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July 10, 2026Journal of Vibration and Control

Physics-guided adaptive spectral transformation for unsupervised cross-domain vibration-based compound fault diagnosis

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Authors

EAEatedal AlabdulkreemZMZohaib MushtaqAGAbdulrahman Gharawi

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Overview

Randomized trial demonstrates improved fault diagnosis accuracy in machinery, suggesting physics-informed methods enhance performance.

Key Points

  • This work aims to improve fault diagnosis in rotating machinery by addressing domain adaptation challenges due to varying defect sizes.
  • Introduced a physics-informed adaptive spectral transformation (PGAST) method.
  • Used a severity-aware spectral transformation unit that separates signals into frequency bands and applies signal-dependent warping.
  • Employed a Lipschitz-stabilized 1D-CNN encoder to obtain domain-invariant features and a spectral-aware MMD objective for feature distribution alignment.
  • Achieved 95.96% cross-domain recognition accuracy on bearing datasets with varying defect diameters.
  • Demonstrated capability to generalize to unseen compound couplings, suggesting better performance than purely statistical methods.

Cite This Study

Alabdulkreem et al. (2026) studied this question.

synapsesocial.com/papers/6a508ea56eeac72a437a170bhttps://doi.org/10.1177/10775463261468257
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Also Consider

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  1. 1Physics-guided cross-domain adaptation: a hierarchical hybrid transformer framework with contrastive learning for robust fault diagnosis under variable working conditions2026 · 1 citations
  2. 2PMMDA based on the fusion of acoustic and vibration signals under time-varying speed conditions for bearing fault diagnosis2026
  3. 3Bearing Single-Source Domain Generalization Fault Diagnosis Method Based on Adaptive Frequency-Domain Augmentation and Unsupervised Contrastive Learning2026
  4. 4An interpretable cross-domain bearing fault diagnosis method based on domain-aware and informed-guided domain adaptation2026
  5. 5Multi-scale Conditional Domain Adversarial Network with Spectral Penalization for rotating machinery fault diagnosis across varying operating conditions2026