Feature Selection for Fault Detection and Prediction based on Event Log Analysis
This work addresses efficiency issues in fault detection for complex systems like lithography machines, but it is incremental as it builds on existing log analysis methods.
The paper tackled the problem of high computational cost in log-based anomaly detection for complex systems by developing a feature selection method, which improved effectiveness and efficiency.
Event logs are widely used for anomaly detection and prediction in complex systems. Existing log-based anomaly detection methods usually consist of four main steps: log collection, log parsing, feature extraction, and anomaly detection, wherein the feature extraction step extracts useful features for anomaly detection by counting log events. For a complex system, such as a lithography machine consisting of a large number of subsystems, its log may contain thousands of different events, resulting in abounding extracted features. However, when anomaly detection is performed at the subsystem level, analyzing all features becomes expensive and unnecessary. To mitigate this problem, we develop a feature selection method for log-based anomaly detection and prediction, largely improving the effectiveness and efficiency.