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Proceedings Paper

Toward dynamic model-based prognostics for transmission gears
Author(s): Wenyi Wang
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Paper Abstract

This paper presents a novel methodology for the diagnosis and prognosis of crucial gear faults, such as gear tooth fatigue cracking. Currently, an effective detection of tooth cracking can be achieved by using the autoregressive (AR) modeling approach, where the gear vibration signal is modeled by an AR model and gear tooth cracking is detected by identifying the sudden changes in the model's error signal. The model parameters can be estimated under the criteria of minimum power or maximum kurtosis of model errors. However, these model parameters possess no physical meaning about the monitored gear system. It is proposed that the AR model be replaced by a gear dynamics model (GDM) that contains physically meaningful parameters, such as mass, damping and stiffness. By identifying and tracking the changes in the parameters, it is possible to make diagnosis and prognosis of gear faults. For example, a reduction in mesh stiffness may indicate cracking of a gear tooth. Towards physical model-based prognosis, an adaptive (or optimization) strategy has been developed for approximating a gear signal using a simplified gear signal model. Preliminary results show that this strategy provides a feasible adaptive process for updating model parameters based on measured gear signal.

Paper Details

Date Published: 16 July 2002
PDF: 11 pages
Proc. SPIE 4733, Component and Systems Diagnostics, Prognostics, and Health Management II, (16 July 2002); doi: 10.1117/12.475505
Show Author Affiliations
Wenyi Wang, Defence Science and Technology Organisation (Australia)

Published in SPIE Proceedings Vol. 4733:
Component and Systems Diagnostics, Prognostics, and Health Management II
Peter K. Willett; Thiagalingam Kirubarajan, Editor(s)

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