Similarity-Measured Isolation Forest: Anomaly Detection Method for Machine Monitoring Data

Abstract

A rough environment or unexpected accident of data acquisition instrument can introduce some anomalies in monitoring data. Those anomalies reduce data quality and lead to the incorrect recognition of machine health status. However, the research on anomaly detection of machine monitoring data (MMD) is very scarce. Moreover, anomaly detection methods in other fields cannot be directly applied to MMD. Therefore, a robust anomaly detection method called similarity-measured isolation forest (SM-iForest) is proposed to detect abnormal segments and the data therein. The inadaptability and instability of iForest were reduced while processing MMD benefiting from the characteristics of sliding-window processing. Moreover, an anomaly identification stage measuring the relative similarity of possible abnormal segments further improved the robustness of iForest. The effectiveness of the proposed method was verified with a vibration simulation signal and three sets of milling force signals. The results demonstrate that SM-iForest can detect the missing, shifting, and swelling segments robustly. Detection results of comparing seven methods suggest that SM-iForest is a promising method to detect MMD anomaly with a high detection rate and low false alarm rate.