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SR-30 turbojet engine real-time sensor health monitoring using neural networks, and Bayesian belief networks

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Abstract

This paper describes the use of artificial intelligence-based techniques for detecting and isolating sensor failures in a turbojet engine. Specifically, three artificial intelligence (AI) techniques are employed: artificial neural networks (NNs), statistical expectations, and Bayesian belief networks (BBNs). These techniques are combined into an overall system that is capable of distinguishing between sensor failure and engine failure—a critical capability in the operation of turbojet engines.

The turbojet engine used in this study is an SR-30 developed by Turbine Technologies. Initially, NNs were designed and trained to recognize sensor failure in the engine. The increased random noise output from failing sensors was used as the key indicator. Next, a Bayesian statistical method was used to recognize sensor failure based on the bias error occurring in the sensors. Finally, a BBN was developed to interpret the results of the NN and statistical evaluations. The BBN determines whether single or multiple sensor failures signify engine failure, or whether sensor failures represent separate, unrelated incidences. The BBN algorithm is also used to distinguish between bias and noise errors on sensors used to monitor turbojet performance. The overall system is demonstrated to work equally well during start-up and main-stage operation of the engine. Results show that the method can efficiently detect and isolate single or multiple sensor failures within this dynamic environment.

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Authors and Affiliations

  1. The University of Alabama, Tuscaloosa, AL, 35487-0280, USA

    Cameron Nott, Semih M. Ölçmen & Charles L. Karr

  2. 2L Research Corporation, Huntsville, AL, 35812, USA

    Luis C. Trevino

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  1. Cameron Nott
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  2. Semih M. Ölçmen
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  3. Charles L. Karr
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  4. Luis C. Trevino
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Nott, C., Ölçmen, S.M., Karr, C.L. et al. SR-30 turbojet engine real-time sensor health monitoring using neural networks, and Bayesian belief networks. Appl Intell 26, 251–265 (2007). https://doi.org/10.1007/s10489-006-0017-z

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