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Detecting weak position fluctuations from encoder signal using singular spectrum analysis.

Xiaoqiang Xu1, Ming Zhao1, Jing Lin2

  • 1Shaanxi Key Laboratory of Mechanical Product Quality Assurance and Diagnostics, School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi Province, China.

ISA Transactions
|September 16, 2017
PubMed
Summary
This summary is machine-generated.

Singular Spectrum Analysis (SSA) effectively detects subtle position signal fluctuations from machine encoders. This method offers a reliable alternative for machinery condition monitoring, outperforming other techniques.

Keywords:
EMDEncoderMachine toolSignal decompositionSingular spectrum analysis (SSA)

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Machine health monitoring relies on detecting mechanical faults indicated by position signal fluctuations.
  • Encoder signals contain trends and noise, obscuring weak fault-related fluctuations.
  • Existing methods struggle with large trends and complex, amplitude-modulated fluctuations.

Purpose of the Study:

  • To introduce Singular Spectrum Analysis (SSA) for detecting weak position fluctuations in encoder signals.
  • To demonstrate SSA's ability to decompose complex signals into interpretable components.
  • To validate SSA's performance against Empirical Mode Decomposition (EMD) and real-world data.

Main Methods:

  • Singular Spectrum Analysis (SSA) applied to encoder position signals.
  • Numerical simulations to evaluate SSA's capability and accuracy.
  • Analysis of linear encoder signals from CNC machine tools.

Main Results:

  • SSA successfully decomposes complex encoder signals into trend, periodic fluctuations, and noise.
  • Numerical simulations show SSA outperforms EMD in accuracy and capability.
  • Analysis of CNC machine data confirms SSA's feasibility for identifying fluctuation magnitudes and sources.

Conclusions:

  • SSA is a feasible and reliable method for machinery condition monitoring.
  • SSA provides a promising alternative to vibration-based monitoring schemes.
  • The technique effectively detects weak fluctuations for improved machine health assessment.