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Smart Structures and Systems
  Volume 23, Number 4, April 2019 , pages 393-403
DOI: https://doi.org/10.12989/sss.2019.23.4.393
 


Faults detection and identification for gas turbine using DNN and LLM
Seyyed Mohammad Emad Oliaee, Mohammad Teshnehlab and Mahdi Aliyari Shoorehdeli

 
Abstract
    Applying more features gives us better accuracy in modeling; however, increasing the inputs causes the curse of dimensions. In this paper, a new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems. This structure consist of two structure. The first part includes Auto-Encoders (AE) as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features. The second part consists of the Local Model Networks (LMNs) with LOcally LInear MOdel Tree (LOLIMOT) algorithm to model outputs (multiple models). The fault detection is based on these multiple models. Hence the residuals generated by comparing the system output and multiple models have been used to alarm the faults. To show the effectiveness of the proposed structure, it is tested on single-shaft industrial gas turbine prototype model. Finally, a brief comparison between the simulated results and several related works is presented and the well performance of the proposed structure has been illustrated.
 
Key Words
    curse of dimension; local model network; deep neural network; LOLIMOT; fault detection; gas turbine
 
Address
Seyyed Mohammad Emad Oliaee and Mohammad Teshnehlab: Control Engineering Dept., Electrical Faculty, K.N.Toosi University of Technology, Tehran, Iran
Mahdi Aliyari Shoorehdeli: Mechatronics Dept., Electrical Faculty, K.N.Toosi University of Technology, Tehran, Iran
 

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