Hierarchical Adaptive Wavelet-Guided Adversarial Network with Physics-Informed Regularization for Generating Multiscale Vibration Signals for Deep Learning-Based Fault Diagnosis of Rotating Machines

Automation, Journal Year: 2025, Volume and Issue: 6(2), P. 14 - 14

Published: March 30, 2025

Rotating machines predominantly operate under healthy conditions, leading to a limited availability of fault data and significant class imbalance in diagnostic datasets. These challenges hinder the development deployment diagnosis methods based on deep learning practice. Considering these issues, novel hierarchical adaptive wavelet-guided adversarial network with physics-informed regularization (HAWAN-PIR) is proposed. First, wavelet-based severity score used quantify within Second, HAWAN-PIR generates synthetic time domain via multiscale wavelet decomposition represents first attempt embed incorporate relevant knowledge. The quality then evaluated comprehensive synthesis index. Furthermore, scale-aware dynamic mixing algorithm proposed optimally integrate real data. Finally, one-dimensional convolutional neural (1-D CNN) employed for extracting features classifying faults. effectiveness method validated through two case studies: motor bearings planetary gearboxes. results show that can synthesize high-quality fake improve accuracy 1-D CNN by 17% bearing 15% gearbox case.

Language: Английский

Assessing the risk of vibration-induced fatigue in process pipework using convolutional neural networks

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127632 - 127632

Published: April 1, 2025

Language: Английский