Low Costs Electrical Calibration System of SLM with the Uncertainty Measurements Compared with Primary System Platform Brūel & Kjær type 3630
Journal: International Journal of Emerging Trends in Engineering Research (IJETER) (Vol.12, No. 7)Publication Date: 2024-07-15
Authors : Mahmoud Younes El Aidy;
Page : 114-127
Keywords : Calibration system for SLM; frequency- and time-weightings; toneburst; range of linearity test; noise measurement; uncertainty of measurements.;
Abstract
Sound level meter (SLM) calibration according to IEC61672-1, 2, 3: 2013 is an essential step in the measurement process. It ensures that the SLM meets its specifications. There is a strong need for an efficient and cheap calibration system device for the electrical calibration of SLM since it avoids the body effect and angle sensitivity of the microphone membrane to the sound incidence angle for free field calibration. As well as, it avoids leakage errors due to the microphone fitting in the coupler and the coupler effect in pressure field calibration. This work gives an alternative system device used in the electrical calibration of SLM. This system consists of individual tools; PULSE signal generator, universal frequency counter, HP Dynamic signal analyzer, and DMM voltmeter. All system tools are complying with the IEC17025. The calibration procedures follow the second edition of IEC61672-2. Calibration measurements of frequency, time-weightings, and linearity of range parameters as the major features of SLMs are tested in the frequency range of 31.5 Hz to 16 k Hz. The calibration was repeated 5 times on different days and different warm-up times for devices to estimate the associated uncertainty measurement values in accordance with Part 2 and Part 3 of IEC 61672. The obtained results show that, the calibration measurement deviations are in the allowable tolerance of the IEC 61672-1 and they are comparable with that obtained from the primary Brüel & Kjær (B & K) platform 3630. The computed uncertainty of measurements is in accepted limits that are required by IEC 61672-1 which is determined to the level of confidence of 95%, using coverage factor 2.
Other Latest Articles
- The Transformative Role of Microsoft Azure AI in Healthcare
- An Effective Data Fusion Methodology for Multi-modal Emotion Recognition: A Survey
- A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare
- Parkinson’s Disease Prediction Using Machine Learning Models
- Disease Detection In Rice And Wheat Leaves: A Comparative Study On Various Deep Learning Techniques
Last modified: 2024-07-19 16:41:49