Machine learning performance comparison for main propulsive shafting systems alignment

The ship shaft alignment is crucial to achieve a high-performance propulsion system. This alignment is carried out, still in drydock, by adjusting the bearings' offsets. However, there are numerous available combinations sets of vertical offsets which results in good alignment final condition. Defining an optimum set of vertical displacements is costing shipyards a valuable time. Therefore, this work proposes the use of machine learning algorithms to predict if a given set of offsets, following the mandatory rules in force, should be implemented or not. In this manner, this paper presents a case study where a finite element model of an Anchor Handling Tug Supply (AHTS) vessel’ shaft line was assembled in order to obtain the bearings reactions, shaft line deflections, shear forces, bending moments and, finally, the influence coefficients matrix (ICM). Those data were used to feed machine learning (ML) models based on four different algorithms, namely, K-Nearest-Neighbors, Extremely Randomized Trees, Random Forest and Gradient Boosting in order to solve multiclass classification problems concerning the offset's set to be applied in the alignment of the shaft line. Results showed that the Gradient Boosting and Random Forest algorithms presented the best results, achieving up to 98.1% of precision in one of the 36 scenarios tested, and an average precision above 96.0%. Such results corroborated with the authors' proposal to implement ML techniques as a way to speed up the currently very time-consuming shaft alignment process. •Machine learning algorithms applied in ships shafting system alignment.•Techniques to predict if the bearings' offsets return an aligned shaft system.•Machine learning classification models applied in shaft alignment.•Influence coefficients matrix verifying the shaft system alignment.•Stiffness method calculating bearings reactions.