HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault

Cui, Lingli; Zhang, Feibin; Chu, Fulei

doi:10.1016/j.ymssp.2018.09.043

Cited by 118 publications

(62 citation statements)

References 27 publications

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“…When a rolling element bearing fails, the rolling body will impact periodically when it touches the fault position [53][54][55][56]. Antoni and Randall [57] indicated that the vibration induced by a single fault in the rolling element bearing can be simulated as follows:…”

Section: Model Of the Collected Vibrationmentioning

confidence: 99%

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

Ming

Zhang

et al. 2019

Applied Sciences

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In order to extract and enhance the weak fault feature of rolling element bearings in strong noise conditions, the Empirical Wavelet Transform (EWT) is improved and a novel fault feature extraction and enhancement method is proposed by combining the Maximum Correlated Kurtosis Deconvolution (MCKD) and improved EWT method. At first, the MCKD method is conducted to de-noise the signal by eliminating the non-impact components. Then, the Fourier spectrum is segmented by local maxima or minima in the envelope of the amplitude spectrum with a pre-set threshold based on the noise level. By building up the wavelet filter banks based on the spectrum segmentation result, the signal is adaptively decomposed into several sub-signals. Finally, by choosing the most meaningful sub-signal with the maximum kurtosis, the fault feature can be extracted in the squared envelope spectrum and teager energy operator spectrum of the chosen component. Both simulations and experiments are performed to validate the effectiveness of the proposed method. It is shown that the spectrum segmentation result of improved EWT is more reasonable than the traditional EWT in strong noise conditions. Furthermore, compared with commonly used methods, such as the Fast Kurtogram (FK) and the Optimal Wavelet Packet Transform (OWPT) method, the proposed method is more effective in the fault feature extraction and enhancement of rolling element bearings.

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Section: Model Of the Collected Vibrationmentioning

confidence: 99%

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

Ming

Zhang

et al. 2019

Applied Sciences

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“…Rotating machinery is the most commonly used type of machine in modern industry, civilian and military applications, such as compressors, steam turbines, automobiles, industrial fans, and aircraft engines [1][2][3][4][5][6][7]. Due to the high service load, harsh operating conditions or inevitable fatigue, faults may develop in rotating machinery [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

171

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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“…Adaptive impulse dictionary [8], combined time-frequency dictionary [9], matching pursuit [10], and dictionary learning method [11] were presented to diagnose faults of rotating machinery. In addition to the above mentioned two popular methods, many other research methods such as fault quantitative diagnosis [12], fault mechanism research [13,14], fault diagnosis of low speed machinery [15] and fault location diagnosis [16] also gained attention. Some traditional methods such as Fourier transform, envelope analysis method, empirical mode decomposition, wavelet transform, spectral kurtosis, and morphological filtering have shown their advantages on single fault detection [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

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Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

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HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault

Cited by 118 publications

References 27 publications

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

Fault Feature Extraction and Enhancement of Rolling Element Bearings Based on Maximum Correlated Kurtosis Deconvolution and Improved Empirical Wavelet Transform

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

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