Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation denoising approach

Li, Qing; Liang, Steven Y.

doi:10.21595/jve.2017.18803

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…In refs. [26,27], Li et al proposed a bicomponent sparse low-rank matrix separation and group sparsity total variation de-noising approach to extract transient fault impulses from the noisy vibration signals, using the rolling bearing and gearbox as example. In ref.…”

Section: Symmetry 2018 10 X For Peer Review 3 Of 28mentioning

confidence: 99%

“…On the other hand, it should be noted that Equation (1) is a highly underdetermined equation, i.e., ill-posed or N − P hard problem [32,33], and has an infinite set of solutions because the number of unknowns is greater than the number of equations. Usually, convex optimization techniques are commonly used to estimate transient components from the observation signal, based on the aforementioned work [24][25][26][27][28][29][30][31], the estimation of x can be formulated as the optimization problem, i.e.,…”

Section: Sparse Representation and Filter Banksmentioning

confidence: 99%

“…where H is a specified high-pass filter, i.e., the HPF in Equation (7 values in x tend to zero, the correspondingly optimization problem in Equation (8) can be estimated by L1-norm fused lasso optimization (LFLO) model [24][25][26][27][28][29][30][31], i.e.,…”

Section: Sparse Representation and Filter Banksmentioning

confidence: 99%

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Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

Liang

2018

Symmetry

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The ability to accurately track the degradation trajectories of rotating machinery components is arguably one of the challenging problems in prognostics and health management (PHM). In this paper, an intelligent prediction approach based on asymmetric penalty sparse decomposition (APSD) algorithm combined with wavelet neural network (WNN) and autoregressive moving average-recursive least squares algorithm (ARMA-RLS) is proposed for degradation prognostics of rotating machinery, taking the accelerated life test of rolling bearings as an example. Specifically, the health indicators time series (e.g., peak-to-peak value and Kurtosis) is firstly decomposed into low frequency component (LFC) and high frequency component (HFC) using the APSD algorithm; meanwhile, the resulting non-convex regularization problem can be efficiently solved using the majorization-minimization (MM) method. In particular, the HFC part corresponds to the stable change around the zero line of health indicators which most extensively occurs; in contrast, the LFC part is essentially related to the evolutionary trend of health indicators. Furthermore, the nonparametric-based method, i.e., WNN, and parametric-based method, i.e., ARMA-RLS, are respectively introduced to predict the LFC and HFC that focus on abrupt degradation regions (e.g., last 100 points). Lastly, the final predicted data could be correspondingly obtained by integrating the predicted LFC and predicted HFC. The proposed methodology is tested using degradation health indicator time series from four rolling bearings. The proposed approach performed favorably when compared to some state-of-the-art benchmarks such as WNN and largest Lyapunov (LLyap) methods.

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Section: Symmetry 2018 10 X For Peer Review 3 Of 28mentioning

confidence: 99%

Section: Sparse Representation and Filter Banksmentioning

confidence: 99%

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Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

Liang

2018

Symmetry

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“…Parameters of TQWT can be selected according to the kurtosis maximum principle [21]. Li et al [22] proposed an incipient fault diagnosis method of bearing based on maximal spectral kurtosis TQWT and group sparsity total variation denoising. In this method the Qfactor of TQWT is pre-selected according to spectral distribution of the impulse component and then the spectral kurtosis of each scale transform coefficients is calculated to select the optimal Q-factor according to the kurtosis maximum principle.…”

Section: Introductionmentioning

confidence: 99%

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

Zhou²,

Gao

et al. 2021

Meas. Sci. Technol.

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In order to solve the difficulty of compound fault diagnosis of rolling bearings, a novel rolling bearings fault diagnosis method based on improved tunable Q-factor wavelet transform (TQWT) is proposed in this paper. Firstly, a new evaluation index of signal decomposition called KR is defined by summing kurtosis and root mean square (RMS) with weight. KR is the compromise between impulse factor and energy factor, which can better represent the fault characteristics of sub-bands obtained by TQWT. Secondly, the KR is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Thirdly, the bearing vibration signal is decomposed by the improved TQWT and the sub-bands are sorted descending according to the KR. Finally, the Hilbert envelope analysis is carried out and the fault types are determined by comparing the fault characteristic frequencies obtained from the Hilbert envelopes to the fault characteristic frequencies calculated by formula. The proposed fault diagnosis method is fully evaluated by simulation and experiments. The results demonstrate that the KR takes advantage of kurtosis and RMS and can be better used to optimize the parameter of TQWT. And the compound fault features of rolling bearings can be accurately separated into different sub-bands by the improved TQWT, which is helpful to improve the accuracy of compound fault diagnosis of rolling bearings.

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“…For the purpose of detecting fault from rotating machines, over the past decades, numerous diagnostic techniques have been developed. For example, some methods 2 of 23 are used via signal transforms, such as wavelet/wavelet packet transform [4][5][6], short-time Fourier transform [7,8], or tunable Q-factor wavelet transform (TQWT) [9,10]. Some methods are used via signal adaptive decomposition, such as empirical mode decomposition (EMD) [11,12], local mean decomposition (LMD) [13,14], and variational mode decomposition (VMD) [15,16]; in addition, some methods are used by intelligent supervised learning techniques, such as artificial neural network (ANN) [17,18], deep learning (DL) [19,20], just to mention a few.…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Detection for Gearboxes Using Majorization–Minimization and Asymmetric Convex Penalty Regularization

Liang

2018

Symmetry

Self Cite

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It is a primary challenge in the fault diagnosis community of the gearbox to extract the weak fault features under heavy background noise and nonstationary conditions. For this purpose, a novel weak fault detection approach based on majorization-minimization (MM) and asymmetric convex penalty regularization (ACPR) is proposed in this paper. The proposed objective cost function (OCF) consisting of a signal-fidelity term, and two parameterized penalty terms (i.e., one is an asymmetric nonconvex penalty regularization term, and another is a symmetric nonconvex penalty regularization term).To begin with, the asymmetric and symmetric penalty functions are established on the basis of an L1-norm model, then, according to the splitting idea, the majorizer of the symmetric function and the majorizer of the asymmetric function are respectively calculated via the MM algorithm. Finally, the MM is reintroduced to solve the proposed OCF. As examples, the effectiveness and reliability of the proposed method is verified through simulated data and gearbox experimental real data. Meanwhile, a comparison with the state of-the-art methods is illustrated, including nonconvex penalty regularization (NCPR) and L1-norm fused lasso optimization (LFLO) techniques, the results indicate that the gear chipping characteristic frequency 13.22 Hz and its harmonic (2f, 3f, 4f and 5f) can be identified clearly, which highlights the superiority of the proposed approach.

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Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation denoising approach

Cited by 7 publications

References 29 publications

Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform

Weak Fault Detection for Gearboxes Using Majorization–Minimization and Asymmetric Convex Penalty Regularization

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