文章基本信息

标题：Fault Diagnosis for an Industrial High Pressure Leaching Process with a Monitoring Dashboard
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作者：Adriaan Haasbroek ; Johannes J. Strydom ; John T. McCoy 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：21
页码：117-122
DOI：10.1016/j.ifacol.2018.09.402
语种：English
出版社：Elsevier
摘要：AbstractAlthough fault detection and diagnosis are extensively studied and demonstrated on synthetic benchmarks, very few demonstrations of industrial application exist. In this paper, an investigation of faulty conditions in an industrial base metal refinery is conducted using standard statistical fault detection and diagnosis methods, and the IntelliSenzo™ monitoring dashboard. A workflow for offline fault investigation is described, and demonstrated on the industrial data, highlighting the need for careful record keeping, a structured approach, and iterative investigative steps. Potential propagation paths established links between the identified fault, process variables of interest, and engineering hypotheses, guiding the investigation and leading to recommendations of corrective action to the operating plant. The root cause of the fault (leaking and choked feed valves) were identified as potential causes in the investigation. Plant personnel confirmed that this structured approach, using process data, statistical techniques, and engineering knowledge, could speed up fault investigations, and reduce the costs of production losses and plant downtime.
关键词：KeywordsProcess MonitoringMineral ProcessingConcentratorsMachine LearningStatistical Inference