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Data-driven prescriptive maintenance toward fault-tolerant multiparametric control
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DOI：10.1002/aic.17489.png)
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摘要
En 中文
Prescriptive maintenance can improve system effectiveness and system safety via integrated production and maintenance optimization. However due to system disruptions there is potential for abnormal operations and an undesirable increased occurrence of process safety incidents. This research provides a multiparametric-based framework for safety-aware, maintenance-aware, and disruption-aware process control. It leverages ensemble classification via machine learning classifiers for fault detection, mixed-integer nonlinear programming for integrated safety-aware production and maintenance scheduling, as well as hybrid multiparametric model predictive control for fault-tolerant setpoint tracking. The results show that the ensemble classifier outperforms the individual classifiers in terms of fault detection accuracy, sensitivity, and specificity. Furthermore, it is seen that the developed controllers are able to reconfigure the control actions based on process disruption information. The framework is illustrated with a chemical complex system, and a cooling water system. The approach can be used to help improve the safety and productivity of industrial processes.
Keyword:
fault detection
machine learning
maintenance scheduling
optimization
smart manufacturing
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4
论文数:
1.1W
被引数:
2.9W
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AUTOMATICAIF5.9
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