Sensors, Vol. 23, Pages 4792: A Hybrid Feature Selection and Multi-Label Driven Intelligent Fault Diagnosis Method for Gearbox

Sensors, Vol. 23, Pages 4792: A Hybrid Feature Selection and Multi-Label Driven Intelligent Fault Diagnosis Method for Gearbox
Sensors doi: 10.3390/s23104792
Authors:
Di Liu
Xiangfeng Zhang
Zhiyu Zhang
Hong Jiang

Gearboxes are utilized in practically all complicated machinery equipment because they have great transmission accuracy and load capacities, so their failure frequently results in significant financial losses. The classification of high-dimensional data remains a difficult topic despite the fact that numerous data-driven intelligent diagnosis approaches have been suggested and employed for compound fault diagnosis in recent years with successful outcomes. In order to achieve the best diagnostic performance as the ultimate objective, a feature selection and fault decoupling framework is proposed in this paper. That is based on multi-label K-nearest neighbors (ML-kNN) as classifiers and can automatically determine the optimal subset from the original high-dimensional feature set. The proposed feature selection method is a hybrid framework that can be divided into three stages. The Fisher score, information gain, and Pearson’s correlation coefficient are three filter models that are used in the first stage to pre-rank candidate features. In the second stage, a weighting scheme based on the weighted average method is proposed to fuse the pre-ranking results obtained in the first stage and optimize the weights using a genetic algorithm to re-ra...

Source: Sensors - May 16, 2023 Category: Biotechnology Authors: Di Liu Xiangfeng Zhang Zhiyu Zhang Hong Jiang Tags: Article Source Type: research