Noise Source Identification of the Grain Combining Harvester Based on Acoustic Array Test

HIGHLIGHTSWe tested the noise of a grain combining harvester using a spiral acoustic array, aiming to identify its main sources and reduce its noise level.The noise of the harvester is mainly concentrated in the frequency range of 1 to 4 kHz.When the power of other devices is cut off, engine is the main noise source. While all devices are in normal working condition, the main source of noise is the header device and the intermediate conveying device.Abstract. The grain combine harvester is an important agricultural equipment with multiple functions of harvesting, threshing, separating, cleaning and grain gathering. As an instantaneous physical pollution, noise has become one of the main causes of modern civilization diseases. The noise generated by the operation of harvesters not only causes harm to the workers, but also leads to environmental noise pollution. Here, we tested the noise of a grain combine harvester using a spiral acoustic array, aiming to identify its main source by noise source identification technology based on the sound pressure distribution and reduce its noise level. The test results show that the noise of the harvester is mainly concentrated in the frequency range of 1 to 4 kHz. When the power of other devices is cut off, the engine is the main noise source, while under normal working conditions of all devices, the main source of noise is the header device and the intermediate conveying device on the front side of the harvester, the threshing device on the rear side, the engine and the threshing device on the left side, and the engine and the header device on the right side. Keywords: Acoustic array technology, Grain combining harvester, Noise source identification, Vibration and noise reduction.

Download Full-text

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

Volume 15: Sound, Vibration and Design ◽

10.1115/imece2009-12194 ◽

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.

Download Full-text

Apply Two Methods to Measure Engine Noise Source Identification

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.259 ◽

2014 ◽

Vol 536-537 ◽

pp. 259-263 ◽

Cited By ~ 1

Author(s):

Can Song Gu ◽

Jun Hong Dong ◽

Hui Gao

Keyword(s):

Frequency Spectrum ◽

Spectrum Analysis ◽

Source Identification ◽

Noise Source ◽

Sound Intensity ◽

Engine Noise ◽

Frequency Spectrum Analysis ◽

Noise Source Identification ◽

Acoustic Array

Based on sound intensity and acoustic array methods, noise source identification of an engine is measured at idle condition. The main noise source of intake surface and exhaust surface are determined, also their frequency spectrum analysis. This two kinds of method testing results fundamentally agreed very well, and verify acoustic array method is more simple and easier than sound intensity method.

Download Full-text