Uncertainty of sound power measurements of a reference sound source using the AHRI Standard 230 sound intensity method

2021 ◽  
Vol 263 (6) ◽  
pp. 641-647
Author(s):  
Curtis Eichelberger ◽  
Paul Bauch
Keyword(s):  
Training Program ◽  
Sound Source ◽  
Measurement Method ◽  
Sound Intensity ◽  
Intensity Measurement ◽  
Sound Power ◽  
Sound Sources ◽  
Scanning Method ◽  
Intensity Measurements ◽  
Power Measurements

The uncertainty of determining the sound power of HVAC equipment using the AHRI Standard 230 sound intensity measurement method is presented. Measurements of six different reference sound sources (RSS) at four different laboratories, by nineteen different individuals with four different instrumentation systems are presented. From 2004 through 2020, these measurements were performed as part of a training program at Johnson Controls HVAC test laboratories to qualify technicians and engineers on the use of sound intensity instrumentation. The results illustrate the reproducibility of sound intensity measurements using the scanning method of AHRI Standard 230.

scholarly journals Automated Sound Intensity Measuremant With Robot And Intensity Probe

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Nikola Knežević ◽  
Miloš Bjelić ◽  
Kosta Jovanović
Keyword(s):  
Measurement Accuracy ◽  
Sound Intensity ◽  
High Accuracy ◽  
Intensity Measurement ◽  
Measurement Process ◽  
Sound Power ◽  
Robot Arm ◽  
Sound Sources ◽  
Complex Sound ◽  
Automatic Positioning

This paper presents the procedure for measuring the sound intensity. The procedure was realized in such a way that therobotic arm carries the intensity probe and performs automatic positioning at the appropriate measurement points. In order todetermine the deviation value, a comparison was performed of sound intensity measurement obtained using robot and the measurementmanually performed by two people. Also, the measurement process was repeated for both the automated and manual positioning of theprobe, which resulted in deviation values for these two types of measurements. The differences in the total sound power of the sourceobtained by automated and manual measurements of the sound intensity were also analyzed. Using a robot arm significantly facilitatesthe measurement process and achieves higher measurement accuracy. Such use of robots can be of interest in measuring the intensity ofsound of complex sound sources in a large number of points, where high accuracy of intensity measurement is required.

scholarly journals Research on the Peripheral Sound Visualization Using the Improved Ripple Mode

2011 ◽  
Vol 2-3 ◽  
pp. 123-126
Author(s):  
Bin Xu ◽  
Dan Yang ◽  
Yun Yi Zhang ◽  
Xu Wang
Keyword(s):  
Neural Network ◽  
Power Spectrum ◽  
Real Time ◽  
Sound Source ◽  
Sound Intensity ◽  
Visualization Method ◽  
Audio Signals ◽  
Sound Sources ◽  
The Real ◽  
Spectrum Function

In this paper, we proposed a peripheral sound visualization method based on improved ripple mode for the deaf. In proposed mode, we designed the processes of transforming sound intensity and exterminating the locations of sound sources. We used power spectrum function to determine the sound intensity. ARTI neural network was subtly applied to identify which kind of the real-time input sound signals and to display the locations of the sound sources. We present the software that aids the development of peripheral displays and four sample peripheral displays are used to demonstrate our toolkit’s capabilities. The results show that the proposed ripple mode correctly showed the information of combination of the sound intensity and location of the sound source and ART1 neural network made accurate identifications for input audio signals. Moreover, we found that participants in the research were more likely to achieve more information of locations of sound sources.

scholarly journals Separation of the Sound Power Spectrum of Multiple Sources by Three-Dimensional Sound Intensity Decomposition

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 279
Author(s):  
Shiyi Chai ◽  
Xiaoqin Liu ◽  
Xing Wu ◽  
Yanjiao Xiong
Keyword(s):  
Power Spectrum ◽  
Sound Source ◽  
Three Dimensional ◽  
Sound Intensity ◽  
Point Sources ◽  
Mechanical Equipment ◽  
Sound Power ◽  
Test Bed ◽  
Multiple Sources ◽  
Noise Sources

The identification and separation of sources are the prerequisite of industrial noise control. Industrial machinery usually contains multiple noise sources sharing same-frequency components. There are usually multiple noise sources in mechanical equipment, and there are few effective methods available to separate the spectrum intensity of each sound source. This study tries to solve the problem by the radiation relationship between three-dimensional sound intensity vectors and the power of the sources. When the positions of the probe and the sound source are determined, the sound power of the sound source at each frequency can be solved by the particle swarm optimization algorithm. The solution results at each frequency are combined to obtain the sound power spectrum of each sound source. The proposed method is first verified by a simulation on two point sources. The experiment is carried out on a fault simulation test bed in an ordinary laboratory; we used three three-dimensional sound intensity probes to form a line array and conducted spectrum separation of the nine main noise sources. The sound intensity on the main frequency band of each sound source was close to the result of the near-field measurement of the one-dimensional sound intensity probe. The proposed spectral separation method of the sound power of multiple sound sources provides a new method for accurate noise identification in industrial environments.

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