Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
A Novel Machining Signal Filtering Technique: Z-notch Filter

Authors: Nuawi M. Z., Lamin F., Ismail A. R., Abdullah S., Wahid Z.

Abstract:

A filter is used to remove undesirable frequency information from a dynamic signal. This paper shows that the Znotch filter filtering technique can be applied to remove the noise nuisance from a machining signal. In machining, the noise components were identified from the sound produced by the operation of machine components itself such as hydraulic system, motor, machine environment and etc. By correlating the noise components with the measured machining signal, the interested components of the measured machining signal which was less interfered by the noise, can be extracted. Thus, the filtered signal is more reliable to be analysed in terms of noise content compared to the unfiltered signal. Significantly, the I-kaz method i.e. comprises of three dimensional graphical representation and I-kaz coefficient, Z∞ could differentiate between the filtered and the unfiltered signal. The bigger space of scattering and the higher value of Z∞ demonstrated that the signal was highly interrupted by noise. This method can be utilised as a proactive tool in evaluating the noise content in a signal. The evaluation of noise content is very important as well as the elimination especially for machining operation fault diagnosis purpose. The Z-notch filtering technique was reliable in extracting noise component from the measured machining signal with high efficiency. Even though the measured signal was exposed to high noise disruption, the signal generated from the interaction between cutting tool and work piece still can be acquired. Therefore, the interruption of noise that could change the original signal feature and consequently can deteriorate the useful sensory information can be eliminated.

Keywords: Digital signal filtering, I-kaz method, Machiningmonitoring, Noise Cancelling, Sound

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058447

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1517

References:


[1] T.W. S. Chowand and H. Z. Tan, "HOS-based nonparametric and parametric methodologies for machine fault detection," IEEE Trans. Ind. Electron,vol. 47, 2000, pp. 1051-1059.
[2] E.J. Weller, H.M. Schrier and B. Weichbrodt, "What sound can be expected from a worn tool?," J. of Engineering Industry, vol. 91, no. 3, 1969, pp. 525-534.
[3] F.J. Alonso and D.R. Salgado, "Application of singular spectrum analysis to tool wear detection using sound signals," Proc. of the IMechE, J. of Engineering Manufacture, vol. 219, no. 9, 2005, pp. 703- 710.
[4] A.B. Sadat and S. Raman, "Detection of tool flank wear using acoustic signature analysis," J. of Wear, vol. 115, no. 3, 1987, pp. 265-272.
[5] R.G. Silva, R.L. Reuben, K.J. Baker and S.J. Wilcox, "Tool wear monitoring of turning operations by neural network classification of a feature set generated from multiple sensors," Mechanical Systems and Signal Processing," vol. 12, 1998, pp. 319-332.
[6] M.C. Lu Jr. and E. Kannatey-Asibu, "Analysis of sound characteristics associated with adhesive wear in machining," Trans. of NAMRI, vol. 28, 2000, pp. 257-262.
[7] J. Kopac and S. Sali, Tool wear monitoring during the turning process. Journal of Materials Processing Technology. Vol. 113, no. 1-3, 2001, pp. 312-316.
[8] M.C. Lu Jr. and E. Kannatey-Asibu, "Analysis of sound signal generation due to flank wear in turning," J. of Manufacturing Science and EngineeringÔÇöTransactions of the ASME, vol. 124 no. 4, 2002, pp. 799-808.
[9] R.G. Silva, K.J. Baker and S.J. Wilcox, "The adaptability of a tool wear monitoring system under changing cutting conditions," Mechanical Systems and Signal Processing, vol. 14, no. 2, 2000, pp. 287-298.
[10] M.C. Lu Jr. and E. Kannatey-Asibu, "Flank wear and process characteristic effect on system dynamics in turning," J. of Manufacturing Science and EngineeringÔÇöTransactions of the ASME, vol. 126 No. 1, 2004, pp. 131-140.
[11] Li Dan and J. Mathew, "Tool Wear and Failure Monitoring Techniques for Turning: a Review," Int. J. Mach. Tools Manufact, vol. 30, no. 4, 1990, pp. 579-598.
[12] B. Brophy, K. Kelly and G. Bryne, "AI-based Condition Monitoring of the Drilling Process," J. of Material Processing Technology, vol. 124, 2002, pp. 305-310.
[13] Z.K. Peng and F.L. Chu, “Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography,”Mechanical Systems and Signal Processing, vol. 18, 2004, pp. 199–221.
[14] F. Lamin, M. Z. Nuawi, S. Abdullah and C. K. E. Nizwan, “A Study of a Machining Signal Analysis Using an Alternative Filtering Approach,” Proc. of World Engineering Congress, vol. 2, 2007, pp. 125-131.
[15] Nuawi M. Z., Nor M. J. M., Jamaluddin N., Abdullah S., Lamin F., Nizwan C. K. E. 2008, “Development of Integrated Kurtosis-Based Algorithm for Z-notch filter Technique,” J. of Applied Sciences, vol. 8, no. 8, pp. 1541-1547.
[16] Correlation analysis, “Matlab 2007 User Guide,” The MathWorks, Inc.