Quality-related fault detection has been a new research hotspot in recent years. It aims to the higher fault detection rate for quality-related faults and the lower fault detection rate for quality-unrelated faults. The traditional principal component analysis (PCA) will alarm all faults and can't satisfy the above requirements

which will cause lots of downtime and seriously affect the normal production. The quality variables usually are not easy to measure online in actual industrial production. So this paper proposed the weighted mutual information principal component analysis (WMIPCA) to solve these problems. Firstly

the supervision relationship between process variables and quality variables is established via mutual information and Bayesian Inference. Then a set of process variables that contain the largest amount of quality variable information is selected and the PCA is modeled on them. After that

the principal components containing more information on the quality variables are selected and used to establish the statistics and monitor the process. Finally

the feasibility and effectiveness of the WMIPCA are verified by experiments.