Data-driven statistical process monitoring approach

including principal component analysis(PCA) and partial least square(PLS)

has been widely used in chemical process monitoring and fault detection.Sensor fault diagnosis algorithms based on signal reconstruction were explored and a general formulation of signal reconstruction algorithm was presented.The fault isolation criteria of model space and residual spaced were defined and the sufficient and necessary conditions to detect and isolate sensor faults were given.Different signal reconstruction approaches were compared in detail with a CSTR simulation application.