Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 116, Issue -, Pages 40-61Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2018.06.026
Keywords
Bearing diagnostics; Time variable speed conditions; Novelty detection; Discrepancy analysis; Probabilistic approach
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Funding
- Eskom Power Plant Engineering Institute (EPPEI)
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Performing condition monitoring on critical machines such as gearboxes is essential to ensure that the machines operate reliably. However, many gearboxes are exposed to variable operating conditions which impede the condition inference task. Rolling element bearing component failures are important causes of gearbox failures and therefore robust bearing diagnostic techniques are required. In this paper, a rolling element bearing diagnostic methodology based on novelty detection is proposed for machines operating under variable speed conditions. The methodology uses the wavelet packet transform, order tracking and a feature modelling approach to generate a diagnostic metric in the form of a discrepancy measure. The probability distribution of the diagnostic metric, statistically conditioned on the corresponding operating conditions is estimated, whereafter the condition of the rolling bearing element is inferred. The rolling element bearing diagnostic methodology is validated on data from a phenomenological gearbox model and two experimental datasets. (C) 2018 Elsevier Ltd. All rights reserved.
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