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Interpretable Predictive Maintenance: Combining Anomaly Detection with Quantitative Root Cause Analysis
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AI
DOI：10.1007/978-3-032-05179-0_18.png)
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Abstract
En 中文
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.
Keywords:
Predictive Maintenance
Anomaly Detection
Root Cause Analysis
Explainability
Journal
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Papers:
36
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Cited Papers
ShareSaveDeep learning models for predictive maintenance: a survey, comparison, challenges and prospects.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
ShareSaveSCANIA Component X dataset: a real-world multivariate time series dataset for predictive maintenanceSCIENTIFIC DATA
OAAI
SCIENTIFIC DATAIF6.9
OAAIShareSave
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