Emerging Sensing Technologies for Machine Health State Awareness
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 30 December 2024 | Viewed by 142
Special Issue Editors
Interests: intelligent sensing; instrumentation; fault diagnostics and prognostics; artificial intelligence and machine learning; signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: UAV-based automatic railway inspection; fault diagnosis; prognostics and health management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modern society depends on the reliable and uninterrupted operation of complex machines such as aircraft, power plants, wind farms, and vehicles. Timely awareness of the health state of these machines is essential to prevent sudden failure and enable condition-based maintenance. Health state awareness relies on acquiring machine condition data, such as vibration, acoustic, temperature, stress, and rotating speed. Sensing technologies are fundamental for acquiring these data. While traditional sensing technologies are abundant, new sensing technologies are emerging with wider/higher sensitivity, more reliable data sensing, higher engineering applicability, more user-friendliness, lower cost, and higher robustness.
In this Special Issue, we aim to collect the latest research progress on new sensing technologies for machine health state awareness. Topics of interest include, but are not limited to, the following:
- New sensor designs such as piezoelectric sensors, infrared cameras, and fiber Bragg gratings;
- Sensor testing and calibration;
- Application of new sensing technologies for machine health monitoring;
- Unmanned aerial vehicle-based video monitoring;
- Sensory data processing;
- Multi-modality sensory data fusion.
Dr. Yuejian Chen
Dr. Zhipeng Wang
Dr. Yuanjin Ji
Guest Editors
Manuscript Submission Information
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Keywords
- intelligent sensing
- fault diagnostics
- machine health monitoring
