A Novel Three-Stage Quality Oriented Data-Driven Nonlinear Industrial Process Monitoring Strategy

Abstract

It is well known that the partial least squares (PLS) and its nonlinear extension kernel PLS (KPLS) are efficient tools widely utilized in fault detection. Nonetheless, these two techniques project obliquely into the process variable space, limiting their ability to discriminate between the quality-related and quality-unrelated faults. Although various enhanced ways based on the basic PLS and KPLS, such as the total PLS and total KPLS methods, have been published to cope with this problem, these methods fail to reduce the false alarm rates of the quality-unrelated fault when the fault amplitude increases, and they need to monitor four statistics to determine the fault categories. To address this issue, this paper proposes a three-stage data-driven method for complicated nonlinear industrial quality-related process monitoring, consisting of three parts: data preprocessing, modeling, and decomposing. The kernel direct orthogonalization (KDO) approach is initially investigated in this strategy, which is based on the traditional direct orthogonalization methodology and the kernel theory. The unnecessary fluctuations in the process variable space are eliminated after the data is preprocessed using the KDO approach. A standard KPLS model is built for the filtered component. Then, a quality-related KPLS (QRKPLS) method is proposed as the decomposing part to further divide the filtered process mapping matrix into two orthogonal parts, namely, the quality-related part and the quality-unrelated part. Additionally, the corresponding test statistics are calculated for these two parts, and the diagnosis logic is also given in this paper. The effectiveness and superiority of the novel established KDO-QRKPLS method is investigated using a widely numerical simulation as well as an industrial simulation.