Diagnostics and prognostics of planetary gearbox using CWT, auto regression (AR) and K-means algorithm

Imthiyas Manarikkal, Faris Elasha, David Mba

    Research output: Contribution to journalArticlepeer-review

    16 Citations (Scopus)
    174 Downloads (Pure)

    Abstract

    Condition monitoring of machine is recognized as effective strategy for undertaking the maintenance in wide variety of industries. Planetary gearbox is a critical component in helicopters, wind turbines, hybrid vehicles and so forth. Planetary gearbox are complex in nature due to its size and meshing components. Condition monitoring and fault diagnosis of planetary gearbox is challenging due to complexity in dependable fault extraction from raw vibration signal. The mechanism of planetary gearbox is complex as there are several gears meshing at the same time. To find out the nature of fault and defective component in planetary gearbox is difficult. In this paper, the fault detection and fault type identification diagnostic approach using auto regression model (AR) and continuous wavelet transforms (CWT) by considering different frequency range is established. The experimental research conducted with different type of fault vibration signals in the gearbox have been diagnosed and identified the fault type using AR Modelling, Impulse and Shape Factor for validation purposes. The unique behaviors and fault characteristics of planetary gearboxes are identified and analyzed. The fault frequency identification and extraction of features from the non-stationary signals in different fault severity level of vibration data demonstrates the reliability of proposed method. The developed algorithm adds efficacy in detecting the nature of fault and defective component without performing a visual inspection.

    Original languageEnglish
    Article number108314
    JournalApplied Acoustics
    Volume184
    Early online date14 Aug 2021
    DOIs
    Publication statusPublished - 15 Dec 2021

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in Applied Acoustics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Acoustics, 184, (2021) DOI: 10.1016/j.apacoust.2021.108314

    © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

    Keywords

    • Auto regression modelling
    • Condition monitoring
    • Continuous wavelet transforms
    • Diagnostics
    • Fault classification
    • Prognostics

    ASJC Scopus subject areas

    • Acoustics and Ultrasonics

    Fingerprint

    Dive into the research topics of 'Diagnostics and prognostics of planetary gearbox using CWT, auto regression (AR) and K-means algorithm'. Together they form a unique fingerprint.

    Cite this