To overcome the false alarm problem that arises for mine wind-velocity sensors due to air-door and mine-car operation, a wind-velocity disturbance identification method based on the wavelet packet transform and gradient lifting decision tree is proposed. In this method, a multi-scale sliding window discretizes continuous wind-velocity monitoring data, the wavelet packet transform extracts the hidden features of discrete data, and a gradient lifting decision tree multi-disturbance classification model is established. Based on the overlap degree rule, the disturbance identification results are merged, modified, combined, and optimized. In accordance with a least absolute shrinkage and selection operator regression, the air-door operation information is further extracted. A similarity experiment is performed to verify the method performance. For the disturbance identification task, the recognition accuracy, accuracy, and recall of the proposed method are 94.58%, 95.70% and 92.99%, respectively, and for the task involving further extraction of disturbance information related to air-door operation, those values are 72.36%, 73.08%, and 71.02%, respectively. This algorithm gives a new recognition method for abnormal time series data.
Copyright: © 2023 Shang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.