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Sensors 2015, 15(9), 23903-23926; doi:10.3390/s150923903

Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

1
Control Systems Department, Universidad de Los Andes, Mérida 5101, Venezuela
2
Mechanical Engineering Department, Universidad Politécnica Salesiana, Cuenca 010150, Ecuador
3
Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control, Chongqing Technology and Business University, Chongqing 400067, China
4
Mechanics Department, Universidad Nacional de Educación a Distancia, Madrid 28040, Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 11 July 2015 / Revised: 30 August 2015 / Accepted: 7 September 2015 / Published: 18 September 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [20637 KB, uploaded 18 September 2015]   |  

Abstract

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%. View Full-Text
Keywords: fault diagnosis; gearbox; vibration signal; feature selection; genetic algorithms; neural networks fault diagnosis; gearbox; vibration signal; feature selection; genetic algorithms; neural networks
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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. (CC BY 4.0).

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MDPI and ACS Style

Cerrada, M.; Sánchez, R.V.; Cabrera, D.; Zurita, G.; Li, C. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal. Sensors 2015, 15, 23903-23926.

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