This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Engine Health Monitoring (EHM) System for Advanced Diagnostic Monitoring of Gas Turbine Engines
Annotation ability available
Sector:
Language:
English
Abstract
Advancing the USAF's capabilities in engine life measurement and diagnostic monitoring of critical engine components is necessary to improve engine availability, minimize performance degradation, and reduce life cycle costs. Proven artificial intelligence (AI) technologies such as neural networks, fuzzy logic and expert systems present an opportunity to significantly enhance current trending and diagnostic capabilities in a real-time monitoring environment.
This paper outlines the strategy adopted by the USAF to develop a state-of-the-art engine health monitoring system. In addition, the status of an R&D program whose ultimate aim is to demonstrate the perceived capability is also discussed. Engine data currently sensed and recorded for post flight processing will be analyzed in a continuous real-time mode. For fault detection and accommodation, extensive knowledge of how a healthy engine operates under given conditions will be analyzed, and any deviation from this “normal” pattern of expected parameters will be detected and further analyzed. Faults resulting from sensor failure modes will be promptly isolated and more complex faults will be identified by reasoning utilizing fuzzy logic and pattern recognition schemes. The same sensed data will be used as inputs to the life measurement module of the monitoring system where life usage algorithms will determine critical component remaining life based on actual mission severity. The system under development will be based on the Rolls-Royce T45 engine (Adour) which is fitted to the Navy's F-405 trainer, and a full-scale demonstration of the technology will ultimately be conducted on this engine.
Recommended Content
Authors
Topic
Citation
Roemer, M. and Pomfret, C., "Engine Health Monitoring (EHM) System for Advanced Diagnostic Monitoring of Gas Turbine Engines," SAE Technical Paper 961305, 1996, https://doi.org/10.4271/961305.Also In
References
- Walker, N. D. Wyatt-Mair, G. F. Sensor Signal Validation Using Analytical Redundancy for an Aluminum Cold Rolling Mill” Control Engineering Practice 3 6 June 1995 753 760
- MvAvoy, T. J. “Sensor Data Analysis Using Autoassociateive Neural Nets” World Congress on Neural Networks 1 1994 161 166
- Holbert, K.E. Heger, A. S. Alang-Rashid, N. K. “Redundant Sensor Validation by using Fuzzy Logic” Nuclear Science and Engineering 118 1 Sept. 1994 54 64
- Harrison, P. R. Harrison, P. A. “Validating an bedded ntelligent Sensor Control System” IEEE Expert 9 3 June 1994 49 53
- Ahmadi-Echendu, J. E. Hengjun, Zhu “Detecting Changes in the Condition of Process Instruments” EEE Transactions on Instrumentation and Measurement 43 2 April 1994 355 358
- Lee, S. C. “Sensor Value Validation Based on Systematic Exploration of the Sensor Redundancy for Fault Diagnosis” EEE Transactions on Systems, Man and Cybernetics 24 4 April 1994 594 605