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The International Journal of Advanced Manufacturing Technology
Published in: The International Journal of Advanced Manufacturing Technology 5-8/2019

27-06-2019 | ORIGINAL ARTICLE

Vibration signal analysis using symbolic dynamics for gearbox fault diagnosis

Authors: Ruben Medina, Jean-Carlo Macancela, Pablo Lucero, Diego Cabrera, Mariela Cerrada, René-Vinicio Sánchez, Rafael E. Vásquez

Published in: The International Journal of Advanced Manufacturing Technology | Issue 5-8/2019

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Abstract

This paper addresses the use of two algorithms based on symbolic dynamics analysis of vibration signal for fault diagnosis in gearboxes. The symbolic dynamics algorithm (SDA) works by subdividing the phase space described by the Poincaré plot into several angular regions; then, a symbol is assigned to each region. The probability distributions generated by the set of symbols are considered as features for classification of faults in a gearbox. The peak symbolic dynamics algorithm (PSDA) is a variant that extracts a sequence of peaks from the vibration signals and then performs the phase-space subdivision and symbol coding. A gearbox vibration signal dataset is analyzed for classifying 10 types of faults. Fault classification is attained using a multi-class support vector machine. The highest accuracy attained using k-fold cross-validation is 100.0% for load L3 with SDA and 100% with load L2 with PSDA. The accuracy considering all signals in the gearbox dataset is 99.2% with SDA and 99.8% with PSDA. The algorithms proposed have the advantage of being simple, accurate, and fast, and they could be adapted for online condition monitoring.
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Metadata
Title
Vibration signal analysis using symbolic dynamics for gearbox fault diagnosis
Authors
Ruben Medina
Jean-Carlo Macancela
Pablo Lucero
Diego Cabrera
Mariela Cerrada
René-Vinicio Sánchez
Rafael E. Vásquez
Publication date
27-06-2019
Publisher
Springer London
Published in
The International Journal of Advanced Manufacturing Technology / Issue 5-8/2019
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-019-03858-0

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