A discrepancy analysis methodology for rolling element bearing diagnostics under variable speed conditions. (1st February 2019)

• Record Type: Journal Article
• Authors: Schmidt, Stephan; Heyns, P. Stephan; Gryllias, Konstantinos C.
• Is Part Of: Mechanical systems and signal processing. Volume 116(2019)
• Page Start: 40
• Page End: 61
• Publication Date: 2019-02-01
• Subjects: Bearing diagnostics -- Time variable speed conditions -- Novelty detection -- Discrepancy analysis -- Probabilistic approach; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.06.026
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A discrepancy analysis framework for bearing diagnostics is proposed. Only healthy historical vibration data are required to apply the methodology. The diagnostic metric is processed to be robust under varying speed conditions. A deteriorating bearing can be detected and localised under varying speed conditions. The framework is validated on numerical as well as two experimental datasets. Abstract: 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.