Data Processing Method of Mine Wind Speed Monitoring Based on an Improved Fuzzy C-Means Clustering Algorithm

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.