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Dinamik Ağırlık Ölçümü ve Dalgacık Dönüşümü Uygulaması

Year 2016, Volume: 28 Issue: 1, 7 - 12, 06.05.2016

Abstract

Günümüzde ürünlerin hızlı ve doğru bir şekilde tartılması üretim sektöründe önemli bir hale gelmiştir. Bundan dolayı ürünlerin hareket halinde iken tartımları yapılarak birim zamanda daha fazla ürünün ölçümünü gerçekleştiren sistemler geliştirilmiştir. Tartım sistemlerinde kullanılan yük hücrelerinin çıkış sinyalinin her zaman salınımlı bir tepkisi vardır ve sinyalin gerçek değerine oturması için belirli bir zamana ihtiyaç duyulmaktadır. Hareket halindeki ürünlerin ölçüm sistemlerinde sistemin ve ürünün hareket halinde olmasından dolayı meydana gelen titreşimlerden kaynaklı yük hücresinin çıkış sinyali oldukça gürültülü olmaktadır.  Bu çalışmada bu gürültüleri elimine etmek ve ürünlerin hareket halinde iken istenilen tartım hızlarına ulaşması için dalgacık dönüşümü kullanılarak analiz yapılmıştır.

References

  • Niedźwiecki, M., Wasilewski, A. (1996). Application of adaptive filtering to dynamic weighing of vehicles. Control Engineering Practice, 4(5), 635-644.
  • Yamazaki, T., Sakurai, Y., Ohnishi, H., Kobayashi, M., Kurosu, S. (2002). Continuous mass measurement in checkweighers and conveyor belt scales. Proceedings of the 41st SICE Annual Conference, Osaka, 5-7 Aug.
  • Boschetti, G., Caracciolo, R., Richiedei, D., Trevisani, A. (2013). Model-based dynamic compensation of load cell response in weighing machines affected by environmental vibrations. Mechanical Systems and Signal Processing, 34(1- 2), 116-130.
  • Pietrzak, P., Meller, M., Niedźwiecki, M. (2014). Dynamic mass measurement in checkweighers using a discrete time- variant low-pass filter. Mechanical Systems and Signal Processing, 48(1-2), 67-76.
  • Yamazaki, T., Ono, T. (2007). Dynamic problems in measurement of mass-related quantities. SICE, 2007 Annual Conference, Takamatsu, 17-20 Sept.
  • Bahar, H.B., Horrocks, D.H. (1998). Dynamic weight estimation using an artificial neural network. Artificial Intelligence in Engineering, 12(1-2), 135-139.
  • Halimic, M., Balachandran, W., Enab, Y. (1996). Fuzzy logic estimator for dynamic weighing system. Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on , New Orleans, LA, 8-11 Sep.
  • Jian, X., Bin, M. (2010) . Investigation of discrete wavelet transform for signal de-noising in weight-in-motion system. Future Computer and Communication (ICFCC), 2010 2nd International Conference on, Wuhan, 21-24 May.
  • Xiao, J., Lv, P. (2009). Application of wavelet transform in weigh-in-motion. Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on, Wuhan, 23-24 May.
  • Bin, M., Xinguo, Z. (2010). Discrete wavelet transform for signal processing in weight-in-motion system. Electrical and Control Engineering (ICECE), 2010 International Conference on, Wuhan, 25-27 June.
  • Jacob, B., Beaumelle, V.F.L. (2010). Improving truck safety: potential of weigh-in-motion technology. IATSS Research, 34(1), 9-15.
  • Liljencrantz, A., Karoumi, R., Olofsson, P. (2007). Implementing bridge weigh-in-motion for railway traffic. Computers & Structures, 85(12), 80-88.
  • Misiti M., Misiti Y., Oppenheim G., Poggi J.M. (2002) Wavelet toolbox for use with MATLAB, User’s Guide Version 2, The MathWorks.
  • Li, S., Wen, J. (2014). A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy and Buildings, 68, 63-71.
  • Ocak, H. (2009). Automatic detection of epilepti seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Application, 36(2), 2027-2036.
  • Aggarwal, R., Singh, J.K., Gupta, V.K., Rathore, S., Tiwari, M., Khare, A. (2011). Noise reduction of speech signal using wavelet transform with modified universal threshold. International Journal of Computer Applicaitons, 20(5), 14- 19.
  • Avci, E. (2008). Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Applied Soft Computing, 8(1), 225-231.
  • Giaouris, D., Finch, J.W. (2008). Denoising using wavelets on electric drive applications. Electric Power Systems Research, 78(4), 559-565.
  • Wu, J.D., Liu, C.H. (2008). Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Systems with Applicaitons, 35(3), 1200- 1213.
  • Panigrahi, B.K., Sinha, S.K., Mohapatra, A., Dash, P., Mallick, M.K. (2011). A comparative study of signal processing and pattern recognition approach for power quality disturbance classification. IETE Journal of Research, 57(1), 5-11.
  • Goudarzi, M., Vahidi, B., Naghizadeh, R.A., Hosseinian, S.H. (2015). Improved fault location algorithm for radial distribution systems with discrete and continuous wavelet analysis. International Journal of Electrical Power & Energy Systems, 67, 423-430.
  • Martis, R.J., Acharya, U.R., Min, L.C. (2013). ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 8(5), 437-448.
  • Bakar, A.H.A., Ali, M.S., Tan, C.K., Mokhlis, H., Arof, H., Illias, H.A. (2014). High impedance fault location in 11kV underground distribution systems using wavelet transforms. International Journal of Electrical Power & Energy Systems, 55, 723-730.
  • El-Zonkoly, A.M., Desouki, H. (2011). Wavelet entropy based algorithm for fault detection and classification in FACTS compensated transmission line. International Journal of Electrical Power & Energy Systems, 33(8), 1368–1374.
Year 2016, Volume: 28 Issue: 1, 7 - 12, 06.05.2016

Abstract

References

  • Niedźwiecki, M., Wasilewski, A. (1996). Application of adaptive filtering to dynamic weighing of vehicles. Control Engineering Practice, 4(5), 635-644.
  • Yamazaki, T., Sakurai, Y., Ohnishi, H., Kobayashi, M., Kurosu, S. (2002). Continuous mass measurement in checkweighers and conveyor belt scales. Proceedings of the 41st SICE Annual Conference, Osaka, 5-7 Aug.
  • Boschetti, G., Caracciolo, R., Richiedei, D., Trevisani, A. (2013). Model-based dynamic compensation of load cell response in weighing machines affected by environmental vibrations. Mechanical Systems and Signal Processing, 34(1- 2), 116-130.
  • Pietrzak, P., Meller, M., Niedźwiecki, M. (2014). Dynamic mass measurement in checkweighers using a discrete time- variant low-pass filter. Mechanical Systems and Signal Processing, 48(1-2), 67-76.
  • Yamazaki, T., Ono, T. (2007). Dynamic problems in measurement of mass-related quantities. SICE, 2007 Annual Conference, Takamatsu, 17-20 Sept.
  • Bahar, H.B., Horrocks, D.H. (1998). Dynamic weight estimation using an artificial neural network. Artificial Intelligence in Engineering, 12(1-2), 135-139.
  • Halimic, M., Balachandran, W., Enab, Y. (1996). Fuzzy logic estimator for dynamic weighing system. Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on , New Orleans, LA, 8-11 Sep.
  • Jian, X., Bin, M. (2010) . Investigation of discrete wavelet transform for signal de-noising in weight-in-motion system. Future Computer and Communication (ICFCC), 2010 2nd International Conference on, Wuhan, 21-24 May.
  • Xiao, J., Lv, P. (2009). Application of wavelet transform in weigh-in-motion. Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on, Wuhan, 23-24 May.
  • Bin, M., Xinguo, Z. (2010). Discrete wavelet transform for signal processing in weight-in-motion system. Electrical and Control Engineering (ICECE), 2010 International Conference on, Wuhan, 25-27 June.
  • Jacob, B., Beaumelle, V.F.L. (2010). Improving truck safety: potential of weigh-in-motion technology. IATSS Research, 34(1), 9-15.
  • Liljencrantz, A., Karoumi, R., Olofsson, P. (2007). Implementing bridge weigh-in-motion for railway traffic. Computers & Structures, 85(12), 80-88.
  • Misiti M., Misiti Y., Oppenheim G., Poggi J.M. (2002) Wavelet toolbox for use with MATLAB, User’s Guide Version 2, The MathWorks.
  • Li, S., Wen, J. (2014). A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy and Buildings, 68, 63-71.
  • Ocak, H. (2009). Automatic detection of epilepti seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Application, 36(2), 2027-2036.
  • Aggarwal, R., Singh, J.K., Gupta, V.K., Rathore, S., Tiwari, M., Khare, A. (2011). Noise reduction of speech signal using wavelet transform with modified universal threshold. International Journal of Computer Applicaitons, 20(5), 14- 19.
  • Avci, E. (2008). Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Applied Soft Computing, 8(1), 225-231.
  • Giaouris, D., Finch, J.W. (2008). Denoising using wavelets on electric drive applications. Electric Power Systems Research, 78(4), 559-565.
  • Wu, J.D., Liu, C.H. (2008). Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Systems with Applicaitons, 35(3), 1200- 1213.
  • Panigrahi, B.K., Sinha, S.K., Mohapatra, A., Dash, P., Mallick, M.K. (2011). A comparative study of signal processing and pattern recognition approach for power quality disturbance classification. IETE Journal of Research, 57(1), 5-11.
  • Goudarzi, M., Vahidi, B., Naghizadeh, R.A., Hosseinian, S.H. (2015). Improved fault location algorithm for radial distribution systems with discrete and continuous wavelet analysis. International Journal of Electrical Power & Energy Systems, 67, 423-430.
  • Martis, R.J., Acharya, U.R., Min, L.C. (2013). ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomedical Signal Processing and Control, 8(5), 437-448.
  • Bakar, A.H.A., Ali, M.S., Tan, C.K., Mokhlis, H., Arof, H., Illias, H.A. (2014). High impedance fault location in 11kV underground distribution systems using wavelet transforms. International Journal of Electrical Power & Energy Systems, 55, 723-730.
  • El-Zonkoly, A.M., Desouki, H. (2011). Wavelet entropy based algorithm for fault detection and classification in FACTS compensated transmission line. International Journal of Electrical Power & Energy Systems, 33(8), 1368–1374.
There are 24 citations in total.

Details

Primary Language Turkish
Journal Section Research Articles
Authors

İsmail Yabanova

Mehmet Yumurtacı

Publication Date May 6, 2016
Published in Issue Year 2016 Volume: 28 Issue: 1

Cite

APA Yabanova, İ., & Yumurtacı, M. (2016). Dinamik Ağırlık Ölçümü ve Dalgacık Dönüşümü Uygulaması. Marmara Fen Bilimleri Dergisi, 28(1), 7-12. https://doi.org/10.7240/mufbed.49446
AMA Yabanova İ, Yumurtacı M. Dinamik Ağırlık Ölçümü ve Dalgacık Dönüşümü Uygulaması. MAJPAS. May 2016;28(1):7-12. doi:10.7240/mufbed.49446
Chicago Yabanova, İsmail, and Mehmet Yumurtacı. “Dinamik Ağırlık Ölçümü Ve Dalgacık Dönüşümü Uygulaması”. Marmara Fen Bilimleri Dergisi 28, no. 1 (May 2016): 7-12. https://doi.org/10.7240/mufbed.49446.
EndNote Yabanova İ, Yumurtacı M (May 1, 2016) Dinamik Ağırlık Ölçümü ve Dalgacık Dönüşümü Uygulaması. Marmara Fen Bilimleri Dergisi 28 1 7–12.
IEEE İ. Yabanova and M. Yumurtacı, “Dinamik Ağırlık Ölçümü ve Dalgacık Dönüşümü Uygulaması”, MAJPAS, vol. 28, no. 1, pp. 7–12, 2016, doi: 10.7240/mufbed.49446.
ISNAD Yabanova, İsmail - Yumurtacı, Mehmet. “Dinamik Ağırlık Ölçümü Ve Dalgacık Dönüşümü Uygulaması”. Marmara Fen Bilimleri Dergisi 28/1 (May 2016), 7-12. https://doi.org/10.7240/mufbed.49446.
JAMA Yabanova İ, Yumurtacı M. Dinamik Ağırlık Ölçümü ve Dalgacık Dönüşümü Uygulaması. MAJPAS. 2016;28:7–12.
MLA Yabanova, İsmail and Mehmet Yumurtacı. “Dinamik Ağırlık Ölçümü Ve Dalgacık Dönüşümü Uygulaması”. Marmara Fen Bilimleri Dergisi, vol. 28, no. 1, 2016, pp. 7-12, doi:10.7240/mufbed.49446.
Vancouver Yabanova İ, Yumurtacı M. Dinamik Ağırlık Ölçümü ve Dalgacık Dönüşümü Uygulaması. MAJPAS. 2016;28(1):7-12.

Marmara Journal of Pure and Applied Sciences

e-ISSN : 2146-5150