# Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling
## 3 Root-Cause-Tracing Algorithm
### Construction of Root-Cause-Tracing Algorithm  
During the rolling process, if the EPAE score for the looper is observed to be low, a root-cause-tracing algorithm is designed based on the EPAE model to find the reason from material factors, equipment factors, control factors, etc., enabling swift identification of issues after an abnormality occurs. When an abnormality occurs in the looper, since there are many factors involved and their proportions are different, it is necessary to construct a suitable equation set, calculate the weight of each factor, and give the most likely reason for the low looper score. Most of the previous classification algorithms used existing data as a reference system, selected appropriate classifiers to fully explore the intrinsic relationships between the data, and established prediction functions. When a new sample comes in, the fault location is determined through the previously established function. The root-cause-tracing algorithm proposed in this paper selects old samples similar to a new sample when entering it into the database and combines the two phases to construct a system of equations to find the source of the fault. This method does not use all the previous data, but selects it selectively, so it has the characteristics of being \"lazy\" [25]. And this root-cause-tracing algorithm not only refers to past experience, but also fully combines the current situation, so can effectively deal with new sudden failures. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment** & **Correlation Coefficient** & \(p\)**-Value** \\ \hline Side Guide & \(-0.768\) & \(4.087\times 10^{-5}\) \\ \hline Looper & \(-0.113\) & \(0.382\) \\ \hline Automatic Gauge Control & \(-0.458\) & \(0.009\) \\ \hline Bending Roller & \(-0.855\) & \(6.627\times 10^{-16}\) \\ \hline Shifting Roller & \(-0.356\) & \(0.024\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Process accuracy evaluation and finish rolling stability. Algorithm Construction Technology  
(1) Data preprocessing  
The steel coil and its corresponding LP_SCOREALL (average score of 6 loopers) is extracted from the original data set, as well as factors related to loopers such as FORCERATE_BODY, FORCERATE_HEAD. Afterwards, missing values and outliers in the data are eliminated or supplemented to improve data quality and facilitate subsequent analysis. (2) Calculate the average  
There are many factors involved in the looper during the hot strip rolling process, and each factor has multiple measured values. Therefore, in order to reduce data redundancy and simplify calculation complexity, the average values of these data are subsequently used as the factor eigenvalues. \[x=\frac{\sum_{i=1}^{n}s_{i}}{n} \tag{20}\]  
where \(s\) is the score corresponding to different aspects of a certain factor, and \(x\) is the average value, which is the characteristic value. (3) Mean deviation  
In order to indicate the quality of the sample, it needs to be compared with the standard value, and the deviation between the two \(\Delta x=x-x_{\text{std}}\) is calculated as the basis for judgment. In the absence of an exact standard value, the looper group with a higher score is identified and its characteristic value is substituted with the empirical standard value. (4) Data normalization  
The measurement units and magnitudes of the corresponding characteristic values of the loopers are different, making the indicators incomparable. Therefore, before data analysis, it is necessary to eliminate the influence of dimensions between eigenvalues. All features are unified into approximately the same numerical range so that indicators of different magnitudes can be weighted and compared. The normalization method is used to linearly map the original feature data to the interval [0, 1]. \[x^{{}^{\prime}}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{21}\]  
where \(x_{\text{max}},x_{\text{min}}\), respectively, correspond to the maximum value and minimum value of a certain influencing factor of the looper.