Marzieh Zare
ABSTRACT
While the data mining intermediaries play a critical role in the rock drilling industry, they also tend to provide an optimized real-time model for the drilling systems. In addition, proper online tool condition monitoring (OTOM) methods can improve the drilling performance by accessing real-time data. Hence, OTOM methods assist depreciating error and detect unspecified faults at early stages. In this study, we proposed appropriate OTOM algorithms to develop and enhance the quality of real-time systems and provide a solution to detect and categorize various stages of drilling operation with the aid of vibration signals (especially in terms of acceleration or velocity). In particular, the proposed methods in this article perform based on statistical approaches. Therefore, in order to recognize the drilling stages, we measured the Root Mean Square (RMS) values corresponding to the acceleration signals. In the meantime, we also succeeded to distinguish the drilling stages by employing estimated power spectral density (PSD) in the frequency domain. The acquired results in this publication confirm the real-time prediction and classification potential of the proposed methods for the different drilling stages and especially for the rock drilling engineering.
- J. Manyika et al., "Big data: The next frontier for innovation, competition, and productivity," 2011.Google Scholar
- D. C. Montgomery, "Big data and the quality profession," Qual. Reliab. Eng. Int., vol. 30, no. 4, p. 447, 2014.Google ScholarCross Ref
- D. P. Moran, "System for optimizing drilling in real time." Google Patents, 2006.Google Scholar
- E. Levrat, B. Iung, and A. Crespo Marquez, "E-maintenance: review and conceptual framework," Prod. Plan. Control, vol. 19, no. 4, pp. 408--429, 2008.Google ScholarCross Ref
- J. Fan, F. Han, and H. Liu, "Challenges of Big Data Analysis," Natl. Sci. Rev., vol. 1, no. 2, pp. 293--314, Jun. 2014.Google ScholarCross Ref
- J. L. Ortiz and R. A. Carrasco, "ALMA engineering fault detection framework," no. June, p. 94, 2018.Google Scholar
- D. Goyal and B. S. Pabla, "The Vibration Monitoring Methods and Signal Processing Techniques for Structural Health Monitoring: A Review," Arch. Comput. Methods Eng., vol. 23, no. 4, pp. 585--594, 2016.Google ScholarCross Ref
- A. Z. Khan, A. B. Stanbridge, and D. J. Ewins, "Detecting damage in vibrating structures with a scanning LDV," Opt. Lasers Eng., vol. 32, no. 6, pp. 583--592, 1999.Google ScholarCross Ref
- M. Nouri, B. K. Fussell, B. L. Ziniti, and E. Linder, "Real-time tool wear monitoring in milling using a cutting condition independent method," Int. J. Mach. Tools Manuf., vol. 89, pp. 1--13, 2015.Google ScholarCross Ref
- J. Erkki, "A summary of methods applied to tool condition monitoring in drilling," Int. J. Mach. Tools Manuf., vol. 42, no. 9, pp. 997--1010, 2002.Google ScholarCross Ref
- Y. Kavaratzis and J. D. Maiden, "System for real time process monitoring and adaptive control during CNC deep hole drilling," in COMADEM 89 International, Springer, 1989, pp. 148--153.Google Scholar
- P. Waydande, N. Ambhore, and S. Chinchanikar, "A Review on Tool Wear Monitoring System," J. Mech. Eng. Autom., vol. 6, no. 5A, pp. 49--53, 2016.Google Scholar
- X. Shi, G. Liu, X. Gong, J. Zhang, J. Wang, and H. Zhang, "An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling," Math. Probl. Eng., vol. 2016, pp. 1--13, 2016.Google Scholar
- I. Abu-Mahfouz, "Drilling wear detection and classification using vibration signals and artificial neural network," Int. J. Mach. Tools Manuf., vol. 43, no. 7, pp. 707--720, 2003.Google ScholarCross Ref
- D. E. D. Snr, "The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation," Proc. Inst. Mech. Eng. Part B J. Eng. Manuf., vol. 215, no. 3, pp. 435--440, 2002.Google ScholarCross Ref
- C. Aliustaoglu, H. M. Ertunc, and H. Ocak, "Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system," Mech. Syst. Signal Process., vol. 23, no. 2, pp. 539--546, 2009.Google ScholarCross Ref
- A. D. Dornfeld and M. F. DeVries, "Drill wear and fracture monitoring using acoustic emission," in CIRP STC" C" meeting, Paris, 1992.Google Scholar
- W. Chen, J. C. Chen, and D. Gemmill, "Analysis of an effective sensing location for detecting tool breakage in end milling operations," J. Ind. Technol., 1998.Google Scholar
- G. D'Elia, "Fault detection in rotating machines by vibration signal processing techniques," Meccanica, p. 113, 2008.Google Scholar
- M. Lavielle, "Using penalized contrasts for the change-point problem," Signal Processing, vol. 85, no. 8, pp. 1501--1510, 2005. Google ScholarDigital Library
- R. Killick, P. Fearnhead, and I. A. Eckley, "Optimal detection of changepoints with a linear computational cost," J. Am. Stat. Assoc., vol. 107, no. 500, pp. 1590--1598, 2012.Google ScholarCross Ref
- S. Zhang, J. Mathew, L. Ma, Y. Sun, and A. D. Mathew, "Statistical condition monitoring based on vibration signals," 2004.Google Scholar
- T. I. El-Wardany, D. Gao, and M. A. Elbestawi, "Tool condition monitoring in drilling using vibration signature analysis," Int. J. Mach. Tools Manuf., vol. 36, no. 6, pp. 687--711, 1996.Google ScholarCross Ref
- C. Bingham, M. Godfrey, and J. Tukey, "Modern techniques of power spectrum estimation," IEEE Trans. audio Electroacoust., vol. 15, no. 2, pp. 56--66, 1967.Google ScholarCross Ref
- P. Welch, "The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms," IEEE Trans. audio Electroacoust., vol. 15, no. 2, pp. 70--73, 1967.Google ScholarCross Ref
- L. R. Rabiner and B. Gold, "Theory and application of digital signal processing," Englewood Cliffs, NJ, Prentice-Hall, Inc., 1975. 777 p., 1975.Google Scholar
- H. J. Nussbaumer, Fast Fourier transform and convolution algorithms, vol. 2. Springer Science & Business Media, 2012.Google Scholar
- D. F. Elliott and K. R. Rao, Fast transforms algorithms, analyses, applications. Elsevier, 1983. Google ScholarDigital Library
- F. Auger and P. Flandrin, "Improving the readability of time-frequency and time-scale representations by the reassignment method," IEEE Trans. signal Process., vol. 43, no. 5, pp. 1068--1089, 1995. Google ScholarDigital Library
- S. A. Fulop and K. Fitz, "Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications," J. Acoust. Soc. Am., vol. 119, no. 1, pp. 360--371, 2006.Google ScholarCross Ref
Index Terms
- Real-time Online Drilling Vibration Analysis Using Data Mining
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