An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data
arXiv:2606.24459v1 Announce Type: cross Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments. Existing approaches address these issues in isolation and rely on implicit feature alignment, limiting effectiveness under concurrent challenges. This paper proposes a knowledge-guided two-stage transfer learning framework that employs a lightweight GPT-2-style Transformer with causal self-attention for hierarchical feature extraction from vibration signals, estab
The increasing availability of lightweight LLMs and the critical need for robust industrial maintenance solutions are driving advancements in fault diagnosis.
This development indicates a growing application of AI, specifically LLMs, in critical industrial predictive maintenance, improving efficiency and reducing downtime.
Bearing fault diagnosis can now be more effectively performed across varied industrial environments even with limited data, leveraging generative AI principles.
- · Industrial IoT providers
- · Predictive maintenance software companies
- · Manufacturing sector
- · AI/ML researchers
- · Traditional fault diagnosis methods
- · Companies reliant on extensive labeled datasets for maintenance
Reduced operational costs and increased uptime for industrial machinery due to improved fault detection.
Accelerated integration of AI and machine learning into more complex industrial automation and control systems.
Potential for autonomous and self-healing industrial systems requiring minimal human intervention for maintenance decisions.
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Read at arXiv cs.CL