_applied_  
_sciences_  
_Article_  
**Data Processing Method of Mine Wind Speed Monitoring**  
**Based on an Improved Fuzzy C-Means Clustering Algorithm**  
**[PERSON]**\({}^{\copyright}\), [PERSON]**\({}^{\star}\) and [PERSON]**  
School of Safety and Emergency Management Engineering, Taiyuan University of Technology,  
Taiyuan 030002, China  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +86-18306895838  
**Abstract:** Analyzing and processing mine wind speed monitoring data is the key to realizing intelligent ventilation and real-time calculation of the ventilation network. According to the characteristics of the artificial regulation of a mine ventilation system, a local regression fuzzy C clustering algorithm is proposed in this paper, which combines local outlier processing with global air volume state analysis. Firstly, the algorithm uses the robust local weighted regression principle to analyze and preprocess the data locally, determines the risk degree of the abnormal data according to the identified times of outliers, determines the clustering number according to the clustering validity function, and analyzes the global air volume fluctuation according to the clustering results. The results show that most outliers are identified in data preprocessing. Still, the processing of dense outliers is weak, related to the window width setting and weighting multiple. The number of clusters can represent the fluctuation of the ventilation state and the pre-processed cluster centers are 4.4% lower than the original data because most of the outliers are higher than the average data. According to the law of air volume balance, the clustering results can pave the way for the global deduction of mine wind speed. There is an implicit relationship between data preprocessing and the clustering process, and when intensive outliers are not eliminated, they may be identified as separate clusters. The research of this paper points out the direction of mine wind speed data analysis, which can provide a theoretical basis for intelligent mine ventilation and real-time calculation of the ventilation network.  
wind speed anomalies; local regression; fuzzy clustering; cluster validity; wind state  
**1. Introduction**  
_I.1. Research Motivation_  
With the rapid application and popularization of artificial intelligence technology in various working fields worldwide, safety and intelligence will become a new mode for coal enterprises to improve industry competitiveness and sustainable development as one of the subsystems of intelligent mine construction [1]; the mine ventilation system puts forward higher requirements for the accurate calculation of airflow, stability of air network, reliable decision-making of disaster, and so on [2]. The mine ventilation system is still in the artificial or semi-artificial stage, far behind the open, intelligent design. It is imperative to break through the common problems of the industry and improve the intellectual level of mine ventilation [3]. The analysis and processing of mine wind speed monitoring data is the key to controlling ventilation state, real-time calculation of ventilation network, accident diagnosis, system optimization, and so on. A ventilation network is a dynamic balance system that is affected by mining, transportation, personnel activities, geological conditions, ventilation system regulation, and other factors; the data of wind speed sensors will have an abrupt short-term phenomenon [4]. It will also show long-term changes in the adjustment process of ventilation facilities [5]. To realize intelligent ventilation and improve the accuracy of ventilation network calculation, a data processing method is needed to eliminate the noise in the mine wind speed data and mine itself and analyze the long-term change information in the data.