An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission. (1st February 2017)

• Record Type: Journal Article
• Authors: Aye, S.A.; Heyns, P.S.
• Is Part Of: Mechanical systems and signal processing. Volume 84:Part A(2017)
• Page Start: 485
• Page End: 498
• Publication Date: 2017-02-01
• Subjects: Gaussian process regressions -- Remaining useful life -- Mean function -- Covariance function; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2016.07.039
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper proposes an optimal Gaussian process regression (GPR) for the prediction of remaining useful life (RUL) of slow speed bearings based on a novel degradation assessment index obtained from acoustic emission signal. The optimal GPR is obtained from an integration or combination of existing simple mean and covariance functions in order to capture the observed trend of the bearing degradation as well the irregularities in the data. The resulting integrated GPR model provides an excellent fit to the data and improves over the simple GPR models that are based on simple mean and covariance functions. In addition, it achieves a low percentage error prediction of the remaining useful life of slow speed bearings. These findings are robust under varying operating conditions such as loading and speed and can be applied to nonlinear and nonstationary machine response signals useful for effective preventive machine maintenance purposes. Highlights: An optimal GPR for prediction of RUL was proposed. Integration of simple mean and covariance functions. The findings show the resulting integrated GPR improves over simple GPR models. The integrated GPR predicts RUL with small errors. The findings are robust under varying operating conditions.