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Modern aircraft turbojet engines represent complex systems where it is important to focus on the issues of safety, reliability, efficiency and also the reduction of maintenance costs. Continuous progress in diagnostics brings new possibilities in the implementation of progressive methods instead of traditional based on the use of hardware redundancy. The article deals with the design of the diagnostic and backup system, which uses a voting method and analytical redundancy representing computational models using experimental identification methods (polynomial models, neural networks). Part of the system is also an expert system, which is able to distinguish between engine failure and sensor error. The proposed system for jet engines was tested in laboratory conditions on a small turbojet engine iSTC-21v with positive results.

Fault Detection and Isolation of an Aircraft Turbojet Engine Using a Multi-Sensor Network and Multiple Model Approach