Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal.

Description: There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.
Universidad Polit?cnica Salesiana
https://www.ncbi.nlm.nih.gov/pubmed/26393603

Topic: FAULT DIAGNOSIS
FEATURE SELECTION
GEARBOX
GENETIC ALGORITHMS
NEURAL NETWORKS
VIBRATION SIGNAL

Get full text: http://repositorio.educacionsuperior.gob.ec/handle/28000/3190