## 1 Introduction  
For complex modern industrial processes, quality-related fault process monitoring is of great significance [1]. Due to the increasing demand for stable product performance, better quality and lower product rejection rates, modern industrial processes become more and more complicated by integrating various functions and interacting control loops [2]. Accordingly, in order to ensure the stability of product quality, it is necessary to monitor the fluctuation of quality-related variables, which have recently attracted more attention both in academia and engineering domains. For such purposes, quality-related process monitoring and diagnosis methods have been deeply studied, and a lot of fruitful results have been achieved [3; 4; 5; 6]. However, the analytical models are difficult to develop in nonlinear, dynamic and strong coupling complex industrial systems, such as steel rolling, chemical industry and so on. In contrast, data-based methods are popular because of their simple form and low requirements for design and engineering work [1; 7]. At present, in the multi-process strip production process, thickness is one of the most important quality indexes in the process of strip production. However, it is difficult to effectively monitor the fluctuation of thickness and other related variables in the actual production process. With the development of data technology, the data-driven process monitoring algorithm is gradually applied to the monitoring process of the complex industry. The establishment of information monitoring, behavior characteristic modeling and diagnosis system for quality-related variables in the rolling process has become an urgent demand in strip production [8; 9]. Data-driven methods are applied to monitor and diagnose complex industrial processes. In the research of multivariate statistical monitoring, the partial least squares (PLS) monitoring model can retain the information of key quality variables [10; 11; 12]. Therefore, PLS and its extended model become one of the most suitable methods for quality-related process monitoring research [13; 14]. A novel quality-driven kernel projection to latent structure (QKPLS) modeling scheme was proposed for concurrent quality-related and process-fault detection for nonlinear processes by [PERSON] [15]. In the extended model, [PERSON] et al. [16] decomposed the principal components and residual into four subspaces, and a model of total projection to latent structures for process monitoring was constructed. This method reduces the missed detection rate. Because the T-PLS algorithm cannot monitor the unpredictable quality changes, [PERSON] et al. [17] further decomposed the predicted quality space by singular value decomposition (SVD) and principal component analysis (PCA); then the latent structure parallel projection (C-PLS) algorithm was proposed. [PERSON] et al. [18] proposed a decentralized fault diagnosis method based on multi-block kernel partial least squares (MBKPLS), which extends MBPLS to monitor large-scale nonlinear processes. Consequently, the model can capture more useful information within and between blocks, which is applied to the process monitoring of a continuous annealing process. According to the multi-scale and time-varying characteristics of the system, [PERSON] et al. [19] introduced the self-adaptive model updating method into the MKPLS method and proposed a self-adaptive multi-scale kernel partial least squares regression (SMKPLS) model. [PERSON] et al. [20] proposed a novel data-driven process monitoring method named Fisher discriminant global-local preserving projection (FDGLPP). The method preserves the global manifold structure and local neighborhood structure of the data set and reduces false positives and detection delay. [PERSON] et al. [21] studied the connections between different types of strip-steel products instead of conventional multimode methods.