Very low-frequency IEPE accelerometer calibration and application to a wind energy structure

In this work, we present an experimental setup for very low-frequency calibration measurements of low-noise Integrated Electronics Piezo Electric (IEPE) accelerometers and a customised signal conditioner design for using IEPE sensor down to 0.05Hz. AC-response IEPE accelerometer and signal conditioners have amplitude and phase deviations at low frequencies. As the standard calibration procedure in the low-frequency range is technically challenging, IEPE accelerometers with standard signal conditioners are usually used in frequency ranges above 1 Hz. Vibrations on structures with low eigenfrequencies like wind turbines are thus often monitored using DC-coupled micro-electro-mechanical systems (MEMS) capacitive accelerometers. This sensor type suffers from higher noise levels compared to IEPE sensors. To apply IEPE sensors instead of MEMS sensors, in this work the calibration of the entire measurement chain of three different IEPE sensors with the customised signal conditioner is performed with a low-frequency centrifuge. The IEPE sensors are modelled using IIR filters to apply the calibration to time-domain measurement data of a wind turbine support structure. This procedure enables an amplitude and phase-accurate vibration analysis with IEPE sensors in the low-frequency range down to 0.05 Hz.

MEMS digital microphone and Arduino compatible microcontroller: an embedded system for noise monitoring

Noise assessment and monitoring are essential parts of an acoustician's work since it helps to understand the environment and propose better solutions for noise control and urban noise management. Traditionally, equipment to carry out this task is standardized, and, eventually, expensive for the early career professional. This work develops a high-quality (and cost-effective) prototype for an embedded noise monitoring device based upon a digital I2S MEMS microphone and an Arduino compatible microcontroller, named Teensy. Its small size and low power consumption are also advantages designed for the project. The system captures and processes sound in real-time, computes A and C frequency-weighted equivalent sound levels, along with time-weighted instant levels with a logging interval of 125 ms. Part of the software handles the audio environment, while the biquadratic IIR filters present in the Cortex Microcontroller library are responsible for the frequency- and time-weightings - using floating-point for enhanced precision. The prototype results were compared against a Class 2 Sound Level Meter, rendering very similar results for the tested situations, proving a powerful and reliable tool. Improvements and further testing are also being conducted to refine its functioning and characterization. Ultimately, the prototype achieved promising performance, confirming as a solution for noise monitoring.