工矿自动化2025，Vol.51Issue(12)：10-15,26,7.DOI:10.13272/j.issn.1671-251x.2025070097
煤矿传感器数据异常检测方法
Anomaly detection method for coal mine sensor data
摘要
Abstract
In response to persistent dense noise anomalies,instantaneous impulse anomalies,and missing anomalies in sensor data caused by the complex underground environment of coal mines,existing data anomaly detection methods have difficulty adapting to nonlinear time-series fluctuations,exhibit high false alarm rates when processing high-frequency data,and rely heavily on large amounts of labeled samples.To address these issues,a coal mine sensor data anomaly detection method was proposed.First,Z-score normalization was used to eliminate dimensional differences in sensor data.Second,proximity-based anomaly detection methods—the distance-based K-Nearest Neighbors(KNN)algorithm and the density-based Local Outlier Factor(LOF)algorithm-were used to perform preliminary anomaly screening and assign anomaly labels.Meanwhile,temporal features including lag features,statistical features,differential features,Fast Fourier Transform(FFT)features,and time-encoding features were extracted using dual-scale sliding windows and concatenated to form a feature matrix.Then,the feature matrix and corresponding anomaly labels were used to construct the sample set required for the eXtreme Gradient Boosting(XGBoost)model.The sample set was divided into training,validation,and test sets according to the temporal order of the data,and the XGBoost model was trained using the training set after negative-sample undersampling.Finally,the trained XGBoost model was used to compute the anomaly probability of each sample in the validation set,and a Precision-Recall(PR)curve was plotted.The anomaly probability that maximized the F1 score was selected as the anomaly decision threshold,and sample points with anomaly probabilities greater than or equal to the threshold were labeled as anomalies,thereby outputting sensor anomaly data.Experimental results show that the proposed method has high anomaly detection accuracy and can maintain stable detection performance under different data distributions and noise environments,demonstrating good generalization capability.关键词
传感器数据异常检测/基于邻近性的异常检测/时序特征/XGBoost/K最近邻/局部异常因子Key words
sensor data anomaly detection/proximity-based anomaly detection/temporal features/XGBoost/K-nearest neighbors/local outlier factor分类
矿业与冶金引用本文复制引用
杨宇琪,付翔,张智星,闫明,李浩杰,刘彬,牛鹏昊,黄金宇..煤矿传感器数据异常检测方法[J].工矿自动化,2025,51(12):10-15,26,7.基金项目
山西省基础研究计划联合资助项目(202403011241002) （202403011241002）
国家自然科学基金项目(52274157) （52274157）
"科技兴蒙"行动重点专项项目(2022EEDSKJXM010). （2022EEDSKJXM010）
