A Hybrid Fuzzy Expert System Based on Module-type Database for Fault Diagnosis of Turbomachinery

모듈 구조 데이터베이스 기반의 터보기기 결함 진단용 하이브리드 퍼지 전문가 시스템

This paper suggests a fuzzy expert system for fault diagnosis of rotating machinery, based on modulated databases. In the proposed system, alarm and trip levels are set based on ISO, considering operating condition, machinery type and maintenance history. Input signals for diagnosis, such as sub- and super-harmonic components of vibration and mean value, are normalized from 0 to 1 under the threshold level and otherwise equal to one so that chronic faults slightly below the threshold level can be monitored. The database for diagnosis consists of two modules: the well-known Sohre's chart module and if-then type rules. The Sohre's chart is utilized for the most common problems of high-speed turbomachinery, while the rule-based module, which was collected from many papers and reports, is for diagnosing peculiar faults according to the type of machinery. To infer the results from two modules, a fuzzy operation of Yager sum was adopted. Using a simulator constructed in laboratory, experimental verification was performed for the cases of resonance and housing looseness which were intended. The experimental results show that the proposed fuzzy expert system has feasibility in practical diagnosis of rotating machinery.