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.

Active noise control (ANC) aims to reduce a noise source at a listening point by destructive interference with a reverse phase noise emitted by one or more control devices. ANC systems cover a wide range of applications, such as industrial plants, vehicle cabins, headphones. Usually, in these applications the control devices are loudspeakers, and the error signals come from microphones. In outdoor applications, this solution comes with some limitations, such as the ageing of the loudspeakers due to humidity, temperature, rain and dust, and the presence of wired connections between the error sensors and the control system. This paper presents a noise reduction solution for a cogeneration plant, consisting of an Active Vibration Control (AVC) system applied to the cogenerator enclosure. To overcome the above limitations, electrodynamic shakers are used as controllers and an accelerometer as error signal. The control algorithm is a single reference, Single Input, Multiple Output (SIMO) Filtered-X Normalized Least Mean Square (FxNLMS), implemented in a Virtual Studio Technology (VST) plugin. VST plugins work in real-time in Digital Audio Workstation (DAW) software installed on personal computers, therefore without the need for low-level implementation on dedicated electronic boards, thus reducing the development time and the cost of the equipment. First, the effectiveness of the solution is demonstrated in a laboratory experiment. Subsequently, the results obtained on the cogenerator are presented, which show remarkable performance, with a noise reduction up to -17.5 dB on the main disturbing frequencies.

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.