The International Journal of Engineering Sciences

Abstract

ABSTRACT
Engineering systems serve a crucial role in supporting manufacturing, transportation, and energy operations, among other critical areas. Because of their growing complexity and the variety of related data, it is more crucial than ever to administer these systems in a trustworthy and effective manner. The purpose of this study is to investigate the cognitive framework and underlying theories that underpin its applications in engineering systems maintenance management and failure prediction. Reviewing and assessing the fundamental ideas and standardized algorithms is part of the technique, which focuses on examining their theoretical potential and anticipated results. According to the study, machine learning models have a great deal of theoretical depth and promise. To increase the precision and dependability of forecasts, it is necessary to provide a comprehensive theoretical framework that tackles problems with data quality, performance evaluation standards, and model transparency in addition to combining theoretical and technical concepts. Future developments emphasize how machine learning approaches can be integrated with engineering models to enhance the process of creating intelligent and sustainable maintenance systems, improve prediction skills, and enable the creation of more transparent and interpretable algorithms.
Keywords: Engineering Systems; Machine Learning; Maintenance Management; Failure Prediction; Algorithms;

References

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Article history_en

Received : Sep 02, 2025

Revised : Sep 04, 2025

Accepted : Oct 07, 2025

Authors Affiliations_en

ALI HUSSIEN ALKHATIB (1),*, RANYA HAMAD BUSTANY (2), JAMAL ALFAKHORY (3)

Ethics declarations_en

Acknowledgment	None
Author Contribution	All authors contributed equally to the main contributor to this paper. All authors read and approved the final paper.
Conflicts of Interest	“The authors declare no conflict of interest.”
Funding	“This research received no external funding”