Abstract
Abnormal points in underground space monitoring data contain valuable information. An anomaly detection algorithm based on characteristic space is proposed in this document. First, based on a temporal edge operator, the edge amplitude of each point in time series data is calculated. The monitoring data curve is linearly segmented by selecting the points with larger edge amplitudes. Second, the data characteristics of each subsegment are extracted, and the original monitoring curve is mapped into the characteristic space. Finally, the abnormal features are identified by the local anomaly factor outlier algorithm, from which abnormal data points can be further obtained. The effectiveness of the proposed algorithm was first verified by identifying artificial abnormal data serials or discrete data point from a normal dataset. The feasibility and applicability of the algorithm was further verified by applying the developed algorithm to detect the anomaly of monitoring data for a real tunnel project.
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