The application of neural networks to vibrational diagnostics for multiple fault conditions
Computer Standards & Interfaces - Intelligent data acquisition and advanced computing systems
Fault diagnosis using state information
FTCS '96 Proceedings of the The Twenty-Sixth Annual International Symposium on Fault-Tolerant Computing (FTCS '96)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Symbolic dynamic analysis of complex systems for anomaly detection
Signal Processing
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This paper presents estimation of multiple faults in aircraft gas-turbine engines, based on a statistical pattern recognition tool called Symbolic Dynamic Filtering (SDF). The underlying concept is built upon statistical analysis of evidences to estimate anomalies in multiple critical parameters of the engine system; it also presents a framework for sensor information fusion. The fault estimation algorithm is validated by numerical simulation on the NASA C-MAPSS test-bed of commercial aircraft engines.