Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems

Dear Colleagues,

With the rapid development of advanced sensing technologies, intelligent data analytics and sensor signal analysis has become a cornerstone for intelligent health management and autonomous systems across a wide range of engineering domains. Modern industrial equipment, robotic systems, and autonomous vehicles rely heavily on multi-modal sensor data to perceive their operating conditions, assess system health, and make autonomous decisions in complex and uncertain environments.

This Special Issue focuses on recent advances in sensor signal analysis methods for intelligent health management and autonomous systems. Intelligent health management includes sensor-based fault diagnosis, degradation assessment, and remaining useful life prediction, aiming to improve system reliability, safety, and operational efficiency. Autonomous systems require robust sensing, perception, and real-time data interpretation to achieve adaptive, safe, and efficient autonomous operation.

Contributions are invited on novel sensor signal processing techniques, data-driven and physics-informed learning methods, and multi-sensor fusion strategies that support both health management and autonomy. Particular emphasis is placed on applications such as intelligent manufacturing, sensor-based robotic machining and assembling, surface quality monitoring, and sensing and perception for autonomous vehicles. This Special Issue aims to bring together interdisciplinary research that advances sensor signal analysis methodologies and their applications in intelligent health management and autonomous systems.

Dr. Chen Yin
Dr. Bingchang Hou
Dr. Lai Hu
Dr. Qun Wang
Guest Editors

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