Electric boats are thought to be noiseless, but in-situ measurements are generally rare. The Underwater Radiated Noise (URN) of 8-m Trimaran Pontoon Boat with two outboard electric engines was measured in the Miramare Marine Protected Area (Trieste, Italy), together with the URN of a fibreglass 5-m boat, with a outboard gasoline engine, for comparisons. International standards and guidelines for shallow waters were considered. URN were provided in one-third octave band and in narrow band spectra. The electric boat produced a low input of underwater noise at low frequencies. Given a low-frequency hearing sensitivity, the listening space reduction (LSR) was lower when generated by the electric than by combustion engine boat for the brown meagre, a local Teleost fish. No difference was found for the bottlenose dolphin LSR although continuous, tonal, high frequency components generated by the electric boat are expected to be highly detrimental for the bottlenose dolphin.

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications.

Monitoring the condition of rolling bearings is critical for industrial reliability, yet tradi-tional contact-based accelerometers can be impractical in confined or hazardous envi-ronments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault de-tection sensitivity across various rotational speeds and load conditions. Using an accel-erometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphones positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), Kurtosis and Crest-Factor values, with advanced diagnostic tech-niques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spec-trum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, Kurtosis, Crest Factor) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the max-imum sensor distance. The study concludes that optimal microphone placement is essen-tial for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications.