Intelligent Fault Diagnosis and Monitoring
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".
Deadline for manuscript submissions: 30 September 2024 | Viewed by 7689
Special Issue Editors
Interests: fault diagnosis and prognosis; reliability; non-destructive testing; structural health evaluation; advanced signal analysis; nonlinear system identification; instrument measurement
Interests: computational intelligence; learning systems; learning control; optimal control
Special Issue Information
Dear Colleagues,
The condition monitoring and fault diagnosis technology for complex industrial production processes have always been the main goal of global future industrial production development, with informatization, intelligence, high reliability and high efficiency. Mechanical fault diagnosis and prediction are the key technology for intelligent and automated safety production in the modern manufacturing production lines. The development of the theories and methods for mechanical fault diagnosis under non-stationary operating condition is an urgent requirement for modern industrial production processes to achieve intelligence, digital monitoring and diagnostic prediction. It has become a technical bottleneck and recognized challenge for the applications of fault diagnosis technology in key mechanical equipment in engineering practice. This Special Issue focuses on the applications of intelligent techniques for sensor signal analysis, such as vibration, acoustics, image, stress, strain, electromagnetic, etc. It includes the development and applications of the advanced methods and theories in the research areas of fault mechanism analysis, system dynamic analysis and model, machine learning, artificial intelligence, data mining, signal processing, reliability analysis, remaining life prognosis, non-destructive testing, advanced imaging process, optimization method, big data analysis, sensors, instrument measurement, nonlinear system analysis, robotic control technology, information technique, etc. It invites research papers and reviews that explore innovative approaches and tools.
Prof. Dr. Hanxin Chen
Prof. Dr. Qinglai Wei
Guest Editors
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Keywords
- fault diagnosis and prognosis
- structural health monitoring
- machine learning
- image and signal processing
- non-destructive testing
- multiple sensors
- nonlinear system identification
- reliability
- big data
- cloud computing
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