Assumption-based Design of Hybrid Diagnosis Systems

Abstract

Hybrid diagnosis systems combine model-based and data-driven methods to leverage their respective strengths and mitigate individual weaknesses in fault diagnosis. This paper proposes a unified framework for analyzing and designing hybrid diagnosis systems, focusing on the principles underlying the computation of diagnoses from observations. The framework emphasizes the importance of assumptions about fault modes and their manifestations in the system. The proposed architecture supports both fault decoupling and classification techniques, allowing for the flexible integration of model-based residuals and data-driven classifiers. Comparative analysis highlights how classical model-based and pure data-driven systems are special cases within the proposed hybrid framework. The proposed framework emphasizes that the key factor in categorizing fault diagnosis methods is not whether they are model-based or data-driven, but rather their ability to decuple faults which is crucial for rejecting diagnoses when fault training data is limited. Future research directions are suggested to further enhance hybrid fault diagnosis systems.