期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 47, 期 5, 页码 1070-1077出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/41.873215
关键词
DC motors; fault diagnosis; fault trees; fuzzy logic; fuzzy neural networks; monitoring; neural networks; supervision
A fault diagnosis system contains 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 required 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 enables the user to understand the inference of the system. In this paper, a new diagnosis scheme is presented and applied to a de motor. The approach is based on the 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 transparency and an increased robustness over traditional classification schemes.
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