Industrial Noise Source Identification by Using an Acoustic Beamforming System

1998 ◽  
Vol 120 (2) ◽  
pp. 426-433 ◽  
Author(s):  
M. R. Bai ◽  
J. Lee
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Microphone Array ◽  
Field Tests ◽  
Minimum Variance ◽  
Industrial Applications ◽  
Noise Sources ◽  
Multiple Signal Classification ◽  
Noise Source Identification ◽  
Identification Technique

A noise source identification technique is proposed for industrial applications by using a microphone array and beamforming algorithms. Both of the directions and the distances of long-range noise sources are calculated. The conventional method, the minimum variance (MV) method, and the multiple signal classification (MUSIC) method are the main beamforming algorithms employed in this study. The results of numerical simulations and field tests indicate the effectiveness of the acoustic beam-former in identifying noise sources in industrial environments.

Identification of Noise Source Based on Partial Coherence Analysis

2012 ◽  
Vol 239-240 ◽  
pp. 482-486
Author(s):  
Hai Ping Wu ◽  
Jing Jun Lou ◽  
Wen Wu Liu
Keyword(s):  
Analytic Hierarchy Process ◽  
Source Identification ◽  
Noise Source ◽  
Partial Coherence ◽  
Coherence Analysis ◽  
Noise Sources ◽  
Analytic Hierarchy ◽  
Noise Source Identification ◽  
Source Excitation ◽  
Hierarchy Process

Noise source identification is the precondition and foundation of the noise reduction. There are some limitations while using some common method to analyze noise sources, so on the basis of partial coherence analysis and analytic hierarchy process, a method was proposed which can sort noise source contribution. When noise in the multi-source excitation system is coherent，this method can identify and sort noise source.

Noise assessment of taxibotted versus conventional taxiing operations using a phased microphone array

2021 ◽  
Vol 263 (5) ◽  
pp. 1152-1163
Author(s):  
Bieke von den Hoff ◽  
Mirjam Snellen ◽  
Dick G. Simons
Keyword(s):  
Noise Source ◽  
Microphone Array ◽  
Noise Levels ◽  
Noise Sources ◽  
Operational Testing ◽  
Electric Aircraft ◽  
Noise Source Identification ◽  
Noise Measurements ◽  
Noise Assessment ◽  
Lower Noise

In sustainable aviation the focus is mostly applied to the greenhouse gas emissions during flight. However airports have an increasing interest in reducing emissions during ground operations such as taxiing for example to improve the local air quality. Amsterdam Airport Schiphol started a pilot for sustainable taxiing with a pilot-controlled hybrid-electric aircraft towing vehicle called TaxiBot in 2020. The COVID-19 pandemic created an opportunity for extensive operational testing on a near-empty airport. Due to the low background noise levels in this situation, also a noise assessment of taxiing with the TaxiBot versus conventional two-engine taxiing was performed. This assessment can be used to evaluate the noise levels to which ground workers or neighbouring communities are exposed due to TaxiBot operations. For the noise measurements a phased microphone array was used, which allowed not only for a noise level and directionality assessment, but also for noise source identification. This paper compares the noise emissions and noise sources between a taxibotted and conventional taxiing operation. The results show that a taxibotted taxiing operation produces significantly lower noise levels. Additionally, acoustic imaging shows that the TaxiBot engine is the main noise source for a taxibotted pass-by manoeuvre.

Aircraft Noise Source Identification Using a Microphone Array: Montreal-Trudeau Airport Test Campaign

2013 ◽  
Author(s):  
Robby Lapointe ◽  
Alain Berry ◽  
Cédric Camier ◽  
Jean-Francois Blais ◽  
Mathieu Patenaude-Dufour ◽  
...  
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Microphone Array ◽  
Aircraft Noise ◽  
Noise Source Identification

An Experimental/Numerical Noise Source Identification Technique

1987 ◽  
Author(s):  
B. K. Gardner ◽  
R. J. Bernhard
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Numerical Noise ◽  
Noise Source Identification ◽  
Identification Technique

NOISE SOURCE IDENTIFICATION IN A REVERBERANT FIELD USING SPHERICAL BEAMFORMING

2008 ◽  
Vol 22 (11) ◽  
pp. 1147-1151 ◽  
Author(s):  
YOUNG-CHUL CHOI ◽  
JIN-HO PARK ◽  
DOO-BYUNG YOON ◽  
HYU-SANG KWON
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Free Field ◽  
Sound Field ◽  
Source Location ◽  
Noise Sources ◽  
Identification Process ◽  
Noise Source Identification ◽  
Conventional Methods ◽  
Reverberant Field

Identification of noise sources, their locations and strengths, has been taken great attention. The method that can identify noise sources normally assumes that noise sources are located at a free field. However, the sound in a reverberant field consists of that coming directly from the source plus sound reflected or scattered by the walls or objects in the field. In contrast to the exterior sound field, reflections are added to sound field. Therefore, the source location estimated by the conventional methods may give unacceptable error. In this paper, we explain the effects of reverberant field on interior source identification process and propose the method that can identify noise sources in the reverberant field.

A Comparison of Beamforming Processing Techniques for Low Frequency Noise Source Identification in Mining Equipment

2009 ◽  
Author(s):  
Hugo E. Camargo ◽  
Patricio A. Ravetta ◽  
Ricardo A. Burdisso ◽  
Adam K. Smith
Keyword(s):  
Source Identification ◽  
Hot Spots ◽  
Noise Source ◽  
Low Frequency ◽  
Adaptive Beamforming ◽  
Frequency Noise ◽  
Mining Equipment ◽  
Frequency Range ◽  
Noise Sources ◽  
Noise Source Identification

In an effort to reduce Noise Induced Hearing Loss (NIHL) in the mining industry, the National Institute for Occupational Safety and Health (NIOSH) is conducting research to develop noise controls for mining equipment whose operators exceed the Permissible Exposure Level (PEL). The process involves three steps: 1) Noise source identification (NSI), 2) development of noise controls, and 3) evaluation of the developed noise controls. For the first and third steps, microphone phased array measurements are typically conducted and data are processed using the conventional beamforming (CB) algorithm. However, due to the size and complexity of the machines, this task is not straight forward. Furthermore, because of the low frequency range of interest, i.e., 200 Hz to 1000 Hz, results obtained using CB may show poor resolution issues which result in inaccuracy in the noise source location. To overcome this resolution issue, two alternative approaches are explored in this paper, namely the CLEAN-SC algorithm and a variarion of an adaptive beamforming algorithm known as Robust Capon Beamformer (RCB). These algorithms were used along with the CB algorithm to process data collected from a horizontal Vibrating Screen (VS) machine used in coal preparation plants. Results with the array in the overhead position showed that despite the use of a large array, i.e., 3.5-meter diameter, the acoustic maps obtained using CB showed “hot spots” that covered various components, i.e., the screen deck, the side walls, the I-beam, the eccentric mechanisms, and the electric motor. Thus, it was not possible to identify which component was the dominant contributor to the sound radiated by the machine. The acoustic maps obtained using the RCB algorithm showed smaller “hot” spots that in general covered only one or two components. Nevertheless, the most dramatic reduction in “hot” spot size was obtained using the CLEAN-SC algorithm. This algorithm yielded acoustic maps with small and well localized “hot” spots that pinpointed dominant noise sources. However, because the CLEAN-SC algorithm yields small and localized “hot” spots, extra care needs to be used when aligning the acoustic maps with the actual pictures of the machine. In conclusion, use of the RCB and the CLEAN-SC algorithms in the low frequency range of interest helped pinpoint dominant noise sources which otherwise would be very hard to identify.

Noise-source identification of a high-speed train by noise source level analysis

2016 ◽  
Vol 231 (6) ◽  
pp. 717-728 ◽  
Author(s):  
Hee-Min Noh
Keyword(s):  
Source Identification ◽  
High Speed ◽  
Sound Pressure ◽  
Noise Source ◽  
Microphone Array ◽  
Beam Power ◽  
High Speed Train ◽  
Scaling Factors ◽  
Noise Source Identification ◽  
Level Analysis

In this study, noise-source identification of a high-speed train was conducted using a microphone array system. The actual sound pressure level analysis of the noise source was performed using scaling factors between the real sound pressure and the beam-power output based on the assumption that the integrated area of the main beam-power lobe is equal to half that of the actual sound pressure of the noise source. Then, the scaling factors for the 144-channel microphone array were derived from analysis of the array response function, and a verification experiment was conducted using a known noise source, an air horn, located on a high-speed train moving at 240 km/h. After the verification test, noise-source identification of the high-speed train was conducted. Based on the resulting noise map of the high-speed train moving at 390 km/h, the main noise sources were determined to be the inter-coach spacing, wheels, and pantograph. The noise generated by the pantograph was then investigated in more detail. It was concluded that the pan head of the pantograph was the main noise source at a frequency of 1000 Hz.

Development of a low cost microphone array system for vehicle noise source identification

2007 ◽  
Vol 122 (5) ◽  
pp. 2965
Author(s):  
Yuri Adson Ribeiro Silva ◽  
Willoium Fonseca ◽  
Samir N. Y. Gerges ◽  
Joel Mobley
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Microphone Array ◽  
Low Cost ◽  
Vehicle Noise ◽  
Noise Source Identification
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