Fuzzy neural network machine prognosis

Patrick K. Simpson; Thomas M. Brotherton

doi:10.1117/12.211798

13 June 1995 Fuzzy neural network machine prognosis

Patrick K. Simpson, Thomas M. Brotherton

Proceedings Volume 2493, Applications of Fuzzy Logic Technology II; (1995) https://doi.org/10.1117/12.211798
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

The ability to predict failures in machinery before they occur would save time, money, and lives. The Army has several areas that would benefit from this ability. Mechanical components could be replaced before they caused catastrophic damage. Electronic components could be replaced in communication and weapon systems before they endangered a mission or lives. One area that would benefit immediately from this ability is predicting the fatique life of the Army's CH-47 helicopter. The CH-47 is a twin-rotor platform that depends on the reliability of its engine, transmissions, rotors, flight controls, and a myriad of other equipment. Predicting the fatigue life of a CH-47 would save the Army operation and support costs through spares elimination and more timely maintenance cycles. We have developed a methodology for a machine fatigue life predictor that utilizes a combination of parameter estimation, model generation, and condition identification. Using data collected from various fault conditions on the tail rotor assembly of a helicopter, we have simulated fatigue conditions and demonstrated the developed methodology.

Citation Download Citation

Patrick K. Simpson and Thomas M. Brotherton "Fuzzy neural network machine prognosis", Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, (13 June 1995); https://doi.org/10.1117/12.211798

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