Self-Healing Control for Multivariable Processes Based on Simple and Canonical Correlation Analyses

Abnormal working conditions often occur in modern industrial processes. Subsequently, if maintenance is not carried out in a timely manner, such abnormal working conditions can cause changes in system output variables, resulting in a decrease in product quality and even production accidents. This study proposes a self-healing control framework that extracts system input and output variable historical data under normal conditions. Then, based on simple correlation analyses and canonical correlation analyses, the correlation between historical input and output data is constructed, and the self-healing control law is calculated using the current batch of system output. The setpoint of the underlying controller and process variables are adjusted according to the control law to restore the system output variables to normal or near normal, thus ensuring the stability of product quality. This study demonstrates the convergence of system output variables and analyzes the computational complexity of the two algorithms. Finally, applied to water storage and continuous stirring reactor systems, the proposed self-healing control framework is validated to have a significant effect in restoring system output variables to normal or near normal, and presents significant safety and economic value in industrial production. Note to Practitioners—Abnormal conditions and fault conditions often occur in traditional complex industrial production processes, such as temperature, pressure fluctuations and pipeline blockages. When abnormal working conditions occur, certain key variables will fluctuate, which will have a huge impact on system safety and stable production. Therefore, this paper proposes a self-healing control system. The system can timely detect the occurrence of abnormal working conditions and make corresponding self-healing control decisions for abnormal working conditions. This system is compatible with several types of algorithms, and two different algorithms are designed in this article. Both algorithms can maintain the stability of key system output variables, and each has its own characteristics of adjustment and operation. In order to explore the application scope of the system, this paper carried out tests in various application scenarios and abnormal working conditions. Experimental results show that the proposed self-healing control system has significant self-healing effects in various industrial production processes.