Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme

Fault diagnosis requires a classification system that can distinguish between different faults based on observed symptoms of the process under investigation. Since the fault symptom relationships are not always known beforehand, a system is needed which can be learned from experimental or simulated data. A fuzzy logic based diagnosis is advantageous. It allows an easy incorporation of a-priori known rules and also enables the user to understand the inference of the system. In this contribution, a new diagnosis scheme is presented and applied to a DC motor. The approach is based on a combination of structural a-priori knowledge and measured data in order to create a hierarchical diagnosis system that can be adapted to different motors. Advantages of the system are its high degree of transparency and an increased robustness.