Yinghong Zhao1, Xiao He1, Junfeng Zhang1, Hongquan Ji2, Donghua Zhou2,1 and Michael G. Pecht3
1 Department of Automation, BNRist, Tsinghua University, Beijing 100084, China
2 College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
3 Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, USA
The moving average (MA)-type scheme, also known as the smoothing method, has been well established within the multivariate statistical process monitoring (MSPM) framework since the 1990s. However, its theoretical basis is still limited to smoothing independent data, and the optimality of its equally or exponentially weighted scheme remains unproven. This paper aims to weaken the independence assumption in the existing MA method, and then extend it to a broader area of dealing with autocorrelated weakly stationary processes. With the discovery of the non-optimality of the equally and exponentially weighted schemes used for fault detection when data have autocorrelation, the essence that they do not effectively utilize the correlation information of samples is revealed, giving birth to an optimally weighted moving average (OWMA) theory. The OWMA method is combined with the Hotelling’s T2 statistic to form an OWMA T2 control chart (OWMA-TCC), in order to detect a more challenging type of fault, i.e., intermittent fault (IF). Different from the MA scheme that puts an equal weight on samples within a time window, OWMA-TCC uses correlation (autocorrelation and cross-correlation) information to find an optimal weight vector (OWV) for the purpose of IF detection (IFD). In order to achieve a best IFD performance, the concept of IF detectability is defined and corresponding detectability conditions are provided, which further serve as selection criteria of the OWV. Then, the OWV is given in the form of a solution to nonlinear equations, whose existence is proven with the aid of the Brouwer fixed-point theory. Moreover, symmetrical structure of the OWV is revealed, and the optimality of the MA scheme for any IF directions when data exhibit no autocorrelation is proven. Finally, simulations on a numerical example and a continuous stirred tank reactor process are carried out to give a comprehensive comparison among OWMA-TCC and several existing static and dynamic MSPM methods. The results show a superior IFD performance of the developed methods.