Asymmetric Studies with Complex Mechanical Systems
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 1515
Special Issue Editors
Interests: deep transfer learning; federated learning; signal processing; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Interests: condition monitoring; reliability; machine learning
Interests: mahcine diagnostics and prognostics; signal processing; sparse representation; machine learning
Interests: signal processing; fault diagnosis and prognosis; vibration analysis and suppression
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Asymmetry analysis is a crucial aspect of mechanical engineering that helps in identifying and diagnosing faults in machines. By studying the asymmetrical characteristics of a mechanical system, engineers can pinpoint the root cause of issues such as unbalance, misalignment, and faults of motors, bearings, etc. With the advent of modern technology, the complexity of mechanical systems has increased, making it more challenging to detect and predict faults accurately.
Machine learning has emerged as a powerful tool for asymmetric analysis in mechanical fault detection and diagnosis. By leveraging the power of artificial intelligence, engineers can train machines to identify patterns and anomalies in data, making it easier to detect faults in real time. The integration of machine learning with cloud computing and Industry 4.0 technologies has opened up new possibilities for improving the accuracy and efficiency of fault diagnosis in complex systems.
Topics may include, but are not limited to:
- Asymmetry analysis of mechanical systems;
- Abnormal detection;
- Intelligent diagnosis;
- Fault prognosis;
- Real-time monitoring;
- Predictive maintenance.
Dr. Zhuyun Chen
Dr. Borong Hu
Dr. Yun Kong
Dr. Minghui Hu
Guest Editors
Manuscript Submission Information
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Keywords
- asymmetry analysis
- abnormal detection
- condition monitoring
- artificial intelligence
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