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Intelligent Monitoring System Based on Noise-Assisted Multivariate Empirical Mode Decomposition Feature Extraction

Le Fa Zhao1, Shahin Siahpour2, Mohammad Reza Haeri Yazdi3

  • 1School of General Education, Shenyang Sport University, Shenyang 110115, China.

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This study introduces novel features from multivariate empirical mode decomposition (MEMD) for analyzing complex rotating machinery vibrations. These features enhance early fault detection in wind turbine gearboxes, overcoming limitations of traditional methods.

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Conventional time- and frequency-domain analyses of rotating machinery vibration signals are limited by signal nonlinearity and nonstationarity.
  • These limitations can lead to misleading results in diagnosing machinery health.
  • Effective analysis requires advanced signal processing techniques to capture complex dynamics.

Purpose of the Study:

  • To introduce novel features derived from Multivariate Empirical Mode Decomposition (MEMD) for improved rotating machinery fault detection.
  • To address the shortcomings of traditional feature extraction methods in analyzing nonlinear and nonstationary vibration signals.
  • To evaluate the efficacy of these new features in diagnosing early faults in complex systems like wind turbine gearboxes.

Main Methods:

  • Utilized Multivariate Empirical Mode Decomposition (MEMD) to decompose vibration signals into Intrinsic Mode Functions (IMFs).
  • Extracted two types of feature vectors: energy moments of effective IMFs and spectral amplitudes at characteristic frequencies.
  • Employed a correlation factor to identify effective IMFs and a noise-assisted extension (NA-MEMD) to mitigate noise.
  • Integrated the proposed feature vectors into a backpropagation neural network for fault classification.

Main Results:

  • The proposed feature vectors demonstrated superior capability in health condition monitoring compared to traditional features, as indicated by a discrimination factor.
  • Noise-assisted MEMD (NA-MEMD) effectively reduced the impact of noise on the analysis.
  • The feature vectors were successfully utilized in a backpropagation neural network for intelligent fault detection.
  • Early faults in complex rotating machinery were accurately diagnosed.

Conclusions:

  • The novel features derived from MEMD and NA-MEMD are highly effective for intelligent fault detection in complex rotating machinery.
  • These features overcome the limitations of conventional methods, enabling accurate diagnosis of early-stage faults.
  • The proposed approach offers a robust solution for condition monitoring and predictive maintenance of critical machinery like wind turbine gearboxes.