A methodology to define underwater acoustic radiated noise norms for small commercial vessel classes using neural networks

Abstract

To address the International Maritime Organization's underwater radiated noise (URN) reduction guidelines (MEPC.1/Circ.833), vessels undertake rangings to evaluate their URN against classification society norms. However, norms for smaller vessels, like Canada's inshore fishing fleet, are not established. The proposed methodology informs URN class norms for small commercial vessels, while minimizing individual vessel rangings, through three steps. Firstly, representative class members are acoustically ranged over the range of operating conditions. URN is logged and correlated with on-board measures of structure-borne noise, ma- chinery states, hull fouling, and weather. Secondly, a neural network (NN) is trained to predict the URN from these logged measures. Third and finally, the network's sensitivity to logged measures is analyzed using per-mutation importance and dropout. Sensitive features demarcate the class, or must be logged for each vessel, towards accurate predictions. This methodology is demonstrated on the Cape Islanders class. The trained NN predicted the decidecade URN spectrum (100Hz - 50kHz) to an accuracy of 6.6dB re: 1microPascal at 1m. URN prediction sensitivity to in-water conditions, engine speed, engine power, cavitation-induced hull vibrations, and hull fouling extent indicates important features to log. This shows that class-wide analysis of URN can inform small vessel class norms using the proposed methodology.