Modified performance-enhanced PCA for incipient fault detection of dynamic industrial processes

Timely fault detection plays a critical role in modern complex industrial processes. While statistical process monitoring has gained significant practical application in recent years, traditional data-driven multivariate statistics often lack sensitivity in detecting incipient faults. To this end, this paper proposes a novel approach for fault detection of dynamic industrial processes. First, the process data are processed by the time series model, so as to separate and obtain corresponding static components. Then, a performance-enhanced principal component analysis method is employed to handle the static components. Besides, the detection index is further optimized to improve its detection performance. Finally, the proposed method’s effectiveness is illustrated through a numerical example and the continuous stirred tank reactor process. Moreover, the universality of the optimization algorithm which acts on the detection statistic is illustrated on the Tennessee Eastman process as well.