Sensor Signal Analysis for Intelligent Health Management and Autonomous Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 79
Special Issue Editors
Interests: signal processing; data analytics; fault diagnosis; health prognostic; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: machinery condition monitoring, fault diagnosis, and prognostics; intelligent autonomous systems; robotics
Special Issues, Collections and Topics in MDPI journals
Interests: high-grade CNC machine tools; high performance machining; milling; grinding; bearing
Special Issues, Collections and Topics in MDPI journals
Interests: energy management; eco-driving; deep reinforcement learning; sensor signal processing for intelligent transportation systems; vehicle sensor data analytics
Special Issue Information
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
Manuscript Submission Information
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Keywords
- sensor-based fault diagnosis and remaining useful life prediction
- condition monitoring and degradation assessment using sensor data
- advanced signal processing and feature learning for multi-modal sensors
- physics-informed and hybrid learning approaches for health management
- multi-sensor data fusion for system health assessment and autonomy
- sensing and perception for robotic machining and assembling
- sensor-based surface quality monitoring and defect detection
- sensor signal analysis for autonomous vehicles and mobile robots
- explainable and trustworthy ai for sensor-based health management
- digital twin technologies driven by sensor signals
- robust sensing and diagnostics under uncertainty and harsh environments
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