  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.
