Nonlinear Parameters for Monitoring Gear: Comparison Between Lempel-Ziv, Approximate Entropy, and Sample Entropy ComplexityReport as inadecuate




Nonlinear Parameters for Monitoring Gear: Comparison Between Lempel-Ziv, Approximate Entropy, and Sample Entropy Complexity - Download this document for free, or read online. Document in PDF available to download.

Shock and Vibration - Volume 2015 2015, Article ID 959380, 12 pages -

Research ArticleDepartment of Mechanical Engineering, École de Technologie supérieure, 1100 Notre-Dame Street West, Montreal, QC, Canada H3C 1K3

Received 15 September 2014; Revised 16 March 2015; Accepted 14 April 2015

Academic Editor: Miguel Neves

Copyright © 2015 Mourad Kedadouche et al. 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.

Abstract

Vibration analysis is the most used technique for defect monitoring failures of industrial gearboxes. Detection and diagnosis of gear defects are thus crucial to avoid catastrophic failures. It is therefore important to detect early fault symptoms. This paper introduces signal processing methods based on approximate entropy ApEn, sample entropy SampEn, and Lempel-Ziv Complexity LZC for detection of gears defects. These methods are based on statistical measurements exploring the regularity of vibratory signals. Applied to gear signals, the parameter selection of ApEn, SampEn, and LZC calculation is first numerically investigated, and appropriate parameters are suggested. Finally, an experimental study is presented to investigate the effectiveness of these indicators and a comparative study with traditional time domain indicators is presented. The results demonstrate that ApEn, SampEn, and LZC provide alternative features for signal processing. A new methodology is presented combining both Kurtosis and LZC for early detection of faults. The results show that this proposed method may be used as an effective tool for early detection of gear faults.





Author: Mourad Kedadouche, Marc Thomas, Antoine Tahan, and Raynald Guilbault

Source: https://www.hindawi.com/



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