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Performance Assessment of Gear Condition Indicators in Detecting Progressive Gear Tooth Crack

[+] Author Affiliations
Yang Luo, Natalie Baddour, Ming Liang

University of Ottawa, Ottawa, ON, Canada

Paper No. DETC2017-67460, pp. V008T12A010; 11 pages
doi:10.1115/DETC2017-67460
From:
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 8: 29th Conference on Mechanical Vibration and Noise
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5822-6
  • Copyright © 2017 by ASME

abstract

Gear condition indicators are one of the most important gear transmission fault detection and diagnosis techniques. Various kinds of condition indicators for different kinds of gear fault types (e.g. tooth crack, wear, eccentricity, etc.) have been proposed in the past several decades. However, their relative effectiveness, especially in light of some newly proposed indicators, on gear fault detection or diagnosis has not been fully evaluated. Performance assessment of gear fault condition indicators is not only helpful in designing new advanced indicators but also important for the development of a reliable Condition Based Maintenance (CBM) system. The objective of this paper is to verify and compare the relative performances of twenty-one selected gear condition indicators as applied to a progressive gear tooth crack under constant load and speed working conditions. The main goals are to identify which indicators are sensitive to the fault or have the capability to detect the initial tooth crack and therefore to recommend the most effective gear condition indicators. Dynamic simulations were used to generate the vibration signals which reflect the real underlying vibration behavior of the transmission system. Based on the simulated results, the performances of the selected indicators under noise-free as well as various signal to noise ratio conditions were evaluated and compared. Results indicate that many of the selected indicators are effective for the detection of the progressive tooth crack only under noise-free conditions, and the indicators that only consider time or frequency domain features, such as RMS, Kurtosis, energy ratio, sideband index, are generally less able to detect a tooth crack at an early stage compared to the methods based on reconstructed signals, such as the NA4, FM4, M6A, M8A.

Copyright © 2017 by ASME

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