LAUSR

With the increasing complexity and scale of modern industrial processes, there widely exist two-valued variables (TVs), such as status monitoring and numerical range variables. However, the traditional process monitoring approaches (such as principal component analysis and partial least square) are strongly based on continuous variables (CVs), thus they totally ignore the useful merit inherent in TVs. Recently, both CVs and TVs are used in combination for monitoring industrial processes for the first time. The mixed hidden naive Bayesian model (MHNBM) and feature-weighted mixed naive Bayes model (FWMNBM) have been proposed to enhance the monitoring performance by simultaneously and efficiently exploiting the valuable information of TVs and CVs. Nevertheless, both models are not suitable for nonstationary processes, which are consistent with the real property of many practical cases. Therefore, this article mainly proposes a novel self-learning FWMNBM (SL-FWMNBM) for nonstationary process monitoring. SL-FWMNBM constantly updates the model parameters in real time at the online detection stage to overcome the changes in the statistical characteristics of monitoring variables. It has the ability to mine process information carried by newly sampled data through self-learning, which is the main difference between SL-FWMNBM and the above two methods. The effectiveness of SL-FWMNBM is demonstrated through a simulation and an actual vibration case of the Zhoushan thermal power plant, China.