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Block feature selection based on NSGA-II applied to fault diagnosis of gearboxes by Xianhua Chen; Zhigang Tian; Meng Rao is a Engineering article available to read on EtoBox.

What is Block feature selection based on NSGA-II applied to fault diagnosis of gearboxes about?

A hybrid model with multiple sub-classifier is a powerful classifier for intelligent fault diagnosis of gearboxes. Each sub-classifier is used to deal with different gear or bearing faults, and thus each should have its own optimal feature group. However, these sub-classifiers in the hybrid model used the same feature group in most reported feature selection methods. To overcome this problem, this study brings some insights into optimization on subclassifiers. This proposed method, known as block feature selection (BFS), optimizes the feature group for only one fault type in each sub-classifier. The selected groups are then amalgamated to form the optimal features for all types of gearbox failures, with corresponding labels in the hybrid model. Each sub-classifier is considered to be one block. In each block, the concept of NSGA-II is used to select features for its corresponding sub-classifier. Then, a new sorting algorithm is proposed to find the best feature group of sub-classifier. In general, BFS can optimize features of each sub-classifier to find the best feature group for hybrid model. The effectiveness of BFS is validated by experimental data from bearing and gear faults i

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Author

Xianhua Chen; Zhigang Tian; Meng Rao

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)