Bearing fault diagnosis based on Kepler algorithm and attention mechanism

Area of Science:

• Mechanical Engineering
• Artificial Intelligence
• Signal Processing

Background:

• Bearings are critical components in rotating machinery, susceptible to damage.
• Effective bearing fault diagnosis is essential for operational reliability and safety.
• Existing diagnostic methods may lack sufficient accuracy or efficiency.

Purpose of the Study:

• To improve the accuracy and efficiency of bearing fault diagnosis.
• To introduce an optimized neural network approach using the Kepler algorithm.
• To leverage attention mechanisms for enhanced feature extraction in fault detection.

Main Methods:

• Developed the Kepler algorithm for optimizing neural network weights.
• Integrated attention mechanisms to focus on relevant diagnostic features.
• Validated the model using third-party bearing datasets.

Main Results:

• The Kepler algorithm and attention mechanisms significantly improved diagnostic accuracy.
• The proposed model demonstrated superior performance compared to other algorithms.
• Efficient extraction of key features was achieved, enhancing diagnostic capabilities.

Conclusions:

• The Kepler algorithm and attention mechanisms offer a superior approach to bearing fault diagnosis.
• This method provides a feasible and effective solution for condition monitoring of rotating machinery.
• The findings contribute to advancing the reliability and predictive maintenance of industrial equipment.