Combining Model‐Driven and Data‐Driven Approaches for Predictive Maintenance

Predictive maintenance is increasingly supported by data‐driven methods, which can effectively learn from operational data but may overlook important structural and behavioural knowledge about the monitored system. This paper presents H‐PdM, a hybrid method that combines information derived from system models with machine‐learning‐based analysis of operational data. The two sources provide complementary information that is combined to support fault diagnosis and maintenance decisions. The method is evaluated on a triplex pump digital twin considering different fault mechanisms and their combinations. Several machine learning classifiers are compared, and the final configuration is evaluated under non‐ideal operating conditions, including measurement noise, sensor bias, drift and missing data. The results show that the hybrid approach is more robust than either the data‐driven or model‐driven component used alone. An explainability analysis also shows how system‐derived rules and data‐driven features contribute differently depending on the detected fault. Overall, the results support the use of model and data‐driven information together to achieve more reliable and interpretable predictive maintenance.