Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models
Yannick Wilhelm, Peter Reimann, Wolfgang Gauchel, Bernhard Mitschang
Abstract
In this paper, we review hybrid approaches for fault detection and fault diagnosis (FDD) that combine data-driven analysis with physics-based and knowledge-based models to overcome a lack of data and to increase the FDD accuracy. We categorize these hybrid approaches according to the steps of an extended common workflow for FDD. This gives practitioners indications of which kind of hybrid FDD approach they can use in their application.
Topics & Concepts
WorkflowFault detection and isolationFault (geology)Computer scienceCategorizationData miningHybrid systemSystems engineeringMachine learningArtificial intelligenceEngineeringDatabaseSeismologyActuatorGeologyFault Detection and Control SystemsMineral Processing and GrindingMachine Fault Diagnosis Techniques