An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings

Lei, Yaguo; Qiao, Zijian; Xu, Xuefang; Lin, Jing; Niu, Shantao

doi:10.1016/j.ymssp.2017.02.041

Cited by 143 publications

(74 citation statements)

References 45 publications

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“…When the fault signal x itself has overlapping group sparsity characteristics, the optimal solution is obtained by using Equation (12). However, the actual fault signal does not have sparsity characteristics, and the multi-fault signal has overlapping group sparsity characteristics under wavelet transform, so the WOGS problem is constructed, as shown in Equation (14).…”

Section: The Fault Feature Extraction Algorithm Based On Wogsmentioning

confidence: 99%

“…As a core concept of condition monitoring and fault diagnosis, a signal processing technique is an efficient and effective method for fault feature extraction. Take as examples, empirical mode decomposition (EMD) [2,7,8], wavelet transform [9,10], spectral kurtosis [3,11], stochastic resonance [12], and morphological filtering [13]. The above traditional signal processing methods are suitable for the feature extraction of a single fault; hence, they are generally inapplicable to multiple fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Weak Multiple Fault Detection Based on Weighted Morlet Wavelet-Overlapping Group Sparse for Rolling Bearing Fault Diagnosis

et al. 2020

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As one of the important parts of a mechanical transmission system, a rolling bearing often has multiple faults coexisting, and the mutual coupling between multiple faults poses a challenge for accurate diagnosis of rolling bearings. Aiming at the above problems, this paper proposes a weighted Morlet wavelet-overlapping group sparse (WOGS) algorithm for the multiple fault diagnosis of rolling bearings. On the basis of the overlapping feature of Morlet wavelet transform coefficients, a WOGS optimization model was initially constructed. Thereafter, the weight coefficients in the model were constructed by analyzing the impulse features of the signal. Thus, majorization-minimization was used to solve the optimization problem. A case study on weak multiple fault diagnosis of rolling bearings was performed to validate the effectiveness of the WOGS algorithm. Quantitative indexes are used to further discuss the extraction accuracies of different algorithms, and the results show that the proposed algorithm exhibits better performance than other algorithms.

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Section: The Fault Feature Extraction Algorithm Based On Wogsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weak Multiple Fault Detection Based on Weighted Morlet Wavelet-Overlapping Group Sparse for Rolling Bearing Fault Diagnosis

et al. 2020

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“…Luo [50] gave specific steps to solve the USSSR parameters by ant colony optimization algorithm. Lei et al [51] further analyzed the advantages of a second-order multistable SR system. However, the coupling model studies the SR system from another point of view.…”

Section: Introductionmentioning

confidence: 99%

A Fault Feature Extraction Method Based on Second-Order Coupled Step-Varying Stochastic Resonance for Rolling Bearings

Yuan

Chen

2020

Applied Sciences

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In mechanical equipment, rolling bearings analyze and monitor their fault based on their vibration signals. Vibration signals obtained are usually weak because the machine works in a noisy background that makes it very difficult to extract its feature. To address this problem, a second-order coupled step-varying stochastic resonance (SCSSR) system is proposed. The system couples two second-order stochastic resonance (SR) systems into a multistable system, one of which is a controlled system and the other of which is a controlling system that uses the output of one system to adjust the output of the other system to enhance the weak signal. In this method, we apply the seeker optimization algorithm (SOA), which uses the output signal-to-noise ratio (SNR) as the estimating function and combines the twice-sampling technology to adaptively select the parameters of the coupled SR system to achieve feature enhancement and collection of the weak periodic signal. The simulation and real fault data of a bearing prove that this method has better results in detecting weak signals, and the system output SNR is higher than the traditional SR method.

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“…The mechanism of such amplification has been addressed theoretically and experimentally in past research [2,7,8]. The applicability of this interesting phenomenon to various fields, including system control [9], imaging [10,11], nano-devices [12][13][14], localization [15], and fault diagnosis [16,17], has also been discussed.…”

Section: Introductionmentioning

confidence: 99%

Enhancing a BPSK receiver by employing a practical parallel network with Stochastic resonance

Tadokoro

Tanaka

Nakashima

et al. 2019

NOLTA

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Stochastic resonance (SR) is a noise-enhancement phenomenon that enables the detection of sub-threshold signals by adding noise and using nonlinear systems. This paper explores the applicability of SR in a BPSK receiver with sub-threshold signals. Although received signals are amplified as a result of the nonlinear behavior of the receiver, they are somewhat distorted. This results in the lower performance of SR receivers in comparison with linear receivers. Employing a parallel network of SR systems is expected to solve this problem. The present theoretical analysis demonstrates that in a certain noise intensity range, the output of the network can fully describe an input sub-threshold signal, and hence, the performance close to that of the linear receivers can be obtained. The effectiveness of the SR receiver was also demonstrated through a numerical example of the bit error rate (BER). However, achieving good BER performance requires an infinite number of arrayed SR systems, which is not realistic in practical systems. A design framework for an SR network with a finite number of elements and an appropriate noise intensity that can realize BER performance close to that in linear systems is also provided.

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An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings

Cited by 143 publications

References 45 publications

Weak Multiple Fault Detection Based on Weighted Morlet Wavelet-Overlapping Group Sparse for Rolling Bearing Fault Diagnosis

Weak Multiple Fault Detection Based on Weighted Morlet Wavelet-Overlapping Group Sparse for Rolling Bearing Fault Diagnosis

A Fault Feature Extraction Method Based on Second-Order Coupled Step-Varying Stochastic Resonance for Rolling Bearings

Enhancing a BPSK receiver by employing a practical parallel network with Stochastic resonance

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