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Multimodal Large Language Model-Enabled Machine Intelligent Fault Diagnosis Method with Non-Contact Dynamic Vision

Zihan Lu¹, Cuiying Sun², Xiang Li¹

¹Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)

|September 27, 2025

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This study introduces a novel non-contact method for bearing fault diagnosis using event cameras and AI. It achieves high accuracy in identifying faults, improving equipment reliability in smart manufacturing.

Area of Science:

Engineering
Artificial Intelligence
Sensor Technology

Background:

Smart manufacturing requires high equipment reliability and availability.
Traditional fault diagnosis methods using vibration sensors have limitations in adaptability, maintenance, and preprocessing.
There is a need for advanced, non-contact fault diagnosis techniques.

Purpose of the Study:

To pioneer the use of event camera data for bearing fault classification.
To fine-tune a multimodal large model (Qwen2.5-VL-7B) using dynamic visual information for fault diagnosis.
To establish a novel, end-to-end intelligent analysis paradigm for non-contact fault detection.

Main Methods:

Utilized high-temporal-resolution dynamic visual information from an event camera.

Keywords:

dynamic vision event camera fault diagnosis multimodal large models

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Processed sparse pulse events into event frames via time surface processing.

Reconstructed event frames into high-temporal-resolution video using spatiotemporal denoising and ROI definition.

Employed two LoRA fine-tuning strategies (Strategy A: OpenCV frame extraction; Strategy B: built-in video pipeline) with Qwen2.5-VL-7B for bearing fault classification.

Main Results:

Achieved classification accuracies of 0.9247 (Strategy A) and 0.9540 (Strategy B).
Demonstrated successful bearing fault classification under varying operating conditions and rotational speeds.
Validated the effectiveness of non-contact sensing and end-to-end intelligent analysis for fault diagnosis.

Conclusions:

Event camera-based dynamic visual information is effective for bearing fault diagnosis.
The multimodal large model fine-tuned with event data offers a promising new paradigm for smart manufacturing.
Non-contact sensing combined with advanced AI enables robust and efficient equipment health monitoring.