# Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling
## 1 Introduction
[11]; so, here we take the looper as a case study to trace its root cause. When identifying a low EPAE score for the looper during production, as Figure 2 shows, instead of examining surface-level faults and errors such as looper angle and looper tension, the focus shifts towards investigating the more profound issues depicted in the figure below. Based on the EPAE model, this paper designs a root-cause-traceability algorithm to analyze material factors, equipment factors, and \begin{table}
\begin{tabular}{c c} \hline \hline
**Object** & **Name** \\ \hline \multirow{2}{*}{Control factors} & Final temperature hit \\  & Plate type \\  & Mechanical equipment \\ \hline \multirow{3}{*}{Material factors} & Roll gap setting accuracy \\  & Surface quality \\  & Electrical equipment \\ \hline \multirow{3}{*}{Equipment factors} & Roll force setting accuracy \\  & Width, thickness \\ \cline{1-1}  & Water, gas, and thermal equipment \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Factors related to production stability in finishing rolling area. Figure 1: Hot rolling process. control factors, to ensure that the root cause of the problem can be quickly located after an abnormality occurs. This paper considers the problem of root cause traceability in looper equipment under the framework of production stability in the hot rolling area, inspired by the [12; 13; 14]. First, this paper elucidates the physical structure and operational principles of the looper. Subsequently, it delineates the precision indices for controlling the looper in each operational process and establishes the EPAE model for the looper. Then, an analysis is conducted to explore the relationship between the EPAE model of the looper and production stability. Based on this, a root-cause-traceability algorithm is proposed. In addition, actual data pertaining to loopers within the 2250 hot rolling equipment production line were used to trace the root cause, identifying the primary factors most likely contributing to a low looper score. The main contributions of the paper can be summarized as the following points. * This paper initiates its approach from the foundational layer of production stability and analyzes problems that may arise in the production process, which is different from most existing work on fault diagnosis. * An EPAE model based on the actual working process of the looper is proposed. This model aims to improve the interpretability of subsequent causal relationship modeling. * A root-cause-tracing algorithm is proposed and its viability is assessed by using available actual production data. The root-cause-tracing algorithm is a brand-new diagnostic algorithm proposed by this paper for industrial processes. It consists of data processing, building neural networks, and calculating weights. Different from previous fault diagnosis algorithms, this algorithm is committed to finding deeper problems, rather than just locating the device where the fault occurred. The calculation of weights is the highlight of this algorithm, this part converts the problem of fault location into a problem of solving a system of equations, and converts the possibility of fault occurrence into the weight of each eigenvalue. And when constructing the system of equations, both new data and past data are used to construct the system of equations. With past data as a reference, the solution is more reliable. The remainder of the paper is structured as follows: Section 2 starts from the control accuracy of the looper and then constructs the EPAE model of the looper; Section 3 analyzes the relationship between the EPAE score and production stability and proposes a root-cause-traceability algorithm; Section 4 uses existing data to verify the root-cause-tracing algorithm and analyze its feasibility; Section 5 summarizes the findings of this paper and suggests some potential future directions.