# A Fault Diagnosis Method of Mine Hoist Disc Brake System Based on Machine Learning
## 3 Data Acquisition and Pretreatment
### SPSS-Based Data Pretreatment  
SPSS is one of the current mainstream data analysis pieces of software, which can analyze the hoist operating state information contained in the data quickly. This analysis method can be applied to the processing and analysis of large amounts of data, providing a reliable scientific basis for analysts to make relevant decisions [19]. In the monitoring data's pretreatment of the brake system, the relationship between the monitoring attributes is analyzed by SPSS, which has a certain enlightenment for the selection of the characteristic attributes in the decision tree. The following are the main treatment methods:  
(1) Parameter test and interval estimation of data sets  
In the trend's estimation under the same working conditions, the estimation method of the confidence interval is usually adopted. In fact, the main body effect test and the estimation of the confidence interval can be realized conveniently in the SPSS. For example, by checking the main body effect between brake shoe clearance and the lifting velocity, hydraulic station oil temperature, and hydraulic station hydraulic pressure, it can be concluded that the brake shoe clearance has a significant influence on the lifting speed. As shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Monitoring Attributes** & **Unit** \\ \hline X1-X2 & Brake disc swing offset 1—Brake disc swing offset 2 & mm \\ X3-X10 & Brake shoe clearance 1—Brake shoe clearance8 & mm \\ X11-X18 & Brake force 1—Brake force 8 & N \\ X19-X27 & Brake shoe temperature 1—Brake shoe temperature 8 & \({}^{\circ}\)C \\ X28 & Hydraulic station oil pressure & MPa \\ X29 & Pine gate oil pressure & MPa \\ X30 & Hydraulic brake pressure & MPa \\ X31 & Hydraulic station residual pressure & MPa \\ X32 & Hydraulic station oil temperature & \({}^{\circ}\)C \\ X33 & Lifting velocity & m/s \\ X34 & Hoisting acceleration & m/s\({}^{2}\) \\ X35 & Percentage of contact area between brake disc and brake shoe & \% \\... &... &... \\ \hline \hline \end{tabular}
\end{table}
Table 1: Brake system monitoring parameters.  
2. Correlation analysis and regression analysis  
In data analysis, the correlation between parameters is usually analyzed, which mainly refers to the attribute relationship. For instance, models can be established under specific conditions or statistically compensated for missing values when dealing with missing values or prediction of fault. Faced with multi-attribute datasets in fault data, correlation analysis can establish correlations between attributes and make scientific estimates of future trends of data. Regression analysis mainly aims to predict the trend of future variables by optimizing the data over time and establish a functional relationship between variables. According to the analysis in SPSS, the lifting velocity and the acceleration have a linear correlation that can be used for dimensionality reduction. However, there is no correlation between the values of the brake shoe gaps. Therefore, this study only analyzes the fault of a single brake shoe by collecting data during the test.  
3. Cluster analysis  
The cluster analysis mainly classifies the variables with high similarity to one class by calculating the affinity relationship between variables and displays the classification results in different ways. Since SPSS has an automatically generated cluster analysis model, the system itself can standardize the data, which solves the problem of normalizing and standardizing data. Cluster analysis can at the same time achieve data discretization. In this study, SPSS software is used to discretize the K-means clustering [20] of data continuity attributes, which is seen in Section 5.2 with the fault diagnosis experiment.