Radiated Noise Assessment of Shipboard Systems Using Vibration Analysis

Since the emergence of acoustic warfare, especially in modern times, noise regulation of ships has become a concern, leading to ships being built and tested with rigorous and periodical measures to ensure their noise signatures are minimal. Simultaneously, other predictive maintenance and load monitoring systems are installed on ships for better resource management. This work came from an idea to merge both worlds and used the predictive maintenance system to predict the radiated noise due to vibrations from the ship’s systems. First, a scientific survey to study the field yielded two plausible theoretical models that could help predict vibro-acoustic transmissions in complex systems - Finite Element Analysis and Statistical Energy Analysis. Those methods were implemented on a simple metal cabinet with limited success—however, a frequency-gain model constructed using a set of planned experiments, performed with reasonable accuracy. Later on, the experimental-based model construction method was implemented on a test ship to predict its frequency-gain model for different shipboard systems. This method did not yield good accuracy; however, using different data analysis tools such as Recurrent Neural Networks helped improve prediction accuracy. Eventually, this work suggests future directions to follow, based on the experience gathered from the research.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Elatov, David

Advisor dc:contributor.advisor

• Leeb, Steven B.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/143219

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/143219