Inferring failure processes via causality analysis: from event logs to predictive fault trees

In the current Artificial Intelligence era, the integration of the Industry 4.0 paradigm in real-world settings requires robust and scientific methods and tools. Two concrete aims are the exploitation of large datasets and the guarantee of a proper level of explainability, demanded by critical systems and applications. Focusing on the predictive maintenance problem, this work leverages causality analysis to elicit knowledge about system failure processes. The result is a model expressed according to a newly introduced formalism: the Predictive Fault Trees. This model is enriched by causal relationships inferred from dependability-related event logs. The proposed approach considers both fault-error-failure chains between system components and the impact of environmental variables (e.g., temperature, pressure) on the health status of the components. A proof of concept shows the effectiveness of the methodology, leveraging an event-based simulator.