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Ball Screw Fault Diagnosis Based on Wavelet Convolution Transfer Learning.

Yifan Xie1,2, Chang Liu1,2, Liji Huang1,2

  • 1Key Laboratory of Advanced Equipment Intelligent Manufacturing Technology of Yunnan Province, Kunming University of Science & Technology, Kunming 650500, China.

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Summary
This summary is machine-generated.

This study introduces an advanced method for diagnosing ball screw faults in CNC machines. The technique enhances signal analysis and uses transfer learning for accurate health assessment across different sensor locations.

Keywords:
adaptive batch normalization algorithmball screwconvolutional neural networkfault diagnosistransfer learning

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Area of Science:

  • Mechanical Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Ball screws are critical components in CNC machine tool feed systems, directly impacting overall machine performance.
  • Assessing the health of ball screws is essential for preventing failures and ensuring operational efficiency.
  • Weak signals and sensor placement limitations pose significant challenges in ball screw fault diagnosis.

Purpose of the Study:

  • To develop a robust fault diagnosis and health assessment method for ball screws in CNC machines.
  • To address challenges related to weak signal interference and sensor installation position limitations.
  • To improve the accuracy and reliability of ball screw fault detection.

Main Methods:

  • A wavelet convolution structure was employed to enhance the extraction of time and frequency domain features from ball screw signals.
  • Transfer learning, specifically Jointly Distributed Adaptation (JDA), was utilized to enable diagnosis across different measurement positions.
  • Adaptive Batch Normalization (AdaBN) was integrated to improve the model's migration diagnosis accuracy.
  • Experiments were conducted on a self-made lead screw fatigue test bench, including tests with added background noise to assess robustness.

Main Results:

  • The proposed method effectively extracts fault diagnosis knowledge from ball screw data.
  • The technique successfully identifies and diagnoses position faults of the nut seat.
  • The model demonstrated robustness when tested with added background noise, indicating reliable performance in real-world conditions.
  • Cross-measurement position diagnosis was achieved with improved accuracy.

Conclusions:

  • The developed method, combining wavelet convolution and transfer learning, offers an effective solution for ball screw fault diagnosis and health assessment.
  • The approach overcomes limitations of weak signals and sensor placement, enhancing diagnostic capabilities.
  • The findings contribute to improving the reliability and maintenance of CNC machine tools through advanced fault detection techniques.