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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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