Vibration-Based Diagnostics and Condition Monitoring
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".
Deadline for manuscript submissions: 20 April 2025 | Viewed by 50
Special Issue Editors
Interests: condition monitoring; vibration monitoring; diagnostics
Special Issues, Collections and Topics in MDPI journals
Interests: machine condition monitoring; diagnostics and prognostics; signal analysis; shock and vibration; nonlinear dynamics; damping systems
Special Issue Information
Dear Colleagues,
Vibration-based diagnostics and condition monitoring are essential for maintaining the reliability, safety, and integrity of industrial machinery. The objective of machinery diagnostics is to investigate the underlying causes of symptoms indicative of abnormal operational conditions. By analyzing vibration data, specific patterns in the extracted features can be associated with deviations from normal operation, potentially signaling the presence of damage.
This Special Issue examines recent advancements in vibration monitoring approaches that enable early fault detection, fostering the switch from programmed to condition-based and preventive maintenance strategies. Both theoretical and practical developments in vibration-based condition monitoring and machine diagnostics are encouraged. Topics of interest may include (but are not limited to) advanced signal processing, feature extraction methods, data-driven diagnostics, methods and the apparatus of vibration-based condition monitoring, and real-time condition monitoring techniques. By incorporating these tools into maintenance practices, industries can improve machinery performance while reducing downtime and costs.
We invite contributions that include experimental research, numerical analyses, and theoretical studies offering innovative solutions to challenges in vibration-based diagnostics and condition monitoring. This Special Issue aims to compile cutting-edge research that advances predictive maintenance and enhances the reliability of industrial systems.
Preference will be given to studies utilizing existing datasets in the literature. For example,
The Politecnico di Torino rolling bearing open-access dataset, DOI: 10.5281/zenodo.3559553 (https://zenodo.org/record/3559553#.Xk0EC2hKhPY)
CHAO, Manuel Aria, et al. Aircraft Engine Run-To-Failure Data Set Under Real Flight Conditions. 2020. (https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan-2)
Biggio, L.; Russi, M.; Bigdeli, S.; Kastanis, I.; Giordano, D.; Gagar, D. PHME Data Challenge. Eur. Conf. Progn. Health Manag. Soc. 2021. (https://github.com/PHME-Datachallenge/Data-Challenge-2021)
Dr. Alessandro P. Daga
Guest Editor
Dr. Luca Viale
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- machine diagnostics
- condition monitoring
- maintenance strategies
- vibration monitoring
- novelty detection
- damage detection
- damage identification
- damage classification
- signal processing
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