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Switching algorithm for maglev train double-modular redundant positioning sensors.

Ning He1, Zhiqiang Long, Song Xue

  • 1College of Mechatronics Engineering and Automation, National University of Defense Technology, Changsha 410073, China. hening0606@126.com

Sensors (Basel, Switzerland)
|November 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a reliable switching algorithm for redundant maglev train positioning sensors, using adaptive prediction and wavelet analysis to overcome signal loss caused by stator joint gaps.

Keywords:
adaptive predictionmaglev trainpositioning sensorswitching algorithmwavelet analysis

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Area of Science:

  • Engineering
  • Transportation Technology
  • Signal Processing

Background:

  • High-resolution positioning for maglev trains relies on detecting the long stator's tooth-slot structure.
  • Large joint gaps between stator sections can invalidate sensor signals, compromising positioning accuracy.
  • Double-modular redundant positioning sensors are employed to enhance system reliability.

Purpose of the Study:

  • To develop and validate a robust switching algorithm for redundant positioning sensors in maglev trains.
  • To address the challenge of signal invalidation caused by stator joint gaps.
  • To improve the reliability and accuracy of maglev train positioning systems.

Main Methods:

  • Adaptive prediction is applied to sensor signals to detect anomalies.
  • Prediction errors are utilized to trigger the switching between redundant sensors.
  • Wavelet analysis is employed to suppress measurement noise while preserving critical signal features related to stator gaps.
  • Time delay characteristics were analyzed for algorithm simplification.

Main Results:

  • The proposed switching algorithm effectively manages redundant sensor data.
  • Wavelet analysis successfully filters disturbances without distorting essential positioning information.
  • The simplified algorithm maintains high reliability and accuracy in experimental verification.
  • The system demonstrates resilience against signal loss from stator joint gaps.

Conclusions:

  • The developed switching algorithm enhances the reliability of maglev train positioning systems.
  • The combination of adaptive prediction and wavelet analysis provides a robust solution for sensor redundancy management.
  • The simplified algorithm is effective and experimentally validated for practical applications.