Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning Article Swipe

MAKE IMPACT

Tasfiq E. Alam · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2508.07536

Accurate and interpretable bearing fault classification is critical for ensuring the reliability of rotating machinery, particularly under variable operating conditions where domain shifts can significantly degrade model performance. This study proposes a physics-informed multimodal convolutional neural network (CNN) with a late fusion architecture, integrating vibration and motor current signals alongside a dedicated physics-based feature extraction branch. The model incorporates a novel physics-informed loss function that penalizes physically implausible predictions based on characteristic bearing fault frequencies - Ball Pass Frequency Outer (BPFO) and Ball Pass Frequency Inner (BPFI) - derived from bearing geometry and shaft speed. Comprehensive experiments on the Paderborn University dataset demonstrate that the proposed physics-informed approach consistently outperforms a non-physics-informed baseline, achieving higher accuracy, reduced false classifications, and improved robustness across multiple data splits. To address performance degradation under unseen operating conditions, three transfer learning (TL) strategies - Target-Specific Fine-Tuning (TSFT), Layer-Wise Adaptation Strategy (LAS), and Hybrid Feature Reuse (HFR) - are evaluated. Results show that LAS yields the best generalization, with additional performance gains when combined with physics-informed modeling. Validation on the KAIST bearing dataset confirms the framework's cross-dataset applicability, achieving up to 98 percent accuracy. Statistical hypothesis testing further verifies significant improvements (p < 0.01) in classification performance. The proposed framework demonstrates the potential of integrating domain knowledge with data-driven learning to achieve robust, interpretable, and generalizable fault diagnosis for real-world industrial applications.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2508.07536

PDF

https://arxiv.org/pdf/2508.07536

OA Status

green

OpenAlex ID

https://openalex.org/W4416242525

Raw OpenAlex JSON

OpenAlex ID

https://openalex.org/W4416242525

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2508.07536

Digital Object Identifier

Title

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning

Work title

Type

preprint

OpenAlex work type

Language

en

Primary language

Publication year

2025

Year of publication

Publication date

2025-08-11

Full publication date if available

Authors

Tasfiq E. Alam

List of authors in order

Landing page

https://arxiv.org/abs/2508.07536

Publisher landing page

PDF URL

https://arxiv.org/pdf/2508.07536

Direct link to full text PDF

Open access

Yes

Whether a free full text is available

OA status

green

Open access status per OpenAlex

OA URL

https://arxiv.org/pdf/2508.07536

Direct OA link when available

Cited by

0

Total citation count in OpenAlex

Full payload