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Carriage error identification based on cross-correlation analysis and wavelet transformation.

Donghui Mu1, Dongju Chen, Jinwei Fan

  • 1School of Mechanical & Electrical Engineering, Beijing University of Chemical Technology, Chao Yang District, Beijing 100029, China. badyun@163.com

Sensors (Basel, Switzerland)
|September 27, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for detecting carriage errors in guideway systems using a multi-body system model. Wavelet analysis and cross-correlation accurately pinpoint error sources, improving precision.

Keywords:
carriage errorcross-correlation analysiserror identificationmulti-body systemwavelet transform

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Area of Science:

  • Mechanical Engineering
  • Signal Processing

Background:

  • Guideway systems are susceptible to various errors affecting performance.
  • Accurate identification of carriage errors is crucial for system maintenance and optimization.

Purpose of the Study:

  • To propose a novel method for identifying carriage errors in guideway systems.
  • To develop a mathematical model for analyzing guideway system errors.
  • To enhance the precision of error source identification.

Main Methods:

  • Developed a general mathematical model of a guideway system using the multi-body system method.
  • Utilized wavelet representation for workpiece flatness measurements from a PGI1240 profilometer.
  • Applied cross-correlation analysis to identify carriage error sources.
  • Created an error model based on experimental results of low-frequency signal components.

Main Results:

  • The proposed model allows for the measurement of most error sources within the guideway system.
  • Wavelet analysis demonstrated high precision in identifying error sources.
  • Cross-correlation analysis effectively pinpointed the origin of carriage errors.

Conclusions:

  • The novel method provides a precise approach to identifying carriage errors in guideway systems.
  • The multi-body system model and wavelet analysis are effective tools for error source identification.
  • High identification precision is achievable for test signals using this methodology.