References
Arash, J., Aghil, M., Vahid, S., Nader, F., Omid, M., & Sohrab, Z. (2021). A Combination of Artificial Neural Network and Genetic Algorithm to Optimize Gas Injection: A Case Study for EOR. Journal of Molecular Liquids, 339, 116654, www.scienceDirect.com An, D., Choi, J. H., & Kim, N. H. (2017). Remaining useful life estimation based on discriminating shapelet extraction. Reliability Engineering & System Safety, 165, 258- Andrea, G., Youdao, W., & Manu, P. (2014). Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Journal of Mechanical Systems and Signal Processing. Anil, K. A., Sanjeev, K., Vikram, S., & Tarun, K. G. (2015). Markov modelling and reliability analysis of urea synthesis system of a fertilizer plant. Journal of Industrial Engineering, 11, 1-14. https://doi.org/10.1007/s40092-014-0091-5 Anirbid, S., & James, B. (2022). Application of machine learning and artificial intelligence in the oil and gas industry: A state-of-the-art review. Journal of Petroleum Technology. Amal, H. (2020). Reliability analysis of gas turbine power plant based on failure data. International Journal of Mechanical & Mechatronics Engineering, 20(6). https://www.researchgate.net/publication/347513797 Basheer, S., Adam, K., & Istvan, N. (2023). Data-driven failure prediction and RUL estimation of mechanical components using accumulative artificial neural networks. Journal Homepage: www.elsevier.com/locate/engappai Baraldi, P., Mangili, F., & Zio, E. (2016). Ensemble of data-driven prognostic algorithms for aircraft/engine health monitoring. Reliability Engineering & System Safety, 149, 1-11. Bezerra, H. M., Rodrigues, L. R., Brito, M. P., Jr, E. L., Silva, I. N., & Silva, M. G. (2018). Prognostics techniques applied to maintenance of centrifugal pumps in onshore facilities. Process Safety and Environmental Protection, 116, 621-633. Cui, L., Chen, L., Roberts, C., & Zhang, L. (2020). A deep learning-based approach for Remaining Useful Life prediction of rotating machinery. IEEE Transactions on Reliability. Daniel, M. A., & Joseph, T. K. (2023). Intelligent model for assessing the reliability of non- intrusive continuous sensors in heat exchanger systems. Sensors and Actuators A: Physical, 321, 112500. Dragomir, O. E., Gouriveau, R., & Zerhouni, N. (2022). Review of prognostic problem in condition-based maintenance. European Journal of Operational Research, 301(1), 1- Djeziri, M. A., Merainani, N., Benbouzid, M. E. H., & Theilliol, D. (2016). Equipment fault detection and diagnosis by a radial basis function network based on a mixed-signal approach. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(14), 2492-2509. Elattar, H. M., Elminir, H. K., & Riad, A. M. (2021). Prognostics and health management of mechanical systems under variable operating conditions based on convolution neural networks. Mechanical Systems and Signal Processing, 152, 107477. Eti, M. C., Ogaji, S. O. T., & Probert, S. D. (2007). Reliability of the Afam electric power generating station, Nigeria. Applied Energy, 77(3), 309-315. https://www.google.com.ng/ Fernando, J. G. C., & Gilberto, F. M. S. (2009). Availability analysis of gas turbine used in power plants. International Journal of Thermodynamics, 12(1), 28-37. https://link.springer.com/chapter/10.1007/978-1-4471-2309-5_8 Federick, A., Akilo, Y., Kaltungo, J., Kumar, S., & Muray, K. (2021). Practical demonstration of a hybrid model for optimizing the reliability, risk and maintenance of rolling stock subsystem. Urban Rail Transit, 7, 139-157. https://doi.org/10.1007/s40864-021- 00148-5 Geramifard, N., Abdollahi, F., & Khanmohammadi, S. (2022). Incorporating physics knowledge into a recurrent neural network model for remaining useful life prediction. Journal of Mechanical Design, 144(1), 011702. Guo, L., Li, W., & Cheng, Y. (2022). A hybrid predictive maintenance approach combining physics-based and data-driven models for complex mechanical systems. Journal of Manufacturing Science and Engineering, 144(2), 021702. Heng, A., Zhang, S., Tan, A. C., & Mathew, J. (2020). Intelligent prognostics of machinery health utilizing Internet of Things and big data platform. IEEE Internet of Things Journal. James, J. S. (2015). Enhanced oil recovery in shale reservoirs by gas injection. Contents lists available at Science Direct Journal of Natural Gas Science and Engineering Journal homepage: www.elsevier.com/locate/jngs. Joerg, B., Kadir, C., & Michail, D. (2021). Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review. Journal of Manufacturing Science and Engineering. Kadir, C., Onur, I., & Harun, U. (2020). Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method. Hindawi Scientific Programming, 2020, Article ID 8616039. https://doi.org/10.1155/2020/861603 Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587. Manu, K. (2017). Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures. International Journal of Engineering & Technology, 7(2.28), 312-319. http://www.sciencepubco.com/index.php/IJET Michail, C., Iraklis, L., & Gerasimos, T. (2020). Machine learning and data-driven fault detection for ship systems operations. Ocean Engineering, 213, 107968. https://doi.org/10.1016/j.oceaneng.2020.107968 Nanda, P., Chakraborty, S., & Majhi, B. (2017). Hybrid self-organizing maps and support vector machine for centrifugal pump fault diagnosis. Mechanical Systems and Signal Processing, 87, 288-299. Oyedepo, S. O., Fagbenle, R. O., Adefila, S. S., & Adavbiele, S. A. (2014). Performance evaluation and economic analysis of a gas turbine power plant in Nigeria. International Journal of Energy Conversion and Management, 19(1), 431-440. http://onlinelibrary.wiley.com/doi/10.1002/ese3.61/full SangJe, C; Jong-Ho, S; Hong-Bae, J; Ho-Jin, H; Chunghun, H. & Jinsang, H. (2016). A Study on Estimating the Next Failure Time of Compressor Equipment in an Offshore Plant. Hindawi Publishing Corporation, Mathematical Problems in Engineering, Article ID 8705796, 14 pages Available at http://dx.doi.org/10.1 155/2016/8705796 Shokufe, A., Nima, R., & Sohrab, Z. (2018). A comprehensive review on Enhanced Oil Recovery by Water Alternating Gas (WAG) injection. Available online: www.elsevier.com/locate/fuel. Steve, N., Ryan, W., Karl, R., & James, K. (2018). A Machine Learning Approach to Diesel Engine Health Prognostics using Engine Controller Data. Applied Research Laboratory, Pennsylvania State University, State College, PA, 16801, USA. Wang, T., Zhang, J., & Long, X. (2020). A hybrid modelling approach to integrate the strengths of physics-based and data-driven methods for subsea pipeline failure prediction. Journal of Offshore Mechanics and Arctic Engineering, 142(3), 1-12. Zeeshan, T., Murtada, S. A., Amjed, H., Mobeen, M., Emad, M., Ammar, E., Sulaiman, A. A., Mohamed, M., & Abdulazeez, A. (2021). A systematic review of data science and machine learning applications to the oil and gas industry. Journal of Petroleum Exploration and Production Technology, 11, 4339–4374. https://doi.org/10.1007/s13202-021-01302-2 Zhiqiang, S., Wei, L., & Jianguo, Z. (2018). Reliability modeling and analysis of multi-state systems using dynamic Bayesian networks. Reliability Engineering & System Safety, 173, 1-11.
