The Method of Quantitative Trend Diagnosis of Rolling Bearing Fault Based on Protrugram and Lempel–Ziv

Du, Jianxi; Cui, Lingli; Zhang, Jianyu; Li, Jin; Huang, Jinfeng

doi:10.1155/2018/4303109

Cited by 6 publications

(2 citation statements)

References 19 publications

(23 reference statements)

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“…Recently, some resonant demodulation techniques have achieved excellent performance in determining the center frequency of fault characteristic components. The fast kurtogram and the protrugram are two popular techniques for locating frequency bands of fault characteristic components [30,31]. These two methods decompose the spectrum of the signal into sub-bands with different bandwidths and center frequencies by constructing a filter bank in the form of a 1/3-binary tree.…”

Section: Introductionmentioning

confidence: 99%

Spectral-coherence guided variational mode extraction and its application in rolling bearing fault diagnosis

Sun¹,

Zhang²,

Pang³

et al. 2022

Meas. Sci. Technol.

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Variational mode extraction (VME), inspired by variational mode decomposition (VMD), is a novel fault diagnosis technique that can efficiently extract narrowband modes from multi-component signals. Compared with VMD, VME is more accurate and faster when extracting the narrowband component. However, the preset center frequency ω c and balance factor α seriously affect the performance of VME. Therefore, a spectral-coherence guided variational mode extraction (SCVME), capable of determining the hyper-parameters automatically, is proposed for fault diagnosis of rolling bearings. First, considering the advantages of spectral coherence (SCoh) for characterizing the cyclostationarity of bearing faults, its energy spectrum is further constructed. The energy spectrum of SCoh can intuitively reveal the fault information energy hidden in each frequency, which provides sufficient support for the determination of the center frequency ω c . Subsequently, a novel signal evaluation index named cyclic pulse intensity (CPI) is proposed to adaptively optimize the balance factor α. It has been verified that the proposed CPI index is superior to these common metrics, including kurtosis, spectral kurtosis (SK) and l2/l1 norm, in identifying periodic pulses. Finally, the modes containing fault information are accurately extracted by VME according to the optimal parameters (ω c , α). The effectiveness of the proposed method has been demonstrated by the simulations and experiments analysis. In addition, comparisons with the VMD and Autogram methods are carried out to highlight the superiority of the SCVME method.

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Section: Introductionmentioning

confidence: 99%

Spectral-coherence guided variational mode extraction and its application in rolling bearing fault diagnosis

Sun¹,

Zhang²,

Pang³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…To sum up, in order to solve the computational efficiency loss in the above optimization methods, this paper has proposed MGD optimization based on the protrugram algorithm (referred to as OMGD herein). It is used to obtain the center frequency and the corresponding frequency band at the maximal kurtosis [27,28]. Moreover, we can use the filter parameters obtained from this algorithm to design the filter and then to complete MGD optimization.…”

Section: Introductionmentioning

confidence: 99%

Application of FIFD-OMGD-1.5D Teager Energy to Extract Microfault Features of Rolling Bearing

Liu

2022

Mathematical Problems in Engineering

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Deconvolution-related methods are the mainstream choice when it comes to enhancing the pulse impact of bearing fault and reducing noise interference. Kurtogram algorithm is used to optimize the minimum generalized Lp/Lq deconvolution to improve the nonconvexity of other optimization criteria. However, it has low computational efficiency and poor diagnostic accuracy under strong background noise. The paper proposes an optimized method using protrugram algorithm that combines fast iterative filter decomposition (FIFD) with minimum generalized Lp/Lq deconvolution (OMGD) for the 1.5-dimension Teager energy spectrum demodulation. Here is the specific process of the application: Fast iterative filtering (FIF) was used to reduce noise interference before using the maximum kurtosis to obtain the center frequency and frequency band and optimize the filter design, which was for the MGD initialization operation to prevent the result from falling into the local optimal solution and check the interference of impulse noise to a certain extent. The 1.5-dimension Teager energy spectrum was then used for demodulation analysis to extract small fault features of rolling bearings. The verification of simulation signals and actual data showed that this method was better in terms of extraction effect and efficiency than the use of fast kurtogram algorithm to optimize minimum generalized Lp/Lq deconvolution when it comes to extracting microfault features with high interference of background noise.

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Dynamic modeling and simulation analysis of inter-shaft bearing fault of a dual-rotor system

Cai

Jun

Zhang

et al. 2023

Mechanical Systems and Signal Processing

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The Method of Quantitative Trend Diagnosis of Rolling Bearing Fault Based on Protrugram and Lempel–Ziv

Cited by 6 publications

References 19 publications

Spectral-coherence guided variational mode extraction and its application in rolling bearing fault diagnosis

Spectral-coherence guided variational mode extraction and its application in rolling bearing fault diagnosis

Application of FIFD-OMGD-1.5D Teager Energy to Extract Microfault Features of Rolling Bearing

Dynamic modeling and simulation analysis of inter-shaft bearing fault of a dual-rotor system

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