Sarwar, U. and Muhammad, M. and Mokhtar, A.A. and Khan, R. and Behrani, P. and Kaka, S. (2024) Hybrid intelligence for enhanced fault detection and diagnosis for industrial gas turbine engine. Results in Engineering, 21.
Full text not available from this repository.Abstract
In context of industry 4.0, intelligent manufacturing and maintenance play a significant role. The fault detection and diagnosis (FDD) of industrial gas turbine (IGT) engine is very crucial in smart manufacturing. With the advancement of machine learning and sensor technology, artificial neural network (ANN) and multi-sensor data fusion have made it possible to solve the above issues. In this work, a hybrid model is proposed for the FDD of an IGT engine. Principal component analysis (PCA) is firstly employed to combine the multi-sensor monitoring data as a pre-processing step. The PCA approach has the capacity to glean insights from raw data and optimize the amalgamation of various condition monitoring datasets, with the aim of enhancing accuracy and maximizing the utility of gas turbine information. Later, ANN based FDD method is applied on the fused multiple sensors monitoring data. The present work also implements a comparative account of supervised and unsupervised ANN learning techniques, like multilayer perceptron and self-organizing map, and their pattern classification evaluations. The proposed model facilitates the attainment of early FDD with minimum error and has been validated and tested using real time data from actual operation environments. The data is collected from twin-shaft (18.7 MW) IGT engine as a case study. Results demonstrate that the proposed hybrid model is able to detect the conditions of industrial gas turbine engine with best diagnosis accuracy and calculated errors of 0.00173 and 1.9498. Comparison of two learning techniques demonstrates the superior performance of supervised learning technique. © 2024 The Author(s)
Item Type: | Article |
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Additional Information: | cited By 2 |
Uncontrolled Keywords: | Condition monitoring; Conformal mapping; Fault detection; Gas turbines; Gases; Learning algorithms; Learning systems; Principal component analysis, Fault detection and diagnosis; Gas turbine engine; Hybrid intelligence; Hybrid model; In contexts; Industrial gas turbines; Intelligent Manufacturing; Learning techniques; Principal-component analysis; Sensor monitoring, Self organizing maps |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 04 Jun 2024 14:19 |
Last Modified: | 04 Jun 2024 14:19 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/19855 |