Electro-Mechanical devices such as Wave Energy Converters (WECs) are potentially an important technology for the generation of renewable ocean energy. These underwater Acoustic Emitting (AE) machines are liable to experience certain failure modes such as damaged bearings, hydraulic faults, electrical arcs, vibration, etc. during their operation. This can lead to catastrophic failure of components of an overall ocean based system if necessary preventative measures are not taken. It is important therefore to create a robust condition monitoring process of rapid detection and classification of faults in these machines, to reduce costs and increase lifetime and reliability. This will assist companies involved in the deployment of these systems, particularly companies involved in the renewable ocean energy and marine technology sectors, to improve on the operational and management costs of ocean based systems, specifically impacting on efficiency and yield, reliability, and maintenance costs. Underwater sound around AE machines and vessels is often monitored for environmental impact assessment studies, and could be used to monitor the health of AE machines and vessels. This thesis illustrates the acquisition and visualization of acoustic signals produced by underwater AE machinery/vessels. It illustrates the technique involved in the deployment of an acquisition system and the subsequent conditioning, processing and presentation of the acquired underwater acoustic data. The work presented in this thesis also involves numerical modelling and simulation of low frequency component sound signals of underwater AE machines and vessels. It illustrates the effect of the interaction of sound signals with varied surfaces and boundaries, and their influences on the propagation of these underwater sound signals. A brief study of the effect of acoustic signals on marine fauna is also discussed in this thesis.