review · Computer Science & IT Research Journal
Artificial intelligence and machine learning are transforming systems engineering across design, integration, and lifecycle management. These computational tools assist in automating design optimisation, managing configurations, and delivering predictive maintenance. By processing large datasets, such algorithms can forecast equipment failures and refine operational performance, leading to more reliable and sustainable engineering systems. However, adopting artificial intelligence in this field introduces distinct difficulties, encompassing technical obstacles, ethical concerns, and a demand for specialised education and training. Successfully addressing these issues requires collaborative, interdisciplinary methods alongside modernised educational programmes that prepare engineers to deploy these tools competently. Adopting artificial intelligence across complex systems demands a balanced perspective that weighs substantial functional opportunities against implementation and technical hurdles.
Modern engineering systems are increasingly complex and difficult to manage using traditional methods alone. Artificial intelligence provides ways to anticipate equipment breakdowns and optimise complex designs before failures occur. Preparing engineers with the appropriate skills and establishing practical frameworks to tackle technical and ethical hurdles will ensure that vital engineering infrastructure remains safe, reliable, and sustainable over its entire operational life.
The review describes broad applications in automated design, predictive maintenance, and configuration management for engineering systems. Intended users include systems engineers and technical managers responsible for system design and lifecycle maintenance. Because the review surveys general capabilities and highlights unresolved technical hurdles and workforce training deficits, the technologies appear to sit at an early to intermediate stage of operational readiness rather than representing off-the-shelf commercial solutions.
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This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in addressing the complexities of systems engineering. It highlights how AI and ML are revolutionizing system design, integration, and lifecycle management by enabling automated design optimization, predictive maintenance, and efficient configuration management. These technologies allow for the analysis of large datasets to predict system failures and optimize performance, thereby enhancing the reliability and sustainability of engineering systems. Despite the promising applications, the integration of AI into systems engineering presents challenges, including technical hurdles, ethical considerations, and the need for comprehensive education and training. The paper emphasizes the importance of interdisciplinary approaches and the continuous evolution of educational programs to equip engineers with the skills to leverage AI effectively. Concluding thoughts underscore AI's potential to redefine systems engineering, advocating for a balanced approach that addresses both the opportunities and challenges presented by AI advancements. Keywords: Artificial Intelligence, Machine Learning, Systems Engineering, Automated Design, Predictive Maintenance, Configuration Management, Education and Training, Technology Integration.
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DOI: 10.51594/csitrj.v5i4.1026
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