reliability rate is 77.04%, and the truck with the lowest reliability is the truck number 556 with a rate of 26.31%. As a result of the maintainability analysis, the truck with the 553 door number had the highest maintainability with a rate of 73.52%. The truck numbered 549 has the lowest maintainability rate with a rate of 42.21%. As a result of the system analysis, the reliability rate was determined as 24% in R (1000) minutes, 59% in R (500) minutes and 83% in R (250) minutes.","PeriodicalId":42526,"journal":{"name":"Journal of Reliability and Statistical Studies","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"System Reliability Analysis of Large Capacity Electric Mining Trucks Used in Coal Mining\",\"authors\":\"Sedat Toraman\",\"doi\":\"10.13052/jrss0974-8024.1614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In today’s world where competition is increasing, the focus of all efforts on meeting production targets at the lowest possible cost has increased the need for maximum benefit from existing machinery/equipment. In this context, availability, reliability and maintainability analysis have become the basic tools to be used in meeting this need.\\nIn this study, reliability, maintainability and availability values were determined using the operation and repair data (2019) of large capacity electric trucks used in the Manisa-Soma coal field. First of all, each truck was analyzed and then the analysis of the entire truck system was performed. In this context, RAM analysis of all trucks was performed and then the general reliability of the entire truck system was calculated by considering the working order in the enterprise. According to this; The truck with the highest reliability is the truck with door number 555 and the reliability rate is 77.04%, and the truck with the lowest reliability is the truck number 556 with a rate of 26.31%. As a result of the maintainability analysis, the truck with the 553 door number had the highest maintainability with a rate of 73.52%. The truck numbered 549 has the lowest maintainability rate with a rate of 42.21%. As a result of the system analysis, the reliability rate was determined as 24% in R (1000) minutes, 59% in R (500) minutes and 83% in R (250) minutes.\",\"PeriodicalId\":42526,\"journal\":{\"name\":\"Journal of Reliability and Statistical Studies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2023-07-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Reliability and Statistical Studies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.13052/jrss0974-8024.1614\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Reliability and Statistical Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13052/jrss0974-8024.1614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
System Reliability Analysis of Large Capacity Electric Mining Trucks Used in Coal Mining
In today’s world where competition is increasing, the focus of all efforts on meeting production targets at the lowest possible cost has increased the need for maximum benefit from existing machinery/equipment. In this context, availability, reliability and maintainability analysis have become the basic tools to be used in meeting this need.
In this study, reliability, maintainability and availability values were determined using the operation and repair data (2019) of large capacity electric trucks used in the Manisa-Soma coal field. First of all, each truck was analyzed and then the analysis of the entire truck system was performed. In this context, RAM analysis of all trucks was performed and then the general reliability of the entire truck system was calculated by considering the working order in the enterprise. According to this; The truck with the highest reliability is the truck with door number 555 and the reliability rate is 77.04%, and the truck with the lowest reliability is the truck number 556 with a rate of 26.31%. As a result of the maintainability analysis, the truck with the 553 door number had the highest maintainability with a rate of 73.52%. The truck numbered 549 has the lowest maintainability rate with a rate of 42.21%. As a result of the system analysis, the reliability rate was determined as 24% in R (1000) minutes, 59% in R (500) minutes and 83% in R (250) minutes.