Industrial Process Monitoring in the Big Data/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis

被引:195
作者
Reis, Marco S. [1 ]
Gins, Geert [2 ]
机构
[1] Univ Coimbra Polo II, Dept Chem Engn, CIEPQPF, Rua Silvio Lima, P-3030790 Coimbra, Portugal
[2] AIXIAL Belgium, Charleroise Steenweg 112, B-1060 Brussels, Belgium
关键词
industrial process monitoring; fault detection and diagnosis; prognosis; process health; equipment health; STATISTICAL PROCESS-CONTROL; ROOT CAUSE ANALYSIS; SENSITIVITY ENHANCING TRANSFORMATIONS; CONTROL CHART LIMITS; FAULT-DETECTION; BATCH PROCESSES; DYNAMIC PROCESSES; SOFT SENSORS; CAUSAL MAP; MULTIVARIATE;
D O I
10.3390/pr5030035
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its introduction almost 100 years ago. Several evolution trends that have been structuring IPM developments over this extended period of time are briefly referred, with more focus on data-driven approaches. We also argue that, besides such trends, the research focus has also evolved. The initial period was centred on optimizing IPM detection performance. More recently, root cause analysis and diagnosis gained importance and a variety of approaches were proposed to expand IPM with this new and important monitoring dimension. We believe that, in the future, the emphasis will be to bring yet another dimension to IPM: prognosis. Some perspectives are put forward in this regard, including the strong interplay of the Process and Maintenance departments, hitherto managed as separated silos.
引用
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页数:16
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