# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
## 3 Mine Intelligent Ventilation Wind Speed Sensor Cleaning Model and Evaluation Metrics  
### Mine Intelligent Ventilation Wind Speed Sensor Cleaning Model  
Mine wind speed sensors provide real-time monitoring of wind speed information in the mine ventilation system. When a ventilation system failure occurs, the wind speed sensor data typically shows trend changes. Given the vast size of the entire ventilation system, the wind speed sensor monitoring data are often contaminated with system errors, outliers, and missing data. Missing data primarily include missing values, gaps, or incomplete segments. These issues reduce the usable information in the original data, affect its continuity, and cause problems such as abnormal alarms in the monitoring system, ultimately impacting normal industrial operations.  
The dynamic cleaning process for sensor data anomalies and missing values proposed in this paper is outlined as follows:  
Step1:Calculate the k-reachability distance and local reachability density of the sample data to obtain the LOF value \(L_{\text{e}}\) for each sample point. These LOF values are then sorted in ascending order to determine the threshold value \(L_{\text{m}}\). Historical fault data is analyzed and researched to determine the error tolerance time as 5, and the error duration as \(T_{\text{e}}\).  
Step 2: Use the GMM model to process the sorted LOF values, clustering the data based on the LOF values of normal and anomalous data. The GMM model will automatically partition the two types of data, and the boundary between the data points of the two clusters will serve as the threshold \(L_{\text{m}}\) for anomaly detection.  
Step 3: Analyze the LOF values of the sample data. 1 If \(L_{\text{e}}<L_{\text{m}}\), the test data corresponds to data from the normal operation of the ventilation system and does not require cleaning. 2 If some data satisfy \(L_{\text{e}}>L_{\text{m}}\) and \(T_{\text{e}}<T_{\text{m}}\), the test data corresponds to missing values or outliers generated during the normal operation of the ventilation system, and cleaning is required. 3 If some data satisfy \(L_{\text{e}}>L_{\text{m}}\) and \(T_{\text{e}}>T_{\text{m}}\), indicating that the data represents a fixed value or a missing value, the test data corresponds to missing values or system errors generated during the normal operation of the ventilation system, and cleaning is required. 4 If the LOF of the sample points shows a sustained increase or decrease, and some data satisfy \(L_{\text{e}}>L_{\text{m}}\) and \(T_{\text{e}}>T_{\text{m}}\), this data is considered to belong to a ventilation fault state and contains valid information, so it should be retained.  
Step 4: Remove the system errors, missing values, and outliers generated during the normal operation of the ventilation system, as identified in Step 3 (categories 2 and 3), to form missing points. These missing points are then input together with the original data samples into the SGAIN model. The SGAIN model is used to impute the missing points, ultimately generating a complete ventilation system wind speed dataset, ensuring the reliability and stability of the monitoring data.