Smart manufacturing demands ever-increasing equipment reliability and continuous availability. Traditional fault diagnosis relies on attached sensors and complex wiring to collect vibration signals. This approach suffers from poor environmental adaptability, difficult maintenance, and cumbersome preprocessing. This study pioneers the use of high-temporal-resolution dynamic visual information captured by an event camera to fine-tune a multimodal large model for the first time. Leveraging non-contact acquisition with an event camera, sparse pulse events are converted into event frames through time surface processing. These frames are then reconstructed into a high-temporal-resolution video using spatiotemporal denoising and region of interest definition. The study introduces the multimodal model Qwen2.5-VL-7B and employs two distinct LoRA fine-tuning strategies for bearing fault classification. Strategy A utilizes OpenCV to extract key video frames for lightweight parameter injection. In contrast, Strategy B calls the model’s built-in video processing pipeline to fully leverage rich temporal information and capture dynamic details of the bearing’s operation. Classification experiments were conducted under three operating conditions and four rotational speeds. Strategy A and Strategy B achieved classification accuracies of 0.9247 and 0.9540, respectively, successfully establishing a novel fault diagnosis paradigm that progresses from non-contact sensing to end-to-end intelligent analysis.
Lü et al. (2025) studied this question.