Archives of Acoustics, 44, 1, pp. 89–98, 2019
10.24425/aoa.2019.126355

Fractal Analysis of Noise Signals of Sampo and John Deere Combine Harvesters in Operational Conditions

Farzad Mahdiyeh BOROUJENI
Shahrekord Uinversity
Iran, Islamic Republic of

Ali MALEKI
Shahrekord University
Iran, Islamic Republic of

Combine harvesters are the source a large amount of noise in agriculture. Depending on different working conditions, the noise of such machines can have a significant effect on the hearing condition of drivers. Therefore, it is highly important to study the noise signals caused by these machines and find solutions for reducing the produced noise. The present study was carried out is order to obtain the fractal dimension (FD) of the noise signals in Sampo and John Deere combine harvesters in different operational conditions. The noise signals of the combines were recorded with different engine speeds, operational conditions, gear states, and locations. Four methods of direct estimations of the FD of the waveform in the time domain with three sliding windows with lengths of 50, 100, and 200 ms were employed. The results showed that the Fractal Dimension / Sound Pressure Level [dB] in John Deere and Sampo combines varied in the ranges of 1.44/96.8 to 1.57/103.2 and 1.23/92.3 to 1.51/104.1, respectively. The cabins of Sampo and John Deere combines reduced and enhanced these amounts, respectively. With an increase in the length of the sliding windows and the engine speed of the combines, the amount of FD increased. In other words, the size of the suitable window depends on the extraction method of calculating the FD. The results also showed that the type of the gearbox used in the combines could have a tangible effect on the trend of changes in the FD.
Keywords: sound; combine; fractal dimension; engine speed; sliding window
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References

Aybek A., Kamer H.A., Arslan S. (2010), Personal noise exposures of operators of agricultural tractors, Applied Ergonomics, 41, 274–281.

Backes A.R., Bruno O.M. (2010), Shape classification using complex network and multi-scale fractal dimension, Pattern Recognition Letters, 31, 44–51.

Bilski B. (2013), Exposure to audible and infrasonic noise by modern agricultural tractors operators, Applied Ergonomics, 44, 2, 210–214.

Bohez E.L.J., Senevirathne T.R. (2001), Speech recognition using fractals, Pattern Recognition, 34, 2227–2243.

Bruno O.M., Plotze R.O., Falvo M., Castro M. (2008), Fractal dimension applied to plant identification, Information Sciences, 178, 2722–2733.

Dewangan K., Kumar G., Tewari V. (2005), Noise characteristics of tractors and health effect on farmers, Applied Acoustics, 66, 1049–1062.

Ehlers J.J., Graydon P.S. (2011), Noise-induced hearing loss in agriculture: Creating partnerships to overcome barriers and educate the community on prevention, Noise Health, 13, 51, 142–146.

Esteller R., Vachtsevanos G., Echauz J., Litt B. (2001), A comparison of waveform fractal dimension algorithms, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 48, 2, 177–183.

Florindo J.B., Bruno O.M. (2011), Closed contour fractal dimension estimation by the Fourier transform, Chaos, Solitons and Fractals, 44, 851–861.

Gnitecki J., Moussavi Z. (2005), The fractality of lung sounds: A comparison of three waveform fractal dimension algorithms, Chaos, Solitons, and Fractals, 26, 1065–1072.

Gomez ‍C., Mediavilla A., Hornero ‍R., Abasolo D., Fernandez A. (2009), Use of the Higuchi’s fractal dimension for the analysis of MEG recordings from Alzheimer’s disease patients, Medical Engineering and Physics, 31, 306–313.

Grift T.E., Novais J., Bohn M. (2011), High-throughput phenotyping technology for maize roots, Biosystems Engineering, 110, 40–48.

Higuchi T. (1988), Approach to an irregular time series on the basis of the fractal theory, Physica D: Nonlinear Phenomena, 31, 277–283.

ISO 5131 (1996), Acoustics: Tractors and machinery for agriculture and forestry measurement of noise at operator’s position.

ISO 7216 (1992), Acoustics: Agricultural and forestry wheeled tractors and self-propelled machines, Measurement of noise emitted when in motion.

Katz M.J. (1988), Fractals and the analysis of waveforms, Computers in Biology and Medicine, 18, 3, 145–156.

Klonowski W., Olejarczyk E., Stepien R. (2005), Sleep-EEG analysis using Higuchi’s fractal dimension, International Symposium on Nonlinear Theory and its Applications, 18–21 October, Bruges, Belgium.

Maleki A., Lashgari M. (2014), Analysis of combine harvester sound pressure level in one-third octave band frequency, Journal of Agricultural Machinery, 4, 2, 154–165.

McBride D.I., Firth H.M., Herbison G.P. (2003), Noise exposure and hearing loss in agriculture: A survey of farmers and farm workers in the Southland region of New Zealand, Journal of Occupational and Environmental Medicine, 45, 12, 1281–1288.

Raghavendra B.S., Narayana Dutt D. (2010), Computing fractal dimension of signals using multiresolution box-counting method, World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic, and Communication Engineering, 4, 1, 183–198.

Rangayyan R.M., Oloumi F., Wu Y., Cai S. (2013), Fractal analysis of knee-joint vibrio orthographic signals via power spectral analysis, Biomedical Signal Processing and Control, 8, 23–29.

Sabanal S., Nakagawa M. (1996), The fractal properties of vocal sounds and their application in the speech recognition model, Chaos, Solitons and Fractals, 7, 11, 1825–1843.

Sevcik C. (2006), On fractal dimension of waveforms, Chaos, Solitons, and Fractals, 28, 579–580.

Xie H., Zhou H.W. (2008), Application of fractal theory to top-coal caving, Chaos, Solitons and Fractals, 36, 797–807.

Xie H.P., Liu J.F., Ju Y., Li J., Xie L.Z. (2011), Fractal property of spatial distribution of acoustic emissions during the failure process of bedded rock salt, International Journal of Rock Mechanics and Mining Science, 48, 1344–1351.




DOI: 10.24425/aoa.2019.126355