摘要：AbstractFor industrial processes, there are usually a number of measurement sensors equipped for monitoring and control purposes. In practice, sensors may suffer from the precision degradation phenomenon due to several aspects such as aging and ambient interference. This phenomenon may lead to imprecise or even incorrect control commands and indications, so the corresponding fault detection task is of vital importance. In this paper, inspired by the fact that precision degradation of a sensor can result in the increase of the variable’s variance, an algorithm based on second-order statistics analysis is proposed to accomplish the detection task for sensor precision degradation faults. By employing the sliding window technique, second-order statistics of process variables are first extracted. Then, conventional principal component analysis (PCA) is used as a dissimilarity quantification tool, with detection statistics and corresponding control limits established, to perform fault detection. Finally, simulations on a numerical example and the continuous stirred tank reactor (CSTR) benchmark process are performed to illustrate the effectiveness and advantages of the proposed method, in comparison with some existing methods such as PCA, dynamic PCA, and dissimilarity (DISSIM).
