Root cause analysis using anomaly detection and temporal informed causal graphs

In industrial processes, anomalies in the production equipment may lead to expensive failures. To avoid and avert such failures, the identification of the right root cause is crucial. Ideally, the search for a root cause is backed by causal information such as causal graphs. We have extended a framework that fuses causal graphs with anomaly detection to infer likely root causes. In this work, we add the use of temporal information to draw temporal valid conclusions about the potential propagation of anomalous information in causal graphs. The use of the framework is demonstrated on a robotic gripping process.