Multimodal Adversarial Transfer Learning for Bearing Fault Diagnosis of Unmanned Mining Trucks in Realistic Noisy Environments

Abstract

Unmanned mining trucks operate in harsh environments such as those in open-pit mines, where online fault diagnosis of critical drivetrain bearings faces severe challenges including slow response, high precision requirements, and strong interference from realistic on-site noise. To address the insufficient generalization capability of existing diagnostic methods in real-world noisy scenarios, this paper proposes a multimodal adversarial transfer learning framework for bearing fault diagnosis in unmanned mining trucks. First, to bridge the domain shift gap between laboratory data and on-site truck data, an augmented multimodal dataset is constructed based on real-vehicle noise grafting. This approach fuses authentic background noise collected from the field with clean laboratory fault signals, thereby simulating graded on-site interference. Second, a deep feature extraction network integrating CNN, ViT, and CBAM attention mechanisms is designed. Building upon this backbone, an adversarial training scheme combined with a hierarchical adaptive fine-tuning strategy is introduced to formulate a domain-adversarial transfer learning model. This model is capable of extracting robust features that are both fault-discriminative and domain-invariant from multimodal signals (vibration and current). Experimental results on the constructed noise-augmented dataset demonstrate that the proposed method maintains high diagnostic accuracy in cross-domain scenarios with strong noise and limited samples, significantly outperforming conventional approaches. This study provides an effective technical pathway for real-time and highly reliable “edge-terminal” fault diagnosis of unmanned mining trucks operating in realistic noisy environments.