Hybrid intelligence for enhanced fault detection and diagnosis for industrial gas turbine engine

Author:

Sarwar UmairORCID,Muhammad Masdi,Mokhtar Ainul Akmar,Khan Rano,Behrani Paras,Kaka Shuaib

Funder

Universiti Teknologi Petronas

Publisher

Elsevier BV

Reference34 articles.

1. Analysis of barriers to Industry 4.0 adoption in manufacturing organizations: an ISM approach;Kumar;Procedia CIRP,2021

2. Fault detection and diagnosis for industrial processes based on clustering and autoencoders: a case of gas turbines;Barrera;International Journal of Machine Learning and Cybernetics,2022

3. Predictive maintenance in the Industry 4.0: a systematic literature review;Zonta;Comput. Ind. Eng.,2020

4. Data-driven fault diagnosis for traction systems in high-speed trains: a survey, challenges, and perspectives;Chen;IEEE Trans. Intell. Transport. Syst.,2020

5. Fault diagnosis of gas turbine fuel systems based on improved SOM neural network;Chen,2019

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