The Noise Cancelling Technique using Z-filter on Machining Signal M. Z. NUAWI, F. LAMIN, M. J. M. NOR, N. JAMALUDDIN, S. ABDULLAH Department of Mechanical and Materials Engineering Universiti Kebangsaan Malaysia 43600, Bangi, Selangor, Malaysia Abstract: - A filter is used to remove undesirable frequency information from a dynamic signal. This paper shows that noise nuisance can be effectively removed from a machining signal when applying the Z-filter filtering technique to extract the desired spectra components. The Z-filter can be applied as the noise of the operation that being studied can be determined. The noise components were initially identified from the sound produced by the operation of machine components itself such as hydraulic system, motor, machine environment condition and etc. By correlating the noise components with the measured machining signal, the filtered signal which was less interfered with the noise can be obtained. As a result, the interested components of the measured signal were extracted. The elimination of noise is very important for fault diagnosis where in common, the noise will change the original signal feature that can deteriorate the useful sensory information. Key-Words: - Digital signal filtering, denoising, sound, I-kaz, Noise Cancelling. 1 Introduction In order to keep the machine performing at its best condition, techniques such as fault monitoring, detection, classification, and diagnosis have become increasingly essential [1]. Different types of sensor have been used to monitor different aspect of the machine environments. The concept of sensing tool wear from the sound signal during a cutting process goes back more than thirty years [2]. There have been several studies using sound signals in this context [3-10], and their results confirm the correlation between tool wear and the sound emitted during the turning process. Li Dan and J. Mathew [11] have reported that sound from a machining operation measured near the cutting zone on a lathe contains a variety of cutting information. Some components of this sound have been used to monitor conditions of the cutting edge. The sound pressure level at the characteristic frequency showed good correlation with tool wear but in a typical machining environment this technique was impractical. This is due to the high ambient noise levels in the factory environment. If there is poor correlation between the sensor signal and the tool condition, it is unlikely that correct classification of tool state can take place. The success of any tool condition monitoring system is dependent on two factors which are the quality of the data acquired by the sensors and the diagnosis algorithm used to analyse the sensory information for determining the tool state [12]. In order to get the good quality data, the signal pre- processing is an important step to enhance the data’s reliability and thereby, to improve the accuracy of the signal analysis. The core of signal pre-processing is to increase the signal-to-noise ratio that is, to remove the noise and to highlight the signals interested [13]. However, the noise is generally unavoidable, which is usually introduced into signals by various disturbances such as the disturbance from the exotic environment, the testing instrument itself and etc. Denoising and extraction of the weak signals are very important for fault diagnostics, especially for early fault detection in which cases features are often very weak and masked by the noise [13]. The noises are often stochastic signals with broadband, whose frequency band will overlap with the interested signals’ [13]. Therefore, it is difficult to eliminate the noise from the signals effectively with general filtering methods. Because of a variety of the noise that generated during the cutting process, the analysis of the recorded acoustic signals requires an appropriate filter. A Novel denoising technique called Z-filter was introduced by Lamin [14]. In this paper, the applicability of the Z-filter denoising technique was demonstrated on machining signal. The Z-filter technique was applied in order to eliminate the noise nuisance that contains in the machining signal. The reliability of the Z-filter to eliminate noise components in machining signal was discussed. The I-kaz analysis, which was pioneered by Mohd Zaki [15] was done to the filtered and unfiltered signals in order to evaluate the applicability of the Z- Proceedings of the 7th WSEAS International Conference on SIGNAL PROCESSING, ROBOTICS and AUTOMATION (ISPRA '08) Proceedings of the 7th WSEAS International Conference on SIGNAL PROCESSING, ROBOTICS and AUTOMATION (ISPRA '08) ISSN: 1790-5117 ISBN: 978-960-6766-44-2 274 University of Cambridge, UK, February 20-22, 2008