Case-Based Reasoning System for Aeroengine Fault Diagnosis Enhanced with Attitudinal Choquet Integral

As the core process of case-based reasoning (CBR), case retrieval is the foundation for CBR success, and the quality of case retrieval depends on the case similarity measure. We improved the CBR system for aeroengine fault diagnosis by embedding the attitudinal Choquet integral (ACI) and 2-order additive measure to consider attribute interactions and decision makers' attitudes. The enhanced case retrieval method can not only integrate the local similarity, attribute importance, and interaction between attributes, but also incorporate the attitude of the decision maker, thus producing more comprehensive and reasonable global similarity and high-quality recommendations. An experimental study of aeroengine fault diagnosis and comparisons with other similarity aggregation methods were performed to demonstrate the effectiveness of the proposed method.

Automated summary

Case retrieval is a critical process in case-based reasoning, and its quality relies on case similarity measures. This study proposes an improved case retrieval method for aeroengine fault diagnosis by incorporating attitudinal Choquet integral and 2-order additive measure to consider attribute interactions and decision makers' attitudes. The enhanced method integrates local similarity, attribute importance, attribute interactions, and decision makers' attitudes, leading to more comprehensive and reasonable global similarity and high-quality recommendations.