Adjustable Multimode Monitoring With Hybrid Variables and Its Application in a Thermal Power Plant

Multiple operating modes have become a key factor affecting the monitoring performance of practical industrial processes. The monitoring of multiple modes with hybrid variables (containing continuous and binary variables) is more intractable. In addition, the label information of the training data may be unavailable, and new modes may arrive or collected modes may disappear in continuous running of the system owing to the influence of production strategies, materials, loads, etc. Therefore, this article proposes an adjustable multimode monitoring with hybrid variables (AMMHV) model. In AMMHV, the expectation maximization algorithm is utilized for parameter estimation when the label information is unknown. AMMHV can not only effectively conduct the multimode process monitoring of hybrid variables without the label information of training samples, but also be updated without retraining when operation modes change. The incremental learning strategy is adopted to extend the model to give it outstanding monitoring performance for new arriving modes. If the originally collected modes no longer appear during operation, AMMHV can improve the monitoring accuracy of the remaining modes by condensing redundant irrelevant information. Finally, the superiority of AMMHV is fully demonstrated first on a numerical example and then on a process of a thermal power plant.