PCA-SDG based fault diagnosis on CAPL furnace temperature system

PCA-SDG based fault diagnosis method and its application on Continuous Annealing Process Line (CAPL) furnace temperature system are mainly discussed. Principle component analysis (PCA) method is applied to build the process monitoring model with a large number of historical data under normal operation conditions. High-dimension process data with noise and linear correlation are projected onto low-dimension and orthogonal sub-space. Real-time monitoring of furnace temperature system is carried out through online calculating T² and SPE statistics of PCA model. When a fault is detected, the signed directed graph (SDG) model of furnace temperature system is used to interpret the residual contributions of PCA model, and then perform fault diagnosis with the rules of SDG. PCA-SDG method combines the advantages of both PCA and SDG methods. The effectiveness and reliability of the proposed PCA-SDG method are verified by the simulations.