A Bayesian approach for shaft centre localisation in journal bearings. (15th July 2022)

• Record Type: Journal Article
• Authors: Lindley, C.A.; Beamish, S.; Dwyer-Joyce, R.S.; Dervilis, N.; Worden, K.
• Is Part Of: Mechanical systems and signal processing. Volume 174(2022)
• Publication Date: 2022-07-15
• Subjects: Journal bearing -- Oil film thickness measurement -- Ultrasound -- Shaft centre localisation -- Gaussian Process -- Bayesian statistics; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109021
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: It has been shown that ultrasonic techniques work well for online measuring of circumferential oil film thickness profile in journal bearings; unfortunately, they can be limited by their measuring range and unable to capture details of the film all around the bearing circumference. Attempts to model the film thickness over the full range of the bearing rely on deterministic approaches, which assume the observations to be true with absolute certainty. Unaccounted uncertainties of the film thickness may lead to a cascade of inaccurate predictions for subsequent calculations of hydrodynamic parameters. In the present work, a probabilistic framework is proposed to model the film thickness with Gaussian Processes. The results are then used to estimate the location of the bearing shaft under various operational conditions. A further step in the process involves using the newly-constructed dataset to generate likelihood maps displaying the probable location of the shaft centre, given the bearing rotational speed and applied static load. The results offer the possibility to visualise the confidence of the predictions and allow the true location to be found within an area of high probability within the bearing's bore. Highlights: A novel approach for shaft centre localisation in journal bearings is proposed. A probabilistic model is developed with ultrasonic experimental measurements. The oil film thickness is modelled with Gaussian Processes. Likelihood maps of shaft centre location are constructed. A more informed analysis is possible when accounting for location uncertainties.