## 1 Introduction
A quality-related process monitoring method based on parametric t-distributed stochastic neighbor embedding (P-t-SNE) and the modified minimum error minimax probability machine (MMEMPM) was proposed, which is applied to the hot strip production process. [PERSON] [22] proposed a hierarchical fault detection method based on local-global correlation characteristics for a multi-process industrial process, which improved the accuracy and reliability of process monitoring. [PERSON] [23] proposed a new monitoring technique using the CVA with UCLs derived from the estimated probability density function through kernel density estimations (KDEs). This method significantly improves the monitoring performance of the system. [PERSON] [24] proposed a hybrid kernel CVDA method for early fault detection of nonlinear dynamic processes. Due to the use of mixed kernels, the detection sensitivity is improved. The kernel mapping is a popular method for designing nonlinear process monitoring techniques. However, the order of the nonlinear relationship between process variables in an industrial process system is often limited by many physical constraints. This kind of infinite order mapping may be redundant and inefficient. Therefore, [PERSON] [25] proposed a nonlinear mapping method by constructing polynomial mapping (CPM) instead of a radial basis kernel function, which avoided too much modeling redundancy. [PERSON] [26] proposed a novel nonlinear full-condition process monitoring model to monitor the idle condition. In addition, the nonlinear cointegration analysis (NCA) is used to reveal the long-term dynamic relationship of non-stationary components. [PERSON] [27] used a transfer entropy algorithm to analyze the causal relationship between the candidate fault variables and find the exact root of the fault. The above data-driven process monitoring methods in the algorithm expansion research are constantly moving to a higher level. Its algorithm model has been effectively verified through simulation. However, the excessive attention to the advanced nature of the algorithm has deviated from the basic metallurgical production background and process. Therefore, in order to ensure that the monitoring model is more suitable for the actual rolling conditions, the research of a data-driven and mechanism fusion model has become a hot direction in the field of process monitoring [28; 29]. [PERSON] [30] analyzed the mechanism correlation between variables in the chemical process and obtained a causal relationship between variables. Combined with the causal relationship between variables, the Bayesian network monitoring model was constructed. [PERSON] [31] combined a shape control mechanism with a DE-ELM neural network. A shape control mechanism and intelligent collaborative control model was established to improve the efficiency and precision of shape control. [PERSON] [32] integrated industrial big data and a metallurgical mechanism and explored the interaction between influencing factors. A global additive hot strip performance prediction model was proposed. Motivated by the above introduction, it is considered that the data model has insufficient ability to explain some variables in the monitoring process [33]. In the complex strip rolling process, the traditional quality-related fault diagnosis methods only rely on the correlation between mining data to identify quality anomalies. In data processing, normalization eliminates the influence of dimension and ignores the influence of various variables on the quality of the strip production process. Therefore, a fault diagnosis method for strip quality-related faults considering mechanism and data fusion was proposed. In this paper, the thickness control mechanism model of the rolling process was used to optimize the data model. A kernel partial least squares monitoring method based on influence weight optimization (W-KPLS) was proposed. The rest of this paper is structured as follows. The process monitoring model based on KPLS is first introduced in Section 2.