Intelligent Fault Diagnosis for Rotating Machinery: Leveraging Symmetry
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 11
Special Issue Editor
Interests: intelligent fault diagnosis; monitoring and fault prediction of major equipment operation; reliability and safety maintenance of transportation equipment operation; big data analysis and mining based on deep learning
Special Issue Information
Dear Colleagues,
Intelligent algorithms, particularly deep learning (DL) and signal processing techniques, are revolutionizing fault diagnosis in rotating machinery such as bearings and gearboxes. Rotational machinery exhibits fundamental cyclic symmetry due to its periodic operation, reflected in vibration/acoustic signals, and a key scholarly focus involves utilizing inherent symmetries to enhance algorithm performance. Relevant research directions are specifically centered on the following aspects:
Data Augmentation: Applying rotational/phase shifts to raw signals synthetically expands limited training datasets, improving model generalization. Crucially, algorithms are designed to learn invariant representations.
Model Architecture: Network structures incorporating parameter sharing (e.g., specific convolutional neural networks (CNNs)) or cyclic structures intrinsically encode symmetry. These architectures more efficiently capture periodic fault patterns and spatial symmetries in multi-sensor setups.
Feature Extraction: Signal processing methods utilizing symmetry properties, such as wavelet transforms or cyclic spectral analysis, are used to pre-process data or within DL models. These techniques effectively isolate fault-induced modulation features masked by noise.
Explicitly incorporating knowledge of rotational symmetry—through invariant learning, symmetric model design, and tailored feature extraction—significantly enhances the accuracy, robustness, and generalization capabilities of intelligent fault diagnosis systems. This synergy between physical symmetry principles and data-driven AI is vital for developing reliable diagnostic tools for complex industrial machinery.
Dr. Yuanhong Chang
Guest Editor
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Keywords
- fault diagnosis
- anomaly detection
- health status monitoring
- intelligent maintenance
- signal processing
- machine learning
- deep learning
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