Journal of Energy Research and Reviews

This research develops and validates autonomous artificial intelligence agents for fault detection and self-healing in smart manufacturing systems within the context of Industry 4.0, addressing critical challenges in operational efficiency and reliability. Motivated by the need to reduce unplanned downtime and enhance production resilience, the study pursues three objectives: designing a robust AI architecture for fault detection, implementing effective self-healing mechanisms, and evaluating performance through rigorous testing. The literature review highlights gaps in temporal-spatial data integration and explainable self-healing, underscoring the potential of spiking neural networks (SNNs) and symbolic rule engines. Adopting a design science research paradigm, the proposed hybrid AI framework integrates SNNs, symbolic reasoning, and Isolation Forest algorithms, supported by industrial IoT sensor networks capturing vibration, thermal, and acoustic data. Validation was conducted through hardware-in-loop simulations, fault injection testing, and field deployments in automotive and electronics manufacturing environments. Results demonstrate a 97.3% fault detection accuracy and 89.4% self-healing recovery rate, reducing mean-time-to-repair by 31.7% and improving overall equipment effectiveness by 6.7 points, offering high annual savings for mid-sized manufacturers. Limitations include a 2.7% error margin in detection accuracy and an 83-second material regeneration latency, which exceed some high-speed production takt times. The research concludes by recommending adoption of the framework for improved manufacturing resilience and suggests future work in transfer learning and energy-efficient algorithms to enhance cross-domain scalability and sustainability.