Gear fault diagnosis using Autogram analysis

Advances in Mechanical Engineering. 2018;10 DOI 10.1177/1687814018812534


Journal Homepage

Journal Title: Advances in Mechanical Engineering

ISSN: 1687-8132 (Print); 1687-8140 (Online)

Publisher: SAGE Publishing

LCC Subject Category: Technology: Mechanical engineering and machinery

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML



Adel Afia

Chemseddine Rahmoune

Djamel Benazzouz


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 16 weeks


Abstract | Full Text

Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal processing techniques, Kurtogram has been widely used to detect gear failure. Usually, vibration signals contain a relatively strong non-Gaussian noise which makes the defective frequencies non-dominant in the spectrum compared to the discrete components, which reduce the performance of the above method. Autogram is a new sophisticated enhancement of the conventional Kurtogram. The modern approach decomposes the data signal by Maximal Overlap Discrete Wavelet Packet Transform into frequency bands and central frequencies called nodes. Subsequently, the unbiased autocorrelation of the squared envelope for each node is computed to select the node with the highest kurtosis value. Finally, Fourier transform is applied to that squared envelope to extract the fault signature. In this article, the proposed method is tested and compared to Fast Kurtogram for gearbox fault diagnosis using experimental vibration signals. The experimental results improve the detectability of the proposed method and affirm its effectiveness.