Condition Monitoring and Intelligent Fault Diagnosis of Rotor System
A special issue of Signals (ISSN 2624-6120).
Deadline for manuscript submissions: 30 September 2026 | Viewed by 163
Special Issue Editors
Interests: intelligent fault diagnosis; status assessment and trend forecasting; rotating machinery signal processing
Special Issues, Collections and Topics in MDPI journals
Interests: fault diagnosis based on deep learning; life prediction based on deep learning; dynamic modeling and fault simulation; digital twin for intelligent operation and maintenance; intelligent data mining and feature extraction; diagnostic algorithm development based on big data platforms; monitoring and diagnostic software development; engineering algorithm design
Interests: diagnostics, prognostics and health management (PHM) for electromechanical and hydraulic equipment; artificial intelligence and signal processing; digital twins
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rotor system is a fundamental component of rotating equipment such as aero-engines, wind turbines, and high-speed trains, playing a vital role in modern society. Due to the diversity of operational requirements, rotor systems frequently operate under high-speed, heavy-load, and variable conditions, inevitably leading to fatigue damage and significant economic losses or even casualties. Condition monitoring and intelligent diagnosis of rotor system components (bearings, gears, rotor shafts) are essential and meaningful. Furthermore, novel methods based on machine learning, deep learning, and transfer learning have been developed, providing effective technical solutions for evaluating rotor system conditions and identifying specific types of faults.
Health monitoring and intelligent fault diagnosis constitute an interdisciplinary field thriving through dynamic intellectual exchange. The planned Special Issue of Signal aims to serve as a forum for researchers and industrial engineers to share the latest advancements in condition monitoring and intelligent fault diagnosis of rotor system, while addressing critical issues, challenges, and potential future trends in modern Prognostics and Health Management (PHM) systems. Papers accepted for this Special Issue are expected to showcase cutting-edge data-driven design methodologies, particularly new theoretical achievements with practical application value. We sincerely invite global experts to contribute their research, covering but not limited to the following areas.
Dr. Jiantao Lu
Dr. Jingsong Xie
Dr. Xiaoli Zhao
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
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Keywords
- data-driven health monitoring and intelligent fault diagnosis
- data-driven performance evaluation, decisions and their applications
- intelligent fault diagnosis with imbalanced samples
- active learning intelligent diagnosis with small samples
- transfer learning
- diagnosis algorithms and technologies for large models
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