Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45399
Title: Inferring Failure Processes via Causality Analysis: from Event Logs to Predictive Fault Trees
Authors: De Fazio, Roberta
DEPAIRE, Benoit 
Marrone, Stefano
Verde, Laura
Issue Date: 2025
Source: Mathematics in AI and ML, Bari, Italy, 2025, January 29-31
Abstract: 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 [1] and the guarantee of a proper level of explainability, demanded by critical systems and applications [2]. 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 [3]. 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 [4]
Document URI: http://hdl.handle.net/1942/45399
Category: C2
Type: Conference Material
Appears in Collections:Research publications

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