Research on Online Condition monitoring for Complex System based on Modified Broad Learning Systems

Abstract

Complex systems contain numerous interacting components, thus deep learning methods with powerful performance and complex structure are often used to achieve condition monitoring. However, the deep learning methods are always too time-consuming and hardware-demanding to be loaded into complex systems for online training and updates. To achieve accurate and timely monitoring of complex system state, based on broad learning system (BLS), an online condition monitoring method is proposed in this paper. General BLSs are based on a randomly generated hidden-layer, usually perform poorly in high-dimensional data classification tasks. In this work, based on correlation and causality, two modified BLSs are proposed and mixed to establish the online monitoring system. Specifically, logistic regression (LR) and structural causal model (SCM) are considered to form rough predictions of the system state, thus to replace the randomly generated ones with no practical significance. The effectiveness of the proposed online monitoring method is verified by both simulation data and real data.