2026
Authors
Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Pasquali, A; Moniz, N; Jorge, AM; Soares, C; Abreu, PH; Gama, J;
Publication
ECML/PKDD (10)
Abstract
2025
Authors
Barbosa, I; Gama, J; Veloso, B;
Publication
Progress in Artificial Intelligence - 24th EPIA Conference on Artificial Intelligence, EPIA 2025, Faro, Portugal, October 1-3, 2025, Proceedings, Part II
Abstract
Predictive Maintenance (PdM) aims to prevent failures through early detection, yet lacks explainability to support decision-making. Current PdM models often identify failures, but fail to explain their root causes, especially in real-world scenarios, with complex and limited labeled data. This study proposes an interpretable framework that combines LSTM-based Anomaly Detection with a dual-layered Root Cause Analysis (RCA) based on SHAP attributions. Applied to a real-world dataset, the method detects degradation transitions, tracks failure patterns over time, and provides interpretable information without explicit root cause labels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
2025
Authors
Bécue, A; Gama, J; Brito, PQ;
Publication
Strategic Business Research
Abstract
2025
Authors
Oliveira, HF; de Faria, ER; Gama, J; Khan, L; Cerri, R;
Publication
CoRR
Abstract
2026
Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;
Publication
ECML/PKDD (7)
Abstract
2026
Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;
Publication
ECML/PKDD (6)
Abstract
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