Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Li, Qing; Liang, Steven Y.

doi:10.3390/e19080421

Cited by 17 publications

(15 citation statements)

References 45 publications

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“…Ding et al [30] employed the time-frequency (TF) dictionary and orthogonal matching pursuit (OMP) to extract the fault feature information of single row cylindrical roller bearings based on acoustic signals. Li et al [31] introduced a novel dictionary learning method called the impulse-step impact dictionary, based on a nonconvex optimization approach; the results demonstrate the method's superiority in weak fault feature extraction via accelerated lifetime testing experiment, compared with OMP, L1-norm convex penalty regularization, and the spectral Kurtogram (SK) method.…”

Section: Introductionmentioning

confidence: 99%

“…(1) Unique dictionaries' atoms and optimal wavelet basis cannot simultaneously match the natural structure of every real vibration signal well; (2) A large number of observed signals should be collected to form a training dictionary before diagnosis, which is always infeasible in practical applications; (3) Computational complexity and time-consuming problems occur simultaneously in dictionary training, such as with the K-SVD training and SI-K-SVD dictionaries training [31].…”

Section: Introductionmentioning

confidence: 99%

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

Liang

2018

Symmetry

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liang

2018

Symmetry

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“…A number of state-of-the-art methodologies including wavelet/wavelet packet transform (W-WPT) [4,5], adaptive signal decomposition (ASD) [6][7][8] and high-order cyclic spectral analysis (HOCS) [9,10], have been developed for detecting weak fault information of rotating machinery under strong noise. Recently, sparse low-rank matrix regularization (SLMR) methods, that estimate the fault signal through solving optimization problems (or inverse problems), have attracted significant attention because of their ability to induce sparsity of the fault signal (or singular values) more effectively than the traditional sparse over-complete dictionaries (SODs) such as step-impulse dictionary and unit-impulse response dictionary [11][12][13][14][15]. For the SLMR, in particular, we consider the problem of estimating fault signal…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach

Liang

2018

Algorithms

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Aimed at the issue of estimating the fault component from a noisy observation, a novel detection approach based on augmented Huber non-convex penalty regularization (AHNPR) is proposed. The core objectives of the proposed method are that (1) it estimates non-zero singular values (i.e., fault component) accurately and (2) it maintains the convexity of the proposed objective cost function (OCF) by restricting the parameters of the non-convex regularization. Specifically, the AHNPR model is expressed as the L1-norm minus a generalized Huber function, which avoids the underestimation weakness of the L1-norm regularization. Furthermore, the convexity of the proposed OCF is proved via the non-diagonal characteristic of the matrix BTB, meanwhile, the non-zero singular values of the OCF is solved by the forward–backward splitting (FBS) algorithm. Last, the proposed method is validated by the simulated signal and vibration signals of tapered bearing. The results demonstrate that the proposed approach can identify weak fault information from the raw vibration signal under severe background noise, that the non-convex penalty regularization can induce sparsity of the singular values more effectively than the typical convex penalty (e.g., L1-norm fused lasso optimization (LFLO) method), and that the issue of underestimating sparse coefficients can be improved.

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“…The periodic component to aperiodic component ratio (PAR) was utilized in this method to differentiate the types of impulses. Li et al improved the kurtogram based on an impulse step dictionary and a reweighted minimizing nonconvex penalty Lq regular for rolling bearing fault diagnosis [12].…”

Section: Introductionmentioning

confidence: 99%

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

Wan

Zhang

2018

Entropy

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Abstract:Kurtogram can adaptively select the resonant frequency band, and then the characteristic fault frequency can be obtained by analyzing the selected band. However, the kurtogram is easily affected by random impulses and noise. In recent years, improvements to kurtogram have been concentrated on two aspects: (a) the decomposition method of the frequency band; and (b) the selection index of the optimal frequency band. In this article, a new method called Teager Energy Entropy Ratio Gram (TEERgram) is proposed. The TEER algorithm takes the wavelet packet transform (WPT) as the signal frequency band decomposition method, which can adaptively segment the frequency band and control the noise. At the same time, Teager Energy Entropy Ratio (TEER) is proposed as a computing index for wavelet packet subbands. WPT has better decomposition properties than traditional finite impulse response (FIR) filtering and Fourier decomposition in the kurtogram algorithm. At the same time, TEER has better performance than the envelope spectrum or even the square envelope spectrum. Therefore, the TEERgram method can accurately identify the resonant frequency band under strong background noise. The effectiveness of the proposed method is verified by simulation and experimental analysis.

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Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Cited by 17 publications

References 45 publications

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

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

Weak Fault Detection of Tapered Rolling Bearing Based on Penalty Regularization Approach

Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

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