GMRVGG: A Bearing Fault Diagnosis Method Based on Tri-Modal Image Feature Fusion

Bearings serve as vital components in rotating machinery. Fault diagnosis of bearings constitutes an essential area within mechanical health monitoring. However, most existing methods rely solely on single-modal data or employ a single signal-to-image conversion technique, leading to insufficient information dimensionality and inadequate feature representation, which ultimately limits diagnostic accuracy. To address these challenges, this paper proposes a bearing fault diagnosis method (GADF-MTF-RP-VGG16, GMRVGG) based on tri-modal image feature fusion. Specifically, three image conversion techniques-Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP)-are utilized to first convert 1D vibration signals into 2D images. Subsequently, shallow to deep features are extracted and fused through the VGG16 backbone network. Finally, fault diagnosis is achieved by integrating a fully connected classifier layer. The proposed methodology was comprehensively validated on both the Case Western Reserve University (CWRU) and the University of Ottawa datasets, which were augmented with severe 6 dB Gaussian white noise and 6 dB pink noise to simulate complex industrial environments. Under these harsh conditions, the proposed method achieved superior overall accuracies (up to 96.9% on the CWRU dataset and consistently 95.8% on the Ottawa dataset), significantly surpassing conventional single-modal approaches. This effectively addresses the limitations of insufficient feature dimensionality and inadequate representation, establishing a highly reliable and robust solution for intelligent bearing fault diagnosis.