Batch processes are very important in most industries and are used to produce high-value-added products

which causes their monitoring and control to emerge as essential techniques. Several multivariate statistical analyses

including multi-way principal component analysis (MPCA)

have been developed for the monitoring and fault detection of batch processes. In this paper

an improved statistical batch monitoring and fault diagnosing approach based on variable-wise unfolding was proposed to overcome the drawbacks of traditional MPCA and the AT method proposed by Aguado. The proposed method did not require prediction of the future values while the dynamic relations of data were preserved by using time-varying score covariance

and principal-component-related variable residual statistics was introduced to replace SPE-statistics

thus avoiding the conservation of SPE statistical test and providing more explicit information about the process conditions. As a result

the root cause that violated the Hotelling T2 test but still satisfied the SPE test could be unambiguously identified

which was impossible in the MPCA. In addition

time-varying contribution charts were proposed to diagnose anomalous batch process. The proposed method was applied to detecting and identifying faults in the simulation benchmark of fed-batch penicillin production. The simulation results clearly demonstrated the power and advantages of the proposed method in comparison to the MPCA and AT method.