A Multimodal Progressive Fusion Bearing Fault Diagnosis Algorithm Based on Residual Network

Bearings are crucial components of rotating machinery in critical industrial equipment such as wind turbines, high-speed trains, and aerospace engines. Existing methods for bearing fault diagnosis are generally confined to superficial integration of multi-sensor or multi-domain data, constrained by either poor heterogeneous information integration in early fusion approaches or information loss and imbalanced modality representations caused by late fusion strategies, resulting in limited diagnostic effectiveness under complex and dynamic industrial operating conditions. To solve this issue, we propose a multimodal progressive fusion bearing fault diagnosis algorithm based on residual networks. The algorithm integrates multi-sensor and multi-domain data and automatically extracts fault features using residual networks. Then, an improved progressive feature fusion technique is applied to optimise the utilisation of the multimodal features, which aims to allow earlier layers to access later fused features, avoiding the loss of important information and improving the fusion representation over multiple iterations. The diagnostic efficacy of the proposed method is validated using two different bearing datasets, achieving a diagnostic accuracy of 99.97% for composite faults. This advancement shows the great potential for implementation on industrial IoT platforms, especially in scenarios such as power generation and transport where predictive maintenance is required, reducing unplanned downtime and maintenance costs.