Detecting and Ranking Causal Anomalies in End-to-End Complex System
This work improves anomaly detection for automated monitoring in large-scale factories, though it appears incremental as it builds on prior methods like RCA.
The paper tackled the problem of identifying and ranking causal anomalies in complex systems by proposing the RCAE2E framework, which addresses limitations in existing methods like RCA by accounting for state diversity and time-lag correlations, and validated it using synthetic and real-world factory data.
With the rapid development of technology, the automated monitoring systems of large-scale factories are becoming more and more important. By collecting a large amount of machine sensor data, we can have many ways to find anomalies. We believe that the real core value of an automated monitoring system is to identify and track the cause of the problem. The most famous method for finding causal anomalies is RCA, but there are many problems that cannot be ignored. They used the AutoRegressive eXogenous (ARX) model to create a time-invariant correlation network as a machine profile, and then use this profile to track the causal anomalies by means of a method called fault propagation. There are two major problems in describing the behavior of a machine by using the correlation network established by ARX: (1) It does not take into account the diversity of states (2) It does not separately consider the correlations with different time-lag. Based on these problems, we propose a framework called Ranking Causal Anomalies in End-to-End System (RCAE2E), which completely solves the problems mentioned above. In the experimental part, we use synthetic data and real-world large-scale photoelectric factory data to verify the correctness and existence of our method hypothesis.