Sensing Technologies for Fault Diagnostics and Prognosis: 2nd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 2640
Special Issue Editor
Interests: fault diagnosis; prognosis; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machinery and mechanical structures in the industry suffer from inevitable degradation and performance degradation during operation. By collecting and processing data using a variety of sensors, timely diagnosis of symptoms of deterioration and reliable estimation of future health conditions are essential for industrial productivity and reliability. Models consisting of sensor data measured in the past using AI technology have shown great potential for fault diagnosis and prognosis in industrial equipment. AI-powered technologies will become more important in the future as the deployment of Internet of Things and cloud-based technologies for stateful maintenance makes vast amounts of measurement data available for decision making. This Special Issue will focus on fault diagnosis and prognosis of industrial equipment and mechanical structures using a variety of sensors. Sensor-based artificial neural network technology, explainable AI solutions, objects for error diagnosis and prognosis in the context of Industry 4.0, cloud computing, cyber-physical systems, and machine-to-machine interfaces and paradigms are welcome.
Prof. Dr. Jongmyon Kim
Guest Editor
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Keywords
- sensor-based artificial neural network technology
- explainable AI solutions
- objects for error diagnosis and prognosis in the context of Industry 4.0
- cloud computing
- cyber-physical systems
- machine-to-machine interfaces and paradigms
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