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设备在线监测与远程故障诊断平台在冶金行业的应用 被引量:1

Application of online equipment monitoring and remote fault diagnosis platform in the metallurgical industry
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摘要 随着科学技术的飞速发展,各行各业在对机械设备管理方面也有了新的要求,在冶金行业也不例外,为了追求更高的经济效益,需要保证机械设备要有较高的运转率,尽量避免设备非计划停机。但是,机械设备在长时间的运转中难免会产生一些故障,这些故障仅仅依赖人工检测无法做到精确检测和及时发现。所以,设备在线监测与远程故障诊断平台技术就应运而生,合理的应用在线监测与远程故障诊断平台可有效避免和减少重大设备事故的发生,通过对设备异常运行状态的早期预警与分析,可以揭示故障的原因、程度、部位,发展趋势等,为设备的在线调整、停机检修提供科学依据,避免常规计划检修模式可能造成的“过修”或“欠修”,可以有效延长设备运行寿命,显著降低维修费用,为企业由传统的“计划维修”模式向更加科学的“预测性维修”模式转变。 As science and technology advance in leaps and bounds,all walks of life have new demands for mechanical equipment management.The metallurgical industry is no exception.To pursue higher economic benefits,it is necessary to ensure that mechanical equipment operates efficiently,and unplanned equipment shutdown should be avoided as far as possible.However,mechanical equipment inevitably breaks down after a long period of operation.These failures cannot be detected in an accurate and timely manner through manual detection.Therefore,the online equipment monitoring and remote fault diagnosis platform technology is born.Major equipment accidents can be avoided or reduced through the reasonable application of online monitoring and remote fault diagnosis platform.Through the early warning and analysis of abnormal operation of equipment,the platform can reveal the causes,extent,and location of failures,development trend,etc.It can provide a scientific basis for online equipment adjustment and shutdown maintenance,avoid“excessive maintenance”or“inadequate maintenance”caused by the conventional planned maintenance mode,prolong the lifespan of equipment,slash maintenance costs,and help enterprises adopt the scientific“predictive maintenance”model instead of the traditional“planned maintenance”model.
作者 白云风 蒋燕生 钱建文 黄成雄 BAI Yun-feng;JIANG Yan-sheng;QIAN Jian-wen;HUANG Cheng-xiong(Yunnan Chihong Resources Comprehensive Utilization Co.,Ltd,Qujing 655000 China)
出处 《世界有色金属》 2022年第16期4-7,共4页 World Nonferrous Metals
关键词 在线监测 故障诊断 预测性维修 online monitoring fault diagnosis predictive maintenance
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