Abnormal Noise Detection Method Based on Wavelet Filter and K-LInformationZhang Gen-YuanZhejiang University of Media and Communications, Hangzhou, P.R. ChinaAbstractDe-noising and extraction of the abnormal noisesignature are important to analyze signal in which abnormal noise are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the wavelet filter-based de-noising methods are introduced to de-noise signals from mechanical defects. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. A periodicity detection method based on singular value decomposition (SVD) and K-L information modelingare used to choose the appropriate scale for the wavelet transform. The experiment result reveals that wavelet filter is more suitable and reliable to detect abnormal noise of mechanical impulse-like defect signals. 1. IntroductionRolling element bearing are of paramount importance to almost all forms of rotating machinery and are among the most common machine elements. Abnormal noise is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic, resulting in costly downtime. In order to prevent these kinds of failures from happening, various bearing condition monitoring techniques have been developed. Among them, vibration analysis has been used extensively due to its intrinsic advantage of revealing bearing failure [1] and [12]. The abnormal noise, at the early stage of defect development, is even more difficult to detect. A signal enhancing method is needed to provide more evident information for incipient defect detection of rolling element bearings.The problem of signal de-noising has a strong connection to roller element bearing prognostics. De-noising and extraction of the abnormal noise are crucial to fault prognostics in which case features are often very weak and masked by noise. However, for a situation where the noise type and frequency range are unknown, traditional filter design could become a very challenging task. Therefore, research is focused on finding alternative methods. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability [13], which is discussed in detail later in this paper. Traditionally, most of the signal de-noising approaches are dealing with detecting smooth curves from the noisy raw signals. However, the vibration signal from faulty mechanical components, such as gears and bearings, are more like impulses, instead of smooth and continuous curves. This unique feature constrains the application of conventional signal de-noising method. A de-noising method based on Morlet wavelet analysis is proposed and applied to the feature extraction of gearbox vibration signals [21]. This method seeks optimal wavelet filters that only yield the largest kurtosis value for the transformed signal, whereas the periodicity of the signal is not addressed.In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared. The comparison results reveal that the wavelet filter is more suitable and reliable to detect abnormal noise signaland impulse-like signatures of mechanical defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select the optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used as a criterion to optimize the shape factor of a Morlet wavelet. A periodicity detection method based on Singular Value Decomposition (SVD) and K-L information are used to choose the appropriate scale for the wavelet transform. The remaining sections of this paper are organized as follows: In Section 2, the concept of a wavelet transform is reviewed. In Section 3, the wavelet decomposition-based de-noising method is discussed in detail. A comparison study is presented using two sets of simulated signals. The results suggest that for impulse-like signals, wavelet decomposition-based de-noising method is not able to achieve a satisfactory level of performance. In Section 4, the Morlet wavelet filter and its underlying capability of detecting abnormal noise signal from a noisy background is discussed and demonstrated using simulated signals. In Section 5, The result demonstrates that by designing an optimal waveletfilter bearing abnormal noise can be detected at an 2009 World Congress on Computer Science and Information Engineering978-0-7695-3507-4/08 $25.00 © 2008 IEEEDOI 10.1109/CSIE.2009.162242009 World Congress on Computer Science and Information Engineering978-0-7695-3507-4/08 $25.00 © 2008 IEEEDOI 10.1109/CSIE.2009.16224
