A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization

Zhong, Dongjie; Yi, Cancan; Xiao, Han; Zhang, Houzhuang; Wu, Anding

doi:10.1155/2018/2169364

Cited by 5 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GMC penalties in the form of ( 9) with (X , Z, Ψ) have already been reported (see, e.g. [25,72]). However these reports do not present any mathematical analysis related to the above key questions (Q1)-(Q4).…”

Section: Examplementioning

confidence: 94%

“…(d) (On Q4) For practical applications, it is important to establish a flexible way to design B and µ under the convexity of J ΨB •L . The GMC penalties in the form of ( 9) with (X , Z, Ψ) have already been reported (see, e.g., [25,72]). However these reports do not present any mathematical analysis related to the above key questions (Q1)-(Q4).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition

2020

View full text Add to dashboard Cite

The convex envelopes of the direct discrete measures, for the sparsity of vectors or for the low-rankness of matrices, have been utilized extensively as practical penalties in order to compute a globally optimal solution of the corresponding regularized least-squares models. Motivated mainly by the ideas in [Zhang'10, Selesnick'17, Yin, Parekh, Selesnick'19] to exploit nonconvex penalties in the regularized least-squares models without losing their overall convexities, this paper presents the Linearly involved Generalized Moreau Enhanced (LiGME) model as a unified extension of such utilizations of nonconvex penalties. The proposed model can admit multiple nonconvex penalties without losing its overall convexity and thus is applicable to much broader scenarios in the sparsity-rank-aware signal processing. Under the general overall-convexity condition of the LiGME model, we also present a novel proximal splitting type algorithm of guaranteed convergence to a globally optimal solution. Numerical experiments in typical examples of the sparsity-rank-aware signal processing demonstrate the effectiveness of the LiGME models and the proposed proximal splitting algorithm.

show abstract

Section: Examplementioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition

2020

View full text Add to dashboard Cite

show abstract

“…The scaled version of the Huber function and GMC penalty is described in this section. For b ̸ = 0, the scaled Huber function s b (x) is determined by [46]:…”

Section: Gmc Penaltymentioning

confidence: 99%

An improved sparsity-enhanced decomposition signal method based on GMC and TQWT for rolling bearing faults

Zhang¹,

Ye²,

He³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

Mechanical equipment is always exposed to the poor working environments, such as humid, high temperature and heavy load, which may lead to a serious damage in the key components. It is critical to identify the initial fault in time to avoid huge economic losses and casualty accidents. In extracting fault characteristics of the rolling bearing, its characteristic frequency is always disturbed by strong noise. In order to accurately separate the fault features from the strong noisy signal, an improved sparsity-enhanced decomposition signal method via using the nonconvex penalty term of generalized minimax-concave (GMC) and the dictionary term of tunable Q-factor wavelet transform (TQWT) is presented in this paper. An adaptive method for selecting regularization parameters is presented to subtly minimize the signal-to-noise ratio and root mean square error. Moreover, in order to reduce calculation cost, the forward-backward splitting algorithm is employed to maintain the convexity of the proposed sparsity. A simulation study and two practical fault experiments are used to validate the effectiveness of the proposed method in rolling bearing fault.

show abstract

“…A Bayesian compressive sensing approach to fault diagnosis was proposed in [24]. Feature extractions based on sparse signal representation for fault classification have been investigated in [25], [26], [27], [28]. Impulse response monitoring with convolution sparse representations was studied in [29].…”

Section: Introductionmentioning

confidence: 99%

Dynamic System Fault Diagnosis Under Sparseness Assumption

Zhang

2021

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

Dynamic system fault diagnosis is often faced with a large number of possible faults. The purpose of this paper is to propose an efficient method for such situations. To avoid intractable combinatorial problems, sparse estimation techniques appear to be a powerful tool for isolating faults, under the assumption that only a small number of possible faults can be simultaneously active. However, sparse estimation is often studied in the framework of linear algebraic equations, whereas model-based fault diagnosis is usually investigated for dynamic systems modeled with state equations involving internal states. The main contribution of this paper is a link between these two formalisms through efficient and reliable algorithms, mainly relying on advanced analyses of residuals generated with the Kalman and Kitanidis filters. Based on these results, it becomes straightforward to solve fault diagnosis problems by applying well known sparse estimation techniques, in the framework of general time varying state-space systems involving unknown inputs.

show abstract

A Novel Fault Diagnosis Method for Rolling Bearing Based on Improved Sparse Regularization via Convex Optimization

Cited by 5 publications

References 34 publications

Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition

Linearly involved generalized Moreau enhanced models and their proximal splitting algorithm under overall convexity condition

An improved sparsity-enhanced decomposition signal method based on GMC and TQWT for rolling bearing faults

Dynamic System Fault Diagnosis Under Sparseness Assumption

Contact Info

Product

Resources

About