LAUSR

This paper proposes a moving window recursive sparse principal component analysis (MWRSPCA)-based online fault monitoring scheme, aim at providing an online fault monitoring solution for large-scale complex industrial processes (e.g., chemical industry processes) with time-varying and dynamically changing characteristics. It establishes a sparse principal component analysis (SPCA) model based on the sliding window block matrixes to perform process monitoring and incorporates normal process monitoring data set simultaneously to the model training set to update the monitoring model online, so that the process monitoring model has strong adaptability to time-varying processes. A recursive computing procedure of the corresponding sparse loading matrixes is derived based on a modified rank-one matrix approximation algorithm, so that the computational complexity of the process monitoring model is greatly decreased and the real-time monitoring capability can be guaranteed. The effectiveness of the proposed method is verified by the benchmark Tennessee-Eastman process. Compared with traditional fault monitoring methods, the proposed method can effectively improve the fault detection accuracies with lower false alarm rates, which is suitable for the fault monitoring of time-varying, long-term and continuous complex industrial processes.