A Comparative Review of Model Driven Control and Data Driven Machine Learning for Manufacturing, Monitoring, Inspection and Optimization: Nigerian Adaptability

Practical automation in autonomous condition assessment of physical assets is one of the obvious central goals of the fourth (4th) Industrial revolution majorly known with Cyber – Physical Systems; IoT and Big Data; AI & Machine Learning; Cloud, Edge Computing and Connectivity. Unfortunately, research on robotic inspection of civil infrastructure and intelligent fault diagnosis for machinery has evolved largely concurrently and, in some cases, almost simultaneously.

This review provides a comparative analysis of these two domains as examined independently in already existing literatures. The six key study areas covered in this review are: Unmanned Ariel Vehicle (UAV), Unmanned Ground Vehicle (UGV), Inspection, Ellipsoidal obstacle avoidance, Noise-robust fault diagnosis, Vibration image-based classification, and Transfer learning for tool wear detection. Comparative reviews were made using Sensing modalities; Machine learning strategies; Real-time constraints; and Validation practices across both fields. Results substantially concur with the results of previous researches that Fault diagnosis research leads in handling data scarcity, noise robustness, and domain adaptation, while Inspection research leads in multi-modal sensor fusion and field deployment. However, it further shows that both domains experience shared challenges in Benchmarking, Edge deployment, Explainability, and Certification, while three convergent areas were identified in: Transfer learning to reduce data needs in inspection; Edge optimization techniques from inspection to inform diagnosis deployment, and Causal explainability to build engineer trust. Interestingly, the review concludes with a research agenda focused on Cross-domain benchmarks, Tiny Machine Learning deployment, and Certification-ready architectures. It further suggests that unifying these streams will accelerate the adoption of autonomous asset management across infrastructure and manufacturing, while proffering conditions for the suitability and adaptability of the results of the review to the Nigerian engineering space.