Qingbin Tong

A fault diagnosis method with continuous frequency-band indicator embedding and class-aware interpretable feature weighting

Shouxin Du, Qingbin Tong, Xuedong Jiang, B. C. Wang et autres

Neural networks have been extensively applied in mechanical fault diagnosis due to their strong capabilities in feature extraction and classification. However, their limited interpretability and unknown credibility of decision hinder deployment in high-reliability scenarios. To address this issue, a frequency band multi-indicator …

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Adaptive conditional kernel Bures metric learning for bearing cross-device fault diagnosis under small and unbalanced samples

Ziwei Feng, Qingbin Tong, Feiyu Lu, B. C. Wang et autres

Abstract To address the prevalent issues of data distribution shift and class imbalance in cross-device bearing fault diagnosis under industrial environments, this paper proposes a novel adaptive conditional kernel Bures (ACKB) metric learning framework. The method aims to learn domain-invariant features by …

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A single-domain generalization framework integrating Zernike-Based detail blur feature extraction and Mamba global attention mechanism

Qingbin Tong, Jilong Zhao, Xuedong Jiang, Baohua Wang et autres

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Dynamic Mask Cepstrum-Enhancement Network (DMC-EN): A dual-task physics-guided method based on cyclostationary features of bearing signals

Qingbin Tong, Xuedong Jiang, Jianjun Xu, Jingyi Huo

While deep learning has advanced bearing fault diagnosis, most models operate as black boxes, treating vibration signals as generic data and failing to integrate fundamental physical principles. To address this limitation, this paper introduces the Dynamic Mask Cepstrum-Enhancement Network (DMC-EN), a novel …

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ACS-DM: Adaptive conditional sampling diffusion model for few-shot machinery fault diagnosis

Qingbin Tong, Ruize Zhu, Feiyu Lu, B. C. Wang et autres

Deep learning has advanced machinery fault diagnosis, yet performance remains constrained by scarce and imbalanced labeled vibration data. We present ACS-DM, an adaptive conditional sampling diffusion framework that synthesizes frequency-faithful yet temporally diverse signals for few-shot regimes. ACS-DM couples a Nested U-Net …

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A time-frequency interpretable framework for bearing fault diagnosis via global channel-region information interaction and weight-CAM

Shouxin Du, Qingbin Tong, Xuedong Jiang, B. C. Wang et autres

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