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

by Mariela Cerrada 1,2,*, René Vinicio Sánchez 2,4,†, Diego Cabrera 2,†, Grover Zurita 2,† and Chuan Li 3,†
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
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Vittorio M. N. Passaro
Sensors 2015, 15(9), 23903-23926; https://doi.org/10.3390/s150923903
Received: 11 July 2015 / Revised: 30 August 2015 / Accepted: 7 September 2015 / Published: 18 September 2015
(This article belongs to the Section Physical Sensors)
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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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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