Adaptive Parameter Identification Based on Morlet Wavelet and Application in Gearbox Fault Feature Detection

Wang, Shibin; Zhu, Zhongkui; He, Yongyong; Huang, Weiguo

doi:10.1155/2010/842879

Cited by 13 publications

(9 citation statements)

References 20 publications

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“…With the convexity condition (21), we can reliably obtain via convex optimization the global minimum of (10) as long as the parameter b is chosen to satisfy (21). In this paper, the split augmented Lagrangian shrinkage (SALSA) [55] method is used to solve the nonconvex WATV denoising problem.…”

Section: Salsa Algorithm For Nonconvex Watvmentioning

confidence: 99%

“…The multiresolution analysis ability of the WT makes it suitable for extracting fault features from nonstationary vibration signals. Wang et al [21] proposed a method combining the Morlet wavelet and correlation filtering to identify the impulse response parameters and the cyclic period between adjacent impulses, which is an effective way to extract features of gearbox fault diagnosis. In [22], the wavelet-based multiscale slope features were extracted from the slope of logarithmic variances calculated from the wavelet coefficients of the discrete WT, and then the extraction features were used to classify gearbox faults with high accuracy and stability.…”

Section: Introductionmentioning

confidence: 99%

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Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Jiang¹,

Chang

Cong³

et al. 2020

IEEE Access

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The vibration-based analysis is an effective technique for planetary gearbox fault diagnosis, but a difficult task is how to accurately identify fault features from noisy vibration signals. In this paper, a nonconvex wavelet thresholding total variation (WATV) denoising method is proposed for planetary gearbox fault diagnosis, which combines wavelet-domain sparsity and total variation (TV) regularization. The TV regularization algorithm is employed to modify the retained wavelet coefficients so that the occurrence of oscillations caused by wavelet thresholding is suppressed. To overcome the underestimation shortcoming of L1-norm regularization, nonconvex penalty function regularization is used to strongly promote the sparsity of estimation while guaranteeing that the global optimal solutions are obtained even though the objective function is nonconvex. Then, the split augmented Lagrangian shrinkage (SALSA) method is developed to solve the nonconvex WATV denoising problem. Two experimental studies are performed to verify the performance and effectiveness of the proposed method. Comparisons with the soft thresholding and basis pursuit denoising (BPD) methods show that the proposed method can accurately estimate the fault features from vibration signals, which means that the proposed method is an effective and promising tool for planetary gearbox fault diagnosis.

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Section: Salsa Algorithm For Nonconvex Watvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Jiang¹,

Chang

Cong³

et al. 2020

IEEE Access

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“…A series of techniques have been developed to investigate the vibration signal for fault diagnosis, such as empirical mode decomposition (EMD), matching pursuit and time-scale method [4]{Wang, 2010 #23} and FFT-based techniques. Wavelet analysis is one of the effective approaches, especially for non-stationary signal processing and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Impulsive Gear Fault Diagnosis Using Adaptive Morlet Wavelet Filter Based on Alpha-Stable Distribution and Kurtogram

2019

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The vibration signals extracted from gearbox are usually masked in heavy background noise. Analyzing the feature components of these signals is challenging and crucial to incipient fault diagnosis. The Kurtogram has been verified as a very powerful and practical tool in the mechanical fault diagnosis due to its advantages in detecting and characterizing transients in signals. However, the accuracy of the kurtogram is limited because it is based on short-time Fourier transform (STFT) or FIR filter in extracting transient characteristics from a noisy signal. Therefore, it is necessary to develop a more accurate filter to overcome its shortcomings and further improve its fault detection accuracy. Wavelet transform has been widely applied in the past as a powerful tool to analyze the non-stationary signals, and an alpha-stable distribution model is often used to describe the statistical features of non-Gaussian signals. In this paper, a Morlet wavelet filter is optimized based on the kurtogram and the alpha parameter of the alpha-stable distribution model. Through the analysis of simulation signals at different fault degrees and operating conditions, it can be concluded that alpha-stable distribution has better performance than kurtosis in measuring the non-Gaussian characteristics of an impulsive gear fault signal. The results obtained from the simulation and practical experiments confirm the superiority of the proposed method for incipient gear fault diagnosis. INDEX TERMS Incipient fault diagnosis, kurtogram, Morlet wavelet, adaptive filter, alpha-stable distribution.

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“…Monitoring electrical loads and systems is particularly one of the areas where transients play a central role. We cite as applications the analysis of disturbances affecting the quality of the electric power system [7,8], fault detection in rotary machines [9,10], and non-intrusive load monitoring (NILM) [11][12][13], a field concerned with extracting individual energy consumption (e.g., of different appliances) from measured total energy consumption (e.g., at main breaker panel).…”

Section: Introductionmentioning

confidence: 99%

Electrical transient modeling for appliance characterization

Meziane

Ravier

Abed-Meraim

et al. 2019

EURASIP J. Adv. Signal Process.

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Transient signals are characteristic of the underlying phenomenon generating them, which makes their analysis useful in many fields. Transients occur as a sudden change between two steady state regimes, subsist for a short period, and tend to decay over time. Hence, superimposed damped sinusoids (SDS) were extensively used for transients modeling as they are adequate for describing decaying phenomena. However, SDS are not adapted for modeling the turn-on transient current of electrical appliances as it tends to decay to a steady state that is different from the one preceding it. In this paper, we propose a new and more suitable model for these signals for the purpose of characterizing appliances. We also propose an algorithm for the model parameter estimation and validate its performance on simulated and real data. Moreover, we give an example on the use of the model parameters as features for the classification of appliances using the Controlled On/Off Loads Library (COOLL) dataset. The results show that the proposed algorithm is efficient and that for real data the network fundamental frequency must be estimated to account for its variations around the nominal value. Finally, real data experiments showed that the model parameters used as features yielded a classification accuracy of 98%.

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Adaptive Parameter Identification Based on Morlet Wavelet and Application in Gearbox Fault Feature Detection

Cited by 13 publications

References 20 publications

Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Nonconvex Wavelet Thresholding Total Variation Denoising Method for Planetary Gearbox Fault Diagnosis

Impulsive Gear Fault Diagnosis Using Adaptive Morlet Wavelet Filter Based on Alpha-Stable Distribution and Kurtogram

Electrical transient modeling for appliance characterization

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