# Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling
## 1 Introduction  
In the contemporary industrial, agricultural, and construction sectors, hot-rolled strip products have become increasingly vital, leading to their production volume representing a significant portion of total steel output for steel companies. This has positioned the hot-rolled production line as a foundational element for these companies [1]. Advancements in traditional production methods, combined with the progression of computer technology, have markedly increased automation within the hot continuous rolling process [2], enhancing both the volume and quality of rolling output. Nevertheless, there has been a growing demand for higher quality in hot-rolled strip products in recent years. This surge in demand imposes more rigorous requirements on the hot-rolling production process and the precision of the equipment used. The challenge of improving the quality of plates and strips while simultaneously reducing the scrap rate represents a crucial developmental direction for steel companies and the plate and strip production industry [3]. As shown in Figure 1, the hot rolling process is inherently complex, exhibiting dynamic and nonlinear attributes. It involves numerous control loops and thousands of process variables that critically influence product quality, epitomizing a sophisticated industrial process [4]. When issues arise in any part of the process or equipment, it can significantly impact the entire production line, ultimately affecting the company's profitability. Therefore, promptly identifying and analyzing these problematic links or equipment is essential. Modern hot tandem rolling technology is characterized by its complexity, particularly in controlling temperature and rolling force [5], which can lead to fluctuations in product quality. To improve the stability of hot continuous rolling production, typical strategies involve focusing on critical stability-affecting factors and exploring enhancements for processes and equipment performance. Various research methodologies have been employed to address these challenges. For instance, a data-driven dynamic concurrent kernel canonical correlation analysis (DCKCCA) method was utilized for diagnosing CAP-thickness-related faults [6]. Additionally, ref. [7] developed a fault diagnosis method using two-dimensional time-frequency images and data enhancement, training a convolutional neural network (CNN)-based model for this purpose. A combination of continuous wavelet transform and a deep convolutional generative adversarial network (DCGAN) was proposed for tackling uneven data distribution in rolling bearing fault diagnosis [8]. Modified independent component analysis (MICA) was used to construct a multivariate statistical process monitoring model for detecting and analyzing chatter in hot strip mill processes [9]. Furthermore, a data-driven key performance indicator (KPI) prediction and diagnosis scheme was developed [10], offering a simplified alternative to the standard partial least squares (PLS) method. However, most existing methods primarily focus on surface data characteristics, inferring the superficial causes of faults based on data traits. Consequently, while these methods can locate the faulty link or equipment, they often fail to identify the underlying root cause. This paper seeks to address this limitation by analyzing the problem from the perspectives of production continuity, product quality stability, and extreme specification production capacity. By examining the issue from exterior to interior layers, this study aims to identify the fundamental cause of faults. Specifically, the finishing rolling area of the hot tandem rolling production line is used as a case study, categorizing production stability factors into material, equipment, and control aspects in Table 1.  
There is much related equipment involved in the finishing rolling area, and the looper can ensure the stability of the strip during the rolling process, control the shape and size of the strip, and reduce surface defects of the strip, etc.