A Novel Machining Signal Filtering Technique: Z-notch Filter
Autor: | Nuawi M., Z., Lamin, F., Ismail A., R., Abdullah, S., Wahid, Z. |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2009 |
Předmět: | |
ISSN: | 1051-1059 |
DOI: | 10.5281/zenodo.1058447 |
Popis: | 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. {"references":["T.W. S. Chowand and H. Z. Tan, \"HOS-based nonparametric and\nparametric methodologies for machine fault detection,\" IEEE Trans. Ind.\nElectron,vol. 47, 2000, pp. 1051-1059.","E.J. Weller, H.M. Schrier and B. Weichbrodt, \"What sound can be\nexpected from a worn tool?,\" J. of Engineering Industry, vol. 91, no. 3,\n1969, pp. 525-534.","F.J. Alonso and D.R. Salgado, \"Application of singular spectrum\nanalysis to tool wear detection using sound signals,\" Proc. of the\nIMechE, J. of Engineering Manufacture, vol. 219, no. 9, 2005, pp. 703-\n710.","A.B. Sadat and S. Raman, \"Detection of tool flank wear using acoustic\nsignature analysis,\" J. of Wear, vol. 115, no. 3, 1987, pp. 265-272.","R.G. Silva, R.L. Reuben, K.J. Baker and S.J. Wilcox, \"Tool wear\nmonitoring of turning operations by neural network classification of a\nfeature set generated from multiple sensors,\" Mechanical Systems and\nSignal Processing,\" vol. 12, 1998, pp. 319-332.","M.C. Lu Jr. and E. Kannatey-Asibu, \"Analysis of sound characteristics\nassociated with adhesive wear in machining,\" Trans. of NAMRI, vol.\n28, 2000, pp. 257-262.","J. Kopac and S. Sali, Tool wear monitoring during the turning process.\nJournal of Materials Processing Technology. Vol. 113, no. 1-3, 2001,\npp. 312-316.","M.C. Lu Jr. and E. Kannatey-Asibu, \"Analysis of sound signal\ngeneration due to flank wear in turning,\" J. of Manufacturing Science\nand EngineeringÔÇöTransactions of the ASME, vol. 124 no. 4, 2002, pp.\n799-808.","R.G. Silva, K.J. Baker and S.J. Wilcox, \"The adaptability of a tool wear\nmonitoring system under changing cutting conditions,\" Mechanical\nSystems and Signal Processing, vol. 14, no. 2, 2000, pp. 287-298.\n[10] M.C. Lu Jr. and E. Kannatey-Asibu, \"Flank wear and process\ncharacteristic effect on system dynamics in turning,\" J. of Manufacturing\nScience and EngineeringÔÇöTransactions of the ASME, vol. 126 No. 1,\n2004, pp. 131-140.\n[11] Li Dan and J. Mathew, \"Tool Wear and Failure Monitoring Techniques\nfor Turning: a Review,\" Int. J. Mach. Tools Manufact, vol. 30, no. 4,\n1990, pp. 579-598.\n[12] B. Brophy, K. Kelly and G. Bryne, \"AI-based Condition Monitoring of\nthe Drilling Process,\" J. of Material Processing Technology, vol. 124,\n2002, pp. 305-310.\n[13] Z.K. Peng and F.L. Chu, \"Application of the wavelet transform in \nmachine condition monitoring and fault diagnostics: a review with \nbibliography,\"Mechanical Systems and Signal Processing, vol. 18, 2004, \npp. 199–221. \n[14] F. Lamin, M. Z. Nuawi, S. Abdullah and C. K. E. Nizwan, \"A Study of \na Machining Signal Analysis Using an Alternative Filtering Approach,\" \nProc. of World Engineering Congress, vol. 2, 2007, pp. 125-131. \n[15] Nuawi M. Z., Nor M. J. M., Jamaluddin N., Abdullah S., Lamin F., \nNizwan C. K. E. 2008, \"Development of Integrated Kurtosis-Based \nAlgorithm for Z-notch filter Technique,\" J. of Applied Sciences, vol. 8, \nno. 8, pp. 1541-1547. \n[16] Correlation analysis, \"Matlab 2007 User Guide,\" The MathWorks, Inc."]} |
Databáze: | OpenAIRE |
Externí odkaz: |