Edge Based Sensor Fusion and Machine Learning for Predictive Maintenance in Smart Grid and Aerospace Systems

Hence, edge based sensor fusion integrated with machine learning becomes a major enabler of predictive maintenance in cyber physical infrastructures ranging from smart grids to aerospace systems. More specifically cloud based monitoring architectures are limited by latency, bandwidth and security concerns when obtaining high frequency sensor data from geographically diverse assets. To this end, in this paper we propose a holistic framework combining edge multi modal sensor fusion with light machine learning models for on line fault detection and remaining useful life prediction. This architecture would leverage low latency analytics, adaptive feature extraction capability and secure data aggregation while ensuring scalability on diverse platforms. Based on recent developments in the areas of edge computing, industrial IoT and predictive analytics, the paper presents a characterization of framework performance with respect to representative operating conditions in smart grid and aerospace domains. Experimental analysis shows that the proposed solution outperforms centralized approaches in terms of accuracy, response time and bandwidth usage efficiency. The results underline the need for decentralized intelligence in safety critical infrastructures while storing practical deployment considerations for resilient and autonomous maintenance systems.