矿山和钢铁厂预测性维护框架下的物联网状态监测

Dr. Prabal Patra
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摘要

如图 1 所示,该案例涉及一家拥有百年历史的综合钢铁厂和矿山,拥有超过 42 万项资本密集型资产,使用年限从 0 年到 100 年不等,占地面积超过 700 公顷。这些资产的自动化成熟度各不相同,维护方法也不尽相同。这就需要将整个维护模式从基于时间和条件的维护转变为基于预测的维护。通过数字化举措和最佳维护成本,我们正在努力制定维护转型路线图(MTR),以确保最大限度地提高机器可用性/可靠性。为了实现从传统维护到预测性维护的模式转变,我们通过系统化的方法确定了关键资产。在这一 MTR 计划中,为弥补传感器方面的差距,SMART 传感器(3 轴振动和温度)被确定为合适的解决方案。可定制的内部 SMART 传感器适用于采矿和钢铁行业的应用,其低成本解决方案有助于横向定位。目前,早期预警警报挽救了 1200 多台关键资产上的 1000 多个潜在故障。预测性维护框架既有有形效益,也有无形效益。安全是这项技术最重要的无形效益,而有形效益则包括已批准的 2,000 万美元的节约。此外,我们预计通过部署我们的传感器可节省 1250 万美元,通过预防故障可节省 3500 万美元。
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IoT Enabled Condition Monitoring Under Predictive Maintenance Framework of Mines and Steel Plant
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
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