Open Access
ARTICLE
Wind Turbine Drivetrain Expert Fault Detection System: Multivariate Empirical Mode Decomposition based Multi-sensor Fusion with Bayesian Learning Classification
R. Uma Maheswari1,*, R. Umamaheswari2
1 Research Scholar, Anna University, Assistant Professor, Department of ECE, Rajalakshmi Institute of Technology, Chennai, India
2 Professor, Velammal Engineering College, Chennai, India
* Corresponding Author: R.Uma Maheswari,
Intelligent Automation & Soft Computing 2020, 26(3), 479-488. https://doi.org/10.32604/iasc.2020.013924
Abstract
To enhance the predictive condition-based maintenance (CBMS), a reliable
automatic Drivetrain fault detection technique based on vibration monitoring is
proposed. Accelerometer sensors are mounted on a wind turbine drivetrain at
different spatial locations to measure the vibration from multiple vibration
sources. In this work, multi-channel signals are fused and monocomponent
modes of oscillation are reconstructed by the Multivariate Empirical Mode
Decomposition (MEMD) Technique. Noise assisted methodology is adapted to
palliate the mixing of modes with common frequency scales. The instantaneous
amplitude envelope and instantaneous frequency are estimated with the Hilbert
transform. Low order and high order statistical moments, signal feature
descriptors and randomness measures (entropy) are extracted as truthful
features. The feature set is fed into the Bayes classifiers to compare the
detection performance. From the analysis it is found that the proposed method
is well performed with the Dynamic Bayes Belief Network Classifier showing the
detection accuracy of 97.69%. To validate the results, the NRELWind Turbine
Drivetrain benchmarking dataset is used.
Keywords
Cite This Article
R. Uma Maheswari and R. Umamaheswari, "Wind turbine drivetrain expert fault detection system: multivariate empirical mode decomposition based multi-sensor fusion with bayesian learning classification,"
Intelligent Automation & Soft Computing, vol. 26, no.3, pp. 479–488, 2020. https://doi.org/10.32604/iasc.2020.013924
Citations