Hyperparameter optimization for enabling multi-level feature classification in a wind turbine gearbox

Vamsi Inturi1 , Karthick Chetti2 , Shreyas N3 , Sabareesh G R4

1, 2, 3, 4Department of Mechanical Engineering, BITS Pilani, Hyderabad Campus, India

1Corresponding author

Vibroengineering PROCEDIA, Vol. 29, 2019, p. 24-30. https://doi.org/10.21595/vp.2019.21146
Received 29 October 2019; accepted 5 November 2019; published 28 November 2019

Copyright © 2019 Vamsi Inturi, et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract.

Majority of the previous research investigations on fault diagnostics in a wind turbine gearbox are limited to binary classification, i.e., either detecting the type of defect or severities of defect. However, wind turbine gearbox consists of multiple speed stages and components, therefore performing the binary classification is not adequate. In the present study, a multi-level classification scheme which is capable of classifying the defects by stage, component, type of defect and severity level is proposed. Experiments are performed and the response is recorded through vibration, acoustic signal and lubrication oil analysis. Later, an integrated multi-variable feature set is achieved by combining the statistical features of the above mentioned individual condition monitoring strategies. Further, the obtained integrated multi-variable feature set is subjected to multi-level classification using various machine learning models and the learning model that best suits for carrying the multi-level classification is investigated. Finally, the hyperparameters of the learning models are optimized by an iterative process of reducing the objective function. It is observed that, optimized support vector machine model has yielded favorable results when compared to other machine learning models with the overall classification accuracy of 82.52 % for the four-level classification.

Graphical Abstract

Highlights
  • Integration of individual condition monitoring techniques to device a multi-variable integrated feature set
  • Multi-level feature classification on a wind turbine gearbox using various machine learning algorithms
  • Investigating the best algorithm after optimizing the hyperparameters of the learning models for carrying the multi-level classification
  • Predicting the severity of gears as well as bearings of a wind turbine gearbox operating under fluctuating speeds

Keywords: vibration analysis, acoustic signal analysis, fault diagnosis, wind turbine gearbox, multi-level classification, machine learning algorithms.

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