Investigation of fault detection and isolation accuracy of different Machine learning techniques with different data processing methods for gas turbine

Author:

Molla Salilew Waleligne,Ambri Abdul Karim Zainal,Alemu Lemma Tamiru

Publisher

Elsevier BV

Subject

General Engineering

Reference66 articles.

1. A Comprehensive Approach for Detection, Classification, and Integrated Diagnostics of Gas Turbine Sensors;Fabio Ceschini;J. Eng. Gas Turbines Power,2018

2. A multi – nets ANN model for real – time performance – based automatic fault diagnosis of industrial gas turbine engines;Tahan;J. Braz. Soc. Mech. Sci. Eng.,2017

3. A. D. Fentaye, A. T. Baheta, S. I. Gilani, and K. G. Kyprianidis, A Review on Gas Turbine Gas-Path Diagnostics : State-of-the-Art Methods , Challenges and Opportunities, 2019.

4. Z. Li, S. Zhong, and L. Lin, Novel Gas Turbine Fault Diagnosis Method Based on Performance Deviation Model, 2016, pp. 1–10.

5. G. F. C. R. H.I.H. Saravanamuttoo, H. Cohen and P. V. S. A.C. Nix, Gas Turbine Theory SEVENTH EDITION Gas Turbine Theory. 2017.

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