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Complexity and entropy representation for machine component diagnostics.

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Summary
This summary is machine-generated.

The Complexity-Entropy Causality Plane (CECP) effectively monitors mechanical component health using vibration signals. This method accurately detects faults in bearings and gears, offering a robust solution for machine condition monitoring.

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Area of Science:

  • Engineering
  • Data Science
  • Signal Processing

Background:

  • Machine condition monitoring relies on analyzing sensor data to detect component degradation.
  • Traditional methods can be sensitive to noise, trends, and stationarity, requiring extensive preprocessing.
  • A need exists for robust, low-preprocessing methods for analyzing time series data from mechanical components.

Purpose of the Study:

  • To evaluate the effectiveness of the Complexity-Entropy Causality Plane (CECP) for machine condition monitoring and fault diagnostics.
  • To assess CECP's ability to represent and differentiate health states of rotary machine components, specifically bearings and gears.
  • To determine optimal parameters for CECP in different application contexts.

Main Methods:

  • Utilized the Complexity-Entropy Causality Plane (CECP), a two-dimensional space defined by normalized permutation entropy and Jensen-Shannon complexity.
  • Applied CECP to vibration signals from roller bearings and gears under various degradation states.
  • Investigated the impact of signal length and CECP parameters (embedding dimension D, embedding delay τ) on classification accuracy.

Main Results:

  • CECP successfully differentiated between healthy and degraded states of bearings and gears with high accuracy.
  • CECP generated linearly separable classes for fault classification, improving with increased signal length.
  • Achieved 90%-100% accuracy for bearing fault classification and 85%-100% for gear fault classification with signal lengths of 16,384.
  • Identified optimal CECP parameters: D=[4,5,6], τ=[1,2,3] for bearings and D=[4,5], τ=[1,5] for gears.

Conclusions:

  • CECP is a powerful and parsimonious tool for machine condition monitoring and fault diagnostics.
  • The method is robust to noise, stationarity, and trends, requiring minimal signal preprocessing.
  • CECP offers high accuracy and class separability for detecting faults in rotary machine components, adaptable via parameter tuning.