{"title":"IoT Enabled Condition Monitoring Under Predictive Maintenance Framework of Mines and Steel Plant","authors":"Dr. Prabal Patra","doi":"10.54026/jmms/1087","DOIUrl":null,"url":null,"abstract":"This case is about a century-old integrated steel plant combined with mines, having more than 0.42 million capitalintensive assets, ages ranging from 0 to 100 years and spreads across 700+ hectares as shown in Figure 1. Assets having varied levels of automation maturity with different maintenance methodologies. It’s about shifting the whole maintenance paradigm from Time-based & Condition-based to Predictive. Through digital initiatives & optimum maintenance cost, we are trying to develop a Maintenance Transformation Roadmap (MTR) to ensure maximum machine availability/ reliability. For this paradigm shift from conventional to predictive maintenance, critical assets were identified through a systematic approach. Under this MTR Journey, to bridge the sensor gap, SMART Sensor (3 axis vibration & Temperature) was identified as the appropriate solution. Customizable In-house SMART Sensor applicable in Mining & Steel industry application and low-cost solution helped in cross locational horizontal. Presently early warning alerts saved 1000+ potential breakdowns on 1200nos critical assets. The Predictive Maintenance Framework has both tangible and intangible benefits. While safety is the most important intangible benefit of this technology, tangible benefits include approved savings of $20 Million. Further, we expect to save $12.5 Million by deploying our Sensor and $35 Million via prevention of breakdowns","PeriodicalId":503317,"journal":{"name":"Journal of Mineral and Material Science (JMMS)","volume":"54 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mineral and Material Science (JMMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54026/jmms/1087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This case is about a century-old integrated steel plant combined with mines, having more than 0.42 million capitalintensive assets, ages ranging from 0 to 100 years and spreads across 700+ hectares as shown in Figure 1. Assets having varied levels of automation maturity with different maintenance methodologies. It’s about shifting the whole maintenance paradigm from Time-based & Condition-based to Predictive. Through digital initiatives & optimum maintenance cost, we are trying to develop a Maintenance Transformation Roadmap (MTR) to ensure maximum machine availability/ reliability. For this paradigm shift from conventional to predictive maintenance, critical assets were identified through a systematic approach. Under this MTR Journey, to bridge the sensor gap, SMART Sensor (3 axis vibration & Temperature) was identified as the appropriate solution. Customizable In-house SMART Sensor applicable in Mining & Steel industry application and low-cost solution helped in cross locational horizontal. Presently early warning alerts saved 1000+ potential breakdowns on 1200nos critical assets. The Predictive Maintenance Framework has both tangible and intangible benefits. While safety is the most important intangible benefit of this technology, tangible benefits include approved savings of $20 Million. Further, we expect to save $12.5 Million by deploying our Sensor and $35 Million via prevention of breakdowns