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A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines

Tahan, M. and Muhammad, M. and Abdul Karim, Z.A. (2017) A multi-nets ANN model for real-time performance-based automatic fault diagnosis of industrial gas turbine engines. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 39 (7). pp. 2865-2876.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

When a robust mathematical model of a process equipment is available, model-based diagnostic methods can be used to identify the occurrence of faults in a system. However, these methods are less effective when the non-linearity, complexity, and modeling uncertainties of the system increase. In recent years, a new discipline, known as artificial intelligence-based methods, has emerged, which allows the behavior of the system to be studied using operational data. While single-learner artificial neural network (ANN)-based models demonstrate a satisfactory level of capability in assessing the health of gas turbines, this article investigated the application of a multiple networks artificial neural network (multi-nets ANN) model using a multiple-views multiple learners approach to provide a real-time performance-based automatic fault detection (AFD) system in gas turbine engines. Towards this end, a number of key performance variables, which are commonly measurable on most industrial gas turbine engines, were monitored, and their associated ANNs were trained for healthy conditions. Two back-propagation training algorithms, namely the Levenberg–Marquardt and Bayesian regularization algorithms, and the k-fold cross-validation technique, were employed to train the optimal networks using a training data set. Using the trained multi-nets ANN model, two case studies were conducted pertaining to the detection of drop in compressor flow capacity and compressor fouling in an industrial 18.7-MW twin-shaft gas turbine engine. The ability of all the trained networks of the multi-nets model to detect these faulty conditions was investigated. The results obtained showed that among the four trained networks in the multi-nets model, the associated network for gas generator rotational speed was able to track these incidents earlier. The findings demonstrated the capabilities and performance of the proposed multi-nets model with regard to AFD in industrial gas turbines. © 2017, The Brazilian Society of Mechanical Sciences and Engineering.

Item Type:Article
Impact Factor:cited By 2
Departments / MOR / COE:Division > Academic > Faculty of Engineering > Mechanical Engineering
ID Code:19444
Deposited By: Ahmad Suhairi
Deposited On:20 Apr 2018 05:56
Last Modified:20 Apr 2018 05:56

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