No 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.

A predictive analytics methodology is presented, utilizing machine learning algorithms to identify the performance state of marine journal bearings in terms of maximum pressure, minimum film thickness, Sommerfeld number, load and shaft speed. A dataset of different bearing operation states has been generated by solving numerically the Reynolds equation in the hydrodynamic lubrication regime, for steady-state loading conditions and assuming isothermal and isoviscous lubricant flow. The shaft has been modelled with four different values of misalignment angle, lying within the acceptable operating range, as defined in the existing regulatory framework. The journal bearing was modelled parametrically using generic geometric parameters of a marine stern tube bearing. The lift-off speed was estimated for each loading scenario to ensure operation in the hydrodynamic lubrication regime and the effect of shaft misalignment on lift-off speed has been evaluated. The generated dataset was utilised for training, testing and validation of several machine learning algorithms, as well as feature selection analysis, in order to solve several classification problems and identify the various bearing operational states.

No abstract

A study of the shafting system of an 82.000DWT bulk carrier is conducted, comparing the initial alignment of the vessel against the performance corresponding to different bearing offset combinations. The shaft is modelled as a beam and the bearings as single support points. Radial shaft loads, thermal expansion of the engine and propeller thrust eccentricity are taken into account. Simulations are performed for 4 propeller immersion conditions and for various new offset combinations, deviating up to 10mm from the initial offsets. The results are evaluated according to rule requirements and classified as; (i) acceptable, (ii) marginal and (iii) not acceptable.