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.

Identification of Grader Radiated Noise Source Based on Partial Coherence Analysis

2012 ◽  
Vol 610-613 ◽  
pp. 2562-2565 ◽  
Author(s):  
Yan Bo Geng ◽  
Zi Yong Han ◽  
Zhe Lei Wei ◽  
Dao Liang Zhou ◽  
Zhan Wen Zhang
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Partial Coherence ◽  
Coherence Analysis ◽  
Computational Formula ◽  
Noise Sources ◽  
Radiated Noise ◽  
Output Noise ◽  
Noise Source Identification ◽  
Identification Model

Partial coherence analysis can eliminate competing influences of different noise sources and identify the main noise source when all noise sources of system are relevant. In the paper, the method of partial coherence analysis is used for identifying noise source of grader. A multi-input and multi-output noise source identification model is established. And then the computational formula is developed. Finally, an experiment is carried out with an actual grader. In the experiment the main noise sources are accurately identified.

scholarly journals A Fuzzy-Based Analytic Hierarchy Process for Mechanical Noise Source Identification of a Diesel Engine

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Junhong Zhang ◽  
Qidi Zhou ◽  
Jiewei Lin ◽  
Weidong Li ◽  
Zhoujie Tang ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Analytic Hierarchy Process ◽  
Test Point ◽  
Partial Coherence ◽  
Component Level ◽  
Noise Sources ◽  
Analytic Hierarchy ◽  
Frequency Bands ◽  
Left Block ◽  
Hierarchy Process

In this paper, a novel fuzzy-based analytic hierarchy process (FAHP) is proposed for identifying noise sources of diesel engine and sorting their contributions. The hierarchy tree consists of 5 levels including the component level, the frequency band level, the test point level, the operating condition level, and the overall noise level. The variational mode decomposition (VMD) is employed to decompose the overall noise into several frequency bands. The factor weights between levels are determined by the partial coherence analysis (PCA) to consider the correlation of component vibration and radiated noise in different frequency bands. A fuzzy consistency matrix is then constructed to order the corresponding factors level by level that can avoid the consistence problem of the traditional AHP. Based on the rig test of a diesel engine, the proposed approach is implemented to weight the noise sources identified. It is shown the overall weighting order of the six major noise sources is (in descending order) the oil pan, the left block, the valve cover, the flywheel housing, and the right block.

Identification of Soil Stabilizer Noise Source with Partial Coherence Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 1785-1788
Author(s):  
Yan Bo Geng ◽  
Lei Lei Gao ◽  
Zhan Wen Zhang ◽  
Zhe Lei Wei
Keyword(s):  
Noise Source ◽  
Coherence Function ◽  
Partial Coherence ◽  
Coherence Analysis ◽  
Noise Sources ◽  
Single Output ◽  
Soil Stabilizer ◽  
Output Noise ◽  
Noise Source Identification ◽  
Identification Model

Since soil stabilizer consists of many operating components, it has lots of noise sources, and the major noise sources are relevant. So, it is appropriate to identify the noise source of the soil stabilizer using the partial coherence analysis. In this paper, a multi-input and single-output noise source identification model is firstly established, and then an algorithm is developed for the calculation of the partial coherence function. Finally, an experiment is carried out with an actual soil stabilizer. In the experiment the main noise sources are accurately identified. This work provides some guidance for further study of vibration and noise reduction of the soil stabilizer.

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.

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.

scholarly journals Ontology-Based Noise Source Identification and Key Feature Selection: A Case Study on Tractor Cab

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Su Han ◽  
Yiqi Zhou ◽  
Yanzhao Chen ◽  
Chenglong Wei ◽  
Rui Li ◽  
...  
Keyword(s):  
Source Identification ◽  
Noise Source ◽  
Noise Sources ◽  
Signal Characteristics ◽  
Knowledge Expression ◽  
Noise Source Identification ◽  
Expression Model ◽  
Noise Vibration ◽  
Agricultural Machines

This paper proposes an ontology-based noise source identification method, establishes an ontology knowledge expression model in the field of noise, vibration, and harshness (NVH), and provides an extensible framework for sharing noise diagnosis knowledge in the field. Based on the key features extracted from the noise and vibration signals at different positions and the prior knowledge, mechanical engineers can construct an ontology rule and locate noise sources by identifying the intrinsic relationship between signal characteristics through ontology reasoning. A case study is conducted to demonstrate the effectiveness of the proposed method in resolving the problem of integrating multisource heterogeneous knowledge and exchanging noise diagnosis knowledge information in the field of NVH for agricultural machines. Thus, our study facilitates the sharing and reuse of knowledge and advances the development of intelligent noise diagnosis expert systems to a certain extent.

Noise Source Identification and Experimental Research of Engine Compartment of a Forklift Based on Fast Independent Component Analysis and Scan and Paint

2015 ◽  
Author(s):  
Enlai Zhang ◽  
Liang Hou ◽  
Weiping Yang
Keyword(s):  
Independent Component Analysis ◽  
Source Identification ◽  
Noise Source ◽  
Component Analysis ◽  
Independent Component ◽  
Engine Compartment ◽  
Noise Sources ◽  
Fast Independent Component Analysis ◽  
Noise Source Identification ◽  
Fastica Algorithm

The noise signals from the engine compartment of a forklift are wideband non-stationary random signals as their structure and working process are complex. In order to separate and identify the noise sources of the engine compartment, blind source identification analysis was carried out based on fast independent component analysis (FastICA) algorithm and experimental research on noise sources localization were done by using Microflown’s Scan & Paint system. Firstly, a numerical analysis method for effectively achieving noise source identification was proposed. Secondly, the feasibility of FastICA algorithm and the efficiency of the proposed method were verified through simulation. Thirdly, the statistical independence and Gaussian of noise signals were analyzed. The results show that noise signals meet the preconditions of independent component analysis (ICA). Then, noise signals were separated by the proposed method. The corresponding relation between independent components (ICs) and different noise sources was obtained. And the accuracy of the identification results was validated with Scan & Paint sound source localization system. The differences between experimental and numerical analysis results are less than 5%. Finally, de-noising methods are devised based on sound source characteristics.

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