Diagnosis and Prognosis of Incipient Faults Using Information Processing or Machine and Deep Learning
Special Issue Editors
Interests: electrical drives; incipient fault diagnosis; fault tolerant control; renewable energy
Special Issues, Collections and Topics in MDPI journals
Interests: data and signal processing; incipient fault diagnosis; detection and estimation; data hiding; watermarking; complex systems; statistical learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Incipient faults can be defined on the basis of their effects (loss of performance), their causes (changes in material features or information properties, for example), or the properties of the signals (high signal to noise ratio (SNR) and low fault to noise ratio (FNR)). Within the health-monitoring framework, reaching good performance (low false alarm rate, low miss detection rates, high classification accuracy, etc.) becomes more challenging when dealing with slowly evolving faults, particularly in noisy environments. The accurate estimation of the remaining useful lifetime also becomes more tedious because of the uncertainties and complex non-linear phenomena, such as regeneration in electrochemical energy storage devices.
The aim of this Special Issue is to provide a forum for academics and the industry to discuss significant recent advances in the development of tools and methods derived from information theory (distance and divergence) and systems theory and their application in accurately diagnosing incipient faults in a timely manner, thus predicting their evolution and assessing the RUL. Discussions on computational requirements (quantity of data and computational capabilities), as well as the tolerance of uncertainties, are welcome. The Special Issue is also an opportunity to discuss the standards and best practices (sensor technologies, dataset building, performance comparison, guidelines, etc.) and future trends. The Special Issue is open to all application sectors, including biomedical, transport, energy production, etc.
Prof. Dr. Demba Diallo
Prof. Dr. Claude Delpha
Dr. Khalid Dahi
Guest Editors
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Keywords
- incipient faults
- resilience
- statistical and information measures
- machine and deep learning
- predictive maintenance
- cybersecurity
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