Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

Shock and Vibration, Jun 2015

When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM). Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs) and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA) or instantaneous frequency (IF). Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective.

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Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis Songrong Luo,1,2 Junsheng Cheng,2 and Jianping Fu2 1College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415003, China 2China College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China Received 19 November 2014; Revised 20 March 2015; Accepted 5 April 2015 Academic Editor: Lei Zuo Copyright © 2015 Songrong Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM). Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs) and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA) or instantaneous frequency (IF). Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective. 1. Introduction Fault diagnosis technique is of great significance to guarantee the normal operation of mechanical and electrical equipment. When a localized defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM) [1], expressed as a frequency family on the spectrum, which contains the center frequency and its harmonic frequency. For this kind of signals, some demodulation techniques have been used to find the fault feature information. Hilbert demodulation method is widely used in machinery fault diagnosis [2, 3], but there exists window effect and end effect of Hilbert transforms inevitably, resulting in greater demodulation error. The energy separation algorithm (ESA) appears much popular in recent years for the application to machinery fault diagnosis [4–7], because it is suitable to extract the local dynamic characteristics of nonstationary signal. However, ESA requires that the processed signal should be narrow-band monocomponent [4, 5]. In addition, ESA is sensitive to noise [8]. Compared with the above time domain demodulation methods, the wavelet ridge demodulation technique is time-frequency domain demodulation method, which uses continuous wavelet transform (CWT) to obtain instantaneous amplitude (IA) information and instantaneous frequency (IF) information [8, 9]. In general, the analytic Morlet wavelet is used as the basic wavelet due to its similarity to the fault associated impacts [10–13]. But, the analytic Morlet wavelet parameters, which are center frequency and shape factor, would exert a great impact on the results of wavelet ridge demodulation. In order to select the proper parameters, some techniques have been employed [10–12]. Unfortunately, there is no mature theory to tell us how to choose them. In addition, there are few methods, which can select both center frequency and shape factor of Morlet wavelet to obtain the optimal time-scale resolution. Here, genetic algorithm (GA), which not only has better ability to search the optimal solution but also has fast convergence, is introduced to obtain the two optimal parameters according to the analyzed signal local characteristics, and Morlet wavelet with optimal parameters using GA is called self-adaptive wavelet. Therefore we will utilize self-adaptive wavelet ridge demodulation approach to obtain better demodulation results in this paper. On the other hand, to greatly eliminate the background noise and improve the demodulation accuracy, multicomponent AM-FM signals should be decomposed into monocomponent AM-FM signals before using self-adaptive wavelet ridge demodulation approach. Empirical mode decomposition (EMD) method [3, 4, 14, 15] or local mean decomposition (LMD) method [16–18] is widely employed to decompose multicomponent AM-FM signal into monocomponent AM-FM signals in general. However, EMD method still has theoretical limitations, such as frequency confusion, overshoo (...truncated)


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Songrong Luo, Junsheng Cheng, Jianping Fu. Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis, Shock and Vibration, 2015, 2015, DOI: 10.1155/2015/735853