Abstract
Abstract 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.
Highlights
There are several approaches used for condition monitoring and predictive maintenance of journal bearings, such as vibration, noise and acoustic emission monitoring and analyses, focusing on detecting and identifying patterns and trends in the recorded signals, and correlating them with present or upcoming fault conditions [1, 2]
In marine engineering applications, and in the study of line and stern tube bearings of the propulsion shafts, the most applicable method is that of oil temperature monitoring, due to the very compact designs, accessibility restrictions and limited advanced sensor equipment onboard modern vessels
The present work is concerned with the development of Machine Learning (ML) algorithms to predict the real-time steady-state performance indices of journal bearings over a wide range of bearing load and rotational speed values, utilising sound and vibration measurements
Summary
There are several approaches used for condition monitoring and predictive maintenance of journal bearings, such as vibration, noise and acoustic emission monitoring and analyses, focusing on detecting and identifying patterns and trends in the recorded signals, and correlating them with present or upcoming fault conditions [1, 2]. Ma and Zhang have investigated in [9] the excitation mechanisms and contributions of tribofilm‒asperity interaction that occur in the hydrodynamic lubrication regime of journal bearings They used the spatial power spectral density as a feature of the non-Gaussian roughness surfaces for early wear to analyse the microscopic pressure fluctuations, aiming to provide a new understanding for characterising noisy vibration signals for early wear monitoring of journal bearings. The present work is concerned with the development of ML algorithms to predict the real-time steady-state performance indices of journal bearings (load, minimum film thickness) over a wide range of bearing load and rotational speed values, utilising sound and vibration measurements. K-Nearest Neighbours k-Nearest Neighbours algorithms used for classification are simple and only require the storage of the training dataset They create a space with as many dimensions as the number of the dataset’s features, and do not build an internal model to aid with the prediction. As more trees (m) are added, the performance of the algorithm improves until the maximum number of trees is reached (M), or until the prediction accuracy is not improving any further after several iterations
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