# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
## 1 Introduction  
Against the backdrop of intelligent mine ventilation development, the construction of intelligent mine ventilation monitoring systems has become a crucial component in the process of mine intelligence development [1]. In intelligent mine ventilation monitoring systems, wind speed sensors play a vital role. The data monitored by these sensors provide critical support for real-time network calculations of mine ventilation, ventilation system state analysis, fault diagnosis, and decision making during disaster periods [2, 3, 4]. Under normal operating conditions of the ventilation system, the complex underground environment, including dust, smoke, humidity changes, unstable power supply or insufficient electricity, and electromagnetic interference from equipment, can cause sensor monitoring values to be abnormal or missing [5, 6]. Mine collapses, roof falls in tunnels, damage to ventilation doors or windows, and blockages in tunnels can lead to abnormal sensor monitoring data. Such abnormalities often manifest as trends or overall shifts in the data, indicating ventilation system faults that impact its normal operation [7]. Therefore, removing and filling abnormal or missing wind speed sensor monitoring values under normal ventilation system conditions, as well as promptly identifying ventilation system faults, are of great importance for the construction of mine ventilation monitoring systems. Currently, data cleaning methods for monitoring data have been widely applied in the fields of distribution networks and power systems. In terms of anomaly detection, researchers have employed various models to identify anomalies and faults in different types of data, including motor wind turbine (WT) data [8], SCMA signals data [9], power generation statistics [10], and transformer DC bias synchronous monitoring data [11]. [PERSON] et al. [12] provide a comparative evaluation of various unsupervised anomaly detection algorithms for multivariate data, offering recommendations for algorithm application and selection. For data imputation, which addresses missing data issues arising during transmission and collection, [PERSON] et al. [13] proposed an MCMC-based method for imputing missing microgrid photovoltaic data, while [PERSON] et al. [14] used chained equation multiple imputation to handle missing mine ventilation parameters. [PERSON] et al. [15] and [PERSON] et al. [16] employed generative adversarial networks (GANs) to impute missing turning energy consumption data and missing bearing test data, respectively. These researchers have applied different methods in their respective fields to ensure data integrity by imputing missing values. However, anomaly detection and missing data imputation are typically carried out separately. To ensure data accuracy and completeness, [PERSON] et al. [17] used CFSFDP to identify anomalies in substation equipment detection data and applied an LSTM neural network to correct \"dirty data\", achieving integrated anomaly detection and data cleaning for substation equipment. [PERSON] et al. [18] proposed a dynamic data cleaning method based on machine learning for the detection and imputation of abnormal and missing data in distribution networks, constructing an integrated dynamic cleaning framework. [PERSON] et al. [19] introduced an anomaly cleaning method for large energy data based on sparse autoencoders, achieving effective cleaning of anomalous energy big data. However, research on integrated anomaly detection and data cleaning for mine ventilation monitoring data remains relatively scarce. [PERSON] [20] improved the Laiida criterion to classify real-time wind speed measurements and removed data identified as anomalies, which impacted the continuity and completeness of the data. [PERSON] et al. [21] proposed a mine wind speed sensor monitoring data cleaning model based on a stacked denoising autoencoder (SDAE). This model automatically detects noise points and missing values, and it repairs \"dirty\" data while effectively preserving fault information.