A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence.

Area of Science:

• Engineering
• Computer Science
• Artificial Intelligence

Background:

• Industry 4.0 and intelligent manufacturing necessitate advanced fault diagnosis for servo motors.
• Traditional methods face limitations in efficiency, accuracy, and real-time performance in complex industrial environments.
• Resource-constrained edge devices require computationally efficient diagnostic models.

Purpose of the Study:

• To develop a novel, accurate, and efficient fault diagnosis approach for servo motors.
• To optimize the diagnostic model for deployment on edge devices within industrial IoT scenarios.
• To overcome the limitations of traditional fault diagnosis methods.

Main Methods:

• Integration of multi-scale convolutional neural networks (MSCNNs), long short-term memory networks (LSTM), and attention mechanisms.
• Application of knowledge distillation and model quantization for edge device optimization.
• Development of a computationally efficient deep learning model for real-time fault identification.

Main Results:

• The proposed method achieves high diagnostic accuracy for servo motor faults on edge devices.
• Significant reduction in computational complexity while maintaining performance.
• Demonstrated excellent inference speed suitable for industrial IoT applications.

Conclusions:

• The novel deep learning approach effectively addresses the challenges in servo motor fault diagnosis.
• The optimized model is well-suited for deployment on resource-constrained edge nodes.
• The method offers a practical solution for efficient and accurate fault detection in intelligent manufacturing.