Article

Sarwar, U. and Mokhtar, A.A. and Muhammad, M. and Wassan, R.K. and Soomro, A.A. and Wassan, M.A. and Kaka, S. (2024) Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction. Engineering Applications of Computational Fluid Mechanics, 18 (1).

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.

Soomro, A.A. and Mokhtar, A.A. and Kurnia, J.C. and Lashari, N. and Sarwar, U. and Jameel, S.M. and Inayat, M. and Oladosu, T.L. (2022) A review on Bayesian modeling approach to quantify failure risk assessment of oil and gas pipelines due to corrosion. International Journal of Pressure Vessels and Piping, 200. ISSN 03080161

Muhammad, M.B and Sarwar, U. and Tahan, M.R. and Abdul Karim, Z.A. (2016) Fault diagnostic model for rotating machinery based on principal component analysis and neural network. ARPN Journal of Engineering and Applied Sciences, 11 (24). pp. 14327-14331. ISSN 18196608

Tahan, M. and Sarwar, U. and Muhammad, M. and Abdul Karim, Z.A. (2016) Modeling and sensitivity analysis of a multi-nets anns model for real-time performance-based condition monitoring of an industrial gas turbine engine. ARPN Journal of Engineering and Applied Sciences, 11 (24). pp. 14269-14274. ISSN 18196608

Conference or Workshop Item

Muhammad, M.B. and Sarwar, U. and Tahan, M. and Karim, Z.A.A. (2017) Intelligent fault diagnostic model for rotating machinery. In: UNSPECIFIED.

Sarwar, U. and Muhammad, M.B. and Abdul Karim, Z.A. (2014) Time series method for machine performance prediction using condition monitoring data. In: UNSPECIFIED.