Journal bearing performance prediction using machine learning and octave-band signal analysis of sound and vibration measurements

• Autorzy: Moschopoulos Marios , Rossopoulos Georgios N. , Papadopoulos Christos I.

Identyfikatory

DOI

10.2478/pomr-2021-0041

• Języki publikacji: EN

Abstrakty

EN

Journal and thrust bearings utilise hydrodynamic lubrication to reduce friction and wear between the shaft and the bearing. The process to determine the lubricant film thickness or the actual applied load is vital to ensure proper and trouble-free operation. However, taking accurate measurements of the oil film thickness or load in bearings of operating engines is very difficult and requires specialised equipment and extensive experience. In the present work, the performance parameters of journal bearings of the same principal dimensions are measured experimentally, aiming at training a Machine Learning (ML) algorithm capable of predicting the loading condition of any similar bearing. To this end, an experimental procedure using the Bently Nevada Rotor Kit 4 is set up, combined with sound and vibration measurements in the vicinity of the journal bearing structure. First, sound and acceleration measurements for different values of bearing load and rotational speed are collected and post-processed utilising 1/3 octave band analysis techniques, for parametrisation of the input datasets of the ML algorithms. Next, several ML algorithms are trained and tested. Comparison of the results produced by each algorithm determines the fittest one for each application. The results of this work demonstrate that, in a laboratory environment, the operational parameters of journal bearings can be efficiently identified utilising non-intrusive sound and vibration measurements. The presented approach may substantially improve bearing condition identification and monitoring, which is an imperative step to prevent journal bearing failures and conduct condition-based maintenance.

Słowa kluczowe

EN

journal bearing loading condition; RK4 Bently Nevada; vibration and acoustic pressure signal; octave band signal analysis; machine learning algorithms

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2021
• Tom: nr 3
• Strony: 137--149
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Moschopoulos Marios

• National Technical University of Athens Iroon Polytechniou 9, 15772 Zografos Greece

autor

Rossopoulos Georgios N.

• National Technical University of Athens Iroon Polytechniou 9, 15772 Zografos Greece

autor

Papadopoulos Christos I.

• National Technical University of Athens Iroon Polytechniou 9, 15772 Zografos Greece

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).