Author:
Ohoriemu Okeoghene Blessing,Ogala Justin Onyarin
Abstract
Predictive maintenance is a critical task for ensuring the reliability and efficiency of industrial systems. The integration of artificial intelligence (AI) and mathematical models has shown great potential in improving the accuracy and efficiency of predictive maintenance. This study provides an overview of the different types of mathematical models used for predictive maintenance, including physics-based, data-driven, and hybrid models. The study also discusses how AI techniques, such as machine learning and deep learning, can be used to enhance the accuracy and efficiency of predictive maintenance models. Additionally, the article highlights some of the challenges and limitations of integrating AI and mathematical models for predictive maintenance in industrial systems. Finally, this study provides a case study to demonstrate the practical application of the integrated approach for predictive maintenance in an industrial setting. This article aims to provide a comprehensive overview of the state-of-the-art in integrating AI and mathematical models for predictive maintenance and to provide guidance for researchers and practitioners working in this field.
Publisher
Federal University Dutsin-Ma
Reference35 articles.
1. Aderibigbe, F.M. and Apanapudor, J.S) . (2014aOn the Extended Conjugate Gradient Method (ECGM) Algorithm for Discrete Optimal Control Problems and some of ts features, IOSR Journal of Mathematics (IOSR-JM), Vol. 10(3)(version IV), pp. 16 – 22
2. Ahad, N.A., Apanapudor, J.S. and Arunaye, F.I.(2021): Robust MULtivariate Correlation Techniques: A Confirmation Analysis using COVID-19 Data Set, Pertanika Journal of Science and Technology, Vol. 29(2), pp. 999 - 1015.
3. Apanapudor, J.S.and Aderibigbe, F.M. (2015): Computing Techniques for the Conjugate Search Directions of the Bouhaya, A., (2021). A Review of Artificial Intelligence Techniques for Predictive Maintenance in Industry. Journal of Maintenance Engineering, 6(2), 117–138. https://doi.org/10.29252/jme.6.2.117
4. Chen, M., & Chen, Z. (2018). Data-Driven Predictive Maintenance: A Survey. Journal of Manufacturing Systems, 48, 144–156. https://doi.org/10.1016/j.jmsy.2018.03.003
5. Ge, M., & Liu, Y. (2021). A Comprehensive Review of Data-Driven Maintenance. IEEE Transactions on Reliability, 70(2), 932–953. https://doi.org/10.1109/TR.2021.3072278