Araştırma Makalesi
BibTex RIS Kaynak Göster

Algorithm Developed for Preventing Time Loss Calculating RMS of Vibration Signals

Yıl 2018, , 248 - 252, 14.12.2018
https://doi.org/10.29048/makufebed.430883

Öz

In this work, an
algorithm is developed to prevent time losses that occur during the calculation
of Root Mean Square (RMS) of the vibration signals. Vibration signals are
obtained during cutting of super alloy material Inconel 718 on a CNC lathe
machine with a cutting speed of 50 meters per minute, 0.15 mm progress per
revolution and 2.5 mm cutting depth. The data is loaded to the computer via the
vibration sensor (353B31 from PCB Piezotronic) and the digital-analog converter
card (DAQ 6062E) and then mathematical expressions are created in Matlab
environment. RMS is one of the most important evaluation parameters in the
evaluation of this data. Running the classical algorithm, it is observed that
there was excessive time loss in the RMS calculation of total 120 million data about
60 tests, each test lasted about eight hours. So a new algorithm has been developed
that can do operations much faster. The results of the new algorithm are
compared with the results of the classic algorithm and it is determined that
the new algorithm produces 100% correct results. 99% of the time loss is
avoided with this new algorithm.

Kaynakça

  • Genç M.H., Çataltepe Z., Pearson T. (2007). A New PCA/ICA Based Feature Selection Method. IEEE 15th, Signal Processing and Communications Applications,. SIU
  • Gençer Ç. (2009). A Microcontroller Based Application of a True Rms Value Scale System. Journal of Polytechnic. Vol: 12 No: 2 pp.79-84
  • Germer, H. (2000). A New Method and a Device for High Precision True RMS Measurements Using the Monte Carlo Method. Precision Electromagnetic Measurements Digest. pp. 581-582
  • Germer, H. (2001). High-Precision AC Measuruments Using the Monte Carlo Method. IEEE Transactions on Instrumentation and Measurument. Vol 50, No 2, pp. 457-460
  • Guyon, I., Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research. Vol 3, pp. 1157-1182
  • Güngör O. (2011). Real Time Monitoring Cutting Tool Vibration, M.Sc.Thesis, Süleyman Demirel University Graduate School of Applied and Natural Sciences Electronic Computer Education Department, ISPARTA
  • Hall, M, A. (2000). Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of 17th International Conference on Machine Learning, pp. 359-366
  • Kuo, S, M., Lee, B. (2001). Real-Time Digital Signal Processing. Implementations, Applications, and Experiments With the TMS320C55X. New York: Wiley.
  • Michale, A., Heydt, G.T. (2003). On the Use of RMS Values in Power Quality Assessment, IEEE Transactıons On Power Delıvery, Vol 18, No 4
  • Mitsubishi. (2005). Materials Kobe Tolls. General Katalogue
  • Rockwell Automation Technologies Inc. (2003). Method And Apparatus For Calculatıng Rms Value, United States Patent, Patent No.: US 6,516,279 B1
  • Vujicic, V., Milovanvev, S., Pesaljevic, M., Pejic, D., Zupunski, I. (1999). Low Frequency Stochastıc True RMS Instrument, IEEE Transactions on Instrumentation and Measurement, Vol 48, No 2, pp. 467 – 470.
  • Wey, W.S. and Huang Y.C. (2000). A CMOS Delta–Sigma True RMS Converter. IEEE Journal of Solid-State Circuits, Vol 35, No 2, pp.248-257.
  • Yu L., Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research, Vol 5, pp. 1205-1224.

Titreşim Sinyallerinin RMS İle Hesaplanmasında Zaman Kaybının Önlenmesine Yönelik Geliştirilen Bir Algoritma

Yıl 2018, , 248 - 252, 14.12.2018
https://doi.org/10.29048/makufebed.430883

Öz

Bu çalışmada; titreşim sinyallerinin kare ortalamalarının karekökü
(RMS) hesaplanması sırasında meydana gelen zaman kayıplarının önüne geçilmesine
yönelik bir algoritma geliştirilmiştir. Titreşim sinyal verisi; Inconel 718
adlı süper alaşım malzemenin, CNC Torna tezgâhında; 50 metre/dak kesme hızı, 0,15 mm/devir ilerleme hızı, 2,5 mm kesme derinliğindeki
kesimi sırasında elde edilmiştir. Veriler titreşim sensörü (PCB Piezotronic’e
ait 353B31) ve dijital-analog çevirici(DAQ 6062E) kartı ile bilgisayara alınmış
ve Matlab ortamında matematiksel ifadeler oluşturulmuştur. RMS ise bu verilerin
değerlendirilmesinde önemli değerlendirme parametrelerden bir tanesidir. Klasik algoritmada, yaklaşık 60
testlik toplam 120 milyon verinin işlendiği
RMS hesaplamasında aşırı zaman kaybı olduğu gözlemlenmiş; her bir test yaklaşık
sekiz saat sürmüştür. Bundan dolayı işlemleri çok daha hızlı yapabilen yeni bir algoritma geliştirilmiştir.
Yeni algoritma ile klasik algoritmanın sonuçları
karşılaştırılmış ve yeni
algoritmanın %100 doğru sonuçlar ürettiği tespit edilmiştir. Yeni algoritma ile zaman
kaybının %99 oranında önüne geçilmiştir.

Kaynakça

  • Genç M.H., Çataltepe Z., Pearson T. (2007). A New PCA/ICA Based Feature Selection Method. IEEE 15th, Signal Processing and Communications Applications,. SIU
  • Gençer Ç. (2009). A Microcontroller Based Application of a True Rms Value Scale System. Journal of Polytechnic. Vol: 12 No: 2 pp.79-84
  • Germer, H. (2000). A New Method and a Device for High Precision True RMS Measurements Using the Monte Carlo Method. Precision Electromagnetic Measurements Digest. pp. 581-582
  • Germer, H. (2001). High-Precision AC Measuruments Using the Monte Carlo Method. IEEE Transactions on Instrumentation and Measurument. Vol 50, No 2, pp. 457-460
  • Guyon, I., Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research. Vol 3, pp. 1157-1182
  • Güngör O. (2011). Real Time Monitoring Cutting Tool Vibration, M.Sc.Thesis, Süleyman Demirel University Graduate School of Applied and Natural Sciences Electronic Computer Education Department, ISPARTA
  • Hall, M, A. (2000). Correlation-based feature selection for discrete and numeric class machine learning. Proceedings of 17th International Conference on Machine Learning, pp. 359-366
  • Kuo, S, M., Lee, B. (2001). Real-Time Digital Signal Processing. Implementations, Applications, and Experiments With the TMS320C55X. New York: Wiley.
  • Michale, A., Heydt, G.T. (2003). On the Use of RMS Values in Power Quality Assessment, IEEE Transactıons On Power Delıvery, Vol 18, No 4
  • Mitsubishi. (2005). Materials Kobe Tolls. General Katalogue
  • Rockwell Automation Technologies Inc. (2003). Method And Apparatus For Calculatıng Rms Value, United States Patent, Patent No.: US 6,516,279 B1
  • Vujicic, V., Milovanvev, S., Pesaljevic, M., Pejic, D., Zupunski, I. (1999). Low Frequency Stochastıc True RMS Instrument, IEEE Transactions on Instrumentation and Measurement, Vol 48, No 2, pp. 467 – 470.
  • Wey, W.S. and Huang Y.C. (2000). A CMOS Delta–Sigma True RMS Converter. IEEE Journal of Solid-State Circuits, Vol 35, No 2, pp.248-257.
  • Yu L., Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy, Journal of Machine Learning Research, Vol 5, pp. 1205-1224.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Orhan Güngör 0000-0002-5398-4571

Hüseyin Bilal Macit 0000-0002-5325-5416

Abdülkadir Çakır

Yayımlanma Tarihi 14 Aralık 2018
Kabul Tarihi 6 Kasım 2018
Yayımlandığı Sayı Yıl 2018

Kaynak Göster

APA Güngör, O., Macit, H. B., & Çakır, A. (2018). Algorithm Developed for Preventing Time Loss Calculating RMS of Vibration Signals. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 9(Ek (Suppl.) 1), 248-252. https://doi.org/10.29048/makufebed.430883