@Article{iasc.2020.013924,
AUTHOR = {R. Uma Maheswari, R. Umamaheswari},
TITLE = {Wind Turbine Drivetrain Expert Fault Detection System: Multivariate Empirical Mode Decomposition based Multi-sensor Fusion with Bayesian Learning Classification},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {479--488},
URL = {http://www.techscience.com/iasc/v26n3/40007},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2020.013924}
}