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Trend-based repair quality assessment for industrial rotating equipment
IEEE Control Systems Letters Pub Date : 2021-11-01 , DOI: 10.1109/lcsys.2020.3041214
Maxwell Toothman , Birgit Braun , Scott J. Bury , Michael Dessauer , Kaytlin Henderson , Ray Wright , Dawn M. Tilbury , James Moyne , Kira Barton

Rotating equipment is widespread in the process industry, where pumps, compressors, and turbines are used to drive continuous manufacturing lines. This class of machinery is meant to run without interruption, but invariably experiences degradation that can lead to equipment failure. Any break in a continuous manufacturing line can halt production, so there is a pressing need to diagnose rotating equipment health before failure occurs. Existing health modeling and diagnosis strategies require supplemental tests to collect model training data, and ignore time-series behavior in machine signals that can be useful for diagnosing equipment health. This letter presents a general modeling structure to give context to historical data, which can act as a substitute for supplemental test data, and describes a methodology for assessing repair quality based on trends in signal features. A case study that uses the proposed methodology to assess the quality of repair procedures is provided.

中文翻译:

基于趋势的工业旋转设备维修质量评估

旋转设备在加工业中得到广泛应用,其中泵,压缩机和涡轮机用于驱动连续生产线。此类机械旨在不间断地运行,但总是会遭受退化,从而导致设备故障。连续生产线的任何中断都可能导致生产中断,因此迫切需要在故障发生之前诊断旋转设备的运行状况。现有的健康建模和诊断策略需要补充测试以收集模型训练数据,并忽略机器信号中的时间序列行为,这对于诊断设备健康非常有用。这封信提出了一个通用的建模结构,可为历史数据提供背景信息,可以代替补充测试数据,并描述了一种基于信号特征趋势评估维修质量的方法。提供了使用建议的方法来评估维修程序质量的案例研究。
更新日期:2021-11-01
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