# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
## 5 Conclusions  
(1) The LOF algorithm and GMM algorithm were used to automatically determine the threshold of local anomaly factors in the dataset, identify outliers and missing data points, and detect ventilation system faults by analyzing the variation patterns of the local anomaly factors.  
(2) The optimal parameters for the SGAIN model were determined, with the number of iterations set to 1000 and the learning rate set to 5 \(\times\) 10\({}^{-2}\). The SGAIN algorithm was applied to impute the dataset processed by the LOF-GMM algorithm. The resulting RMSE and MAE values were 0.0579 and 0.0478, respectively, demonstrating higher imputation accuracy compared to models such as GAIN, RF, and DAE. Considering the high accuracy requirements for mine ventilation wind speed data, the SGAIN model is more suitable for application in the mining ventilation field.  
(3) The method proposed in this study was applied to process the mine ventilation wind speed sensor monitoring data, ensuring the accuracy and completeness of the monitored data and demonstrating significant value for engineering applications.  
This paper proposes a method for identifying and imputing anomalies and missing data points in mine ventilation wind speed sensor monitoring data based on the LOF-GMM and SGAIN models. The method was validated using actual mine data and achieved favorable results. The data used for the model consisted of field-measured data, where the presence of noise was uncertain. During data collection, the background noise was not significant. Considering time constraints, noise was manually added to the data to validate the model's effectiveness. The type of manually added noise is consistent with the noise observed in mine ventilation sensor monitoring. Under these conditions, the model demonstrated good performance. Regarding the noise issue, future research could investigate whether the model can achieve satisfactory performance in environments with severe noise. If the results fail to meet expectations, noise reduction techniques should be employed to distinguish between noise and anomalies, enabling subsequent model applications. Furthermore, the proposed model removes and repairs anomalous data in the original monitoring dataset while retaining valid information related to ventilation system faults. This effectively prevents false alarms in the mine intelligent ventilation monitoring system and enhances the safety of mine ventilation.  
Conceptualization, J.N. and S.Y.; methodology, S.Y.; software, S.Y.; validation, J.N. and S.Y.; formal analysis, J.N.; investigation, S.Y.; resources, Y.L.; data curation, J.N.; writing--original draft preparation, S.Y. and J.N.; writing--review and editing, J.N., S.Y. and Y.L.; visualization, S.Y.; supervision, Y.L.; project administration, J.N.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 52104194).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.