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Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine

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  • Ogaji, S. O. T.
  • Singh, R.
  • Probert, S. D.

Abstract

Sensor failures are a major cause of concern in engine-performance monitoring as they can result in false alarms and, in some cases, lead to the condemnation of a non-offending component or section of the engine. This condition has the potential to increase engine downtime and thus incur higher operational costs. The fact that more than a single sensor could be faulty simultaneously should also not be overlooked. In this paper, we present a set of neural networks, modularly designed to diagnose and quantify single and dual-sensor faults in a two-shaft stationary gas-turbine. A further outcome of the analysis is the restructuring of the faulty data to a fault-free form through the filtering out of noise and bias. This restructured data can be used to perform sensor-based calculations accurately. The engine chosen for this analysis is thermodynamically similar in performance to the Rolls Royce (RR) Avon. The data used to train the networks were derived from a non-linear aero-thermodynamic model of the engine's behaviour. The results obtained show the good prospects for the use of this technique.

Suggested Citation

  • Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2002. "Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine," Applied Energy, Elsevier, vol. 71(4), pages 321-339, April.
    • Handle: RePEc:eee:appene:v:71:y:2002:i:4:p:321-339
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    Citations

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    Cited by:

    1. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    2. Yang, Dan & Peng, Xin & Ye, Zhencheng & Lu, Yusheng & Zhong, Weimin, 2021. "Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes," Applied Energy, Elsevier, vol. 303(C).
    3. Palmé, Thomas & Fast, Magnus & Thern, Marcus, 2011. "Gas turbine sensor validation through classification with artificial neural networks," Applied Energy, Elsevier, vol. 88(11), pages 3898-3904.
    4. Junjie Lu & Jinquan Huang & Feng Lu, 2017. "Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle," Energies, MDPI, vol. 10(1), pages 1-15, January.
    5. Wang, Shengwei & Cui, Jingtan, 2005. "Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method," Applied Energy, Elsevier, vol. 82(3), pages 197-213, November.
    6. Kowalski, Jerzy, 2015. "Concept of the multidimensional diagnostic tool based on exhaust gas composition for marine engines," Applied Energy, Elsevier, vol. 150(C), pages 1-8.
    7. Zhao, Junjie & Li, Yi-Guang & Sampath, Suresh, 2023. "A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics," Applied Energy, Elsevier, vol. 332(C).

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