Gas anomaly detection algorithm merged with coal multi-dimensional time series data

Gas is a major threat to coal mine security cause of coal mine threat to security risk, it plays an important role to efficiently and precisely detect gas anomaly for the safety of coal mine production. Traditional gas anomaly detection methods depend solely on gas monitoring data which is often of low reliability due to poor conditions in coal mine and failure of sensors, resulting in false alarm and miss alarm. To solve the above problems, a novel gas anomaly detection algorithm fusing multi-dimensional time series data was proposed based on the time series data from multiple kinds of sensors. The multi-dimensional time series data was sampled with a sliding window, and the Local-sensitive Hashing Isolation Forest was trained with these samples. According to the data to be detected, the anomaly scores and rate were calculated based on the path length of each tree in the forest. When the anomaly rate was greater than the threshold, the forest would be automatically updated. Extensive experiments on real-world Huainan Zhuji coal mine data sets showed that the proposed method achieved higher detection accuracy. © 2020, Editorial Department of CIMS. All right reserved.