Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis

Chen, Bingyan; Zhang, Weihua; Gu, Jiahui; Song, Dongli; Cheng, Yao; Zhou, Zewen; Gu, Fengshou; Ball, Andrew

doi:10.1016/j.ymssp.2023.110270

Cited by 52 publications

(20 citation statements)

References 57 publications

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“…When the inner race defect goes through the unloaded zone, F = 0, it passes through the loaded zone, F = 1. Here, τ i is the effect of random slippage of rolling elements and stands for the ith realization of a random variable with a zero mean and a uniform distribution, and the level of slip is usually less than 2% [50]. When a rolling element defect strikes either an inner or outer raceway, the simulation signal of a ball fault can be obtained using equation ( 22)…”

Section: Numerical Simulationmentioning

confidence: 99%

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Li,

Yan,

Hou

et al. 2024

Meas. Sci. Technol.

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Due to the coupling of multiple fault feature information and contamination of heavy background noise, it is a challenging task to accurately identify Rolling Bearing Compound Fault (RBCF). A method for isolating and identifying the RBCF is proposed by integrating the Adaptive Periodized Singular Spectrum Analysis (APSSA) with Rényi entropy (RE). The adaptive selection of embedding dimension of Hankel matrix in APSSA without setting parameters empirically is proposed, and a selection criterion for singular values is established to preprocess the vibration signals of rolling bearing and enhance fault periodic component. A RE-based threshold value is introduced to further isolate and decouple the impulse segments of vibration signal in the time domain. Considering inner raceway fault, outer raceway fault, ball fault and skidding, a comprehensive simulation model of compound fault is constructed by the response mechanism of different excited resources. Simulated and experimental data are applied to validate the effectiveness and practicability of proposed method. The results demonstrate that RBCF can be identified correctly by the proposed method under strong background noise.

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Section: Numerical Simulationmentioning

confidence: 99%

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Li,

Yan,

Hou

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Based on the expression of GES, it can be concluded that parameter p has a great impact on the final analysis result, which should be paid more attention. According to the research results in reference 32, for the signal with the modulation components are polluted by the strong noise components, which means the fault signatures are masked by irrelevant parts. The amplitude of the noise components is larger.…”

Section: The Basic Principlesmentioning

confidence: 99%

“…Moreover, based on the formation of ES and SES, the generalized envelope spectra (GES) and the product envelope spectrum (PES) are constructed, which is demonstrated in the fault diagnosis of REBs. 32 However, how to select the parameter in the GES and PES more properly is still worth further research. Teager energy operator and high order energy are also used for the enhancement of fault features, and the demodulation effect can be enhanced.…”

Section: Introductionmentioning

confidence: 99%

“…To amplify the frequency components contained in signals and attenuate the uncorrelated parts, the autocorrelated envelopes and cross‐correlation spectrum are employed, 30,31 which show great advantages in noise suppression. Moreover, based on the formation of ES and SES, the generalized envelope spectra (GES) and the product envelope spectrum (PES) are constructed, which is demonstrated in the fault diagnosis of REBs 32 . However, how to select the parameter in the GES and PES more properly is still worth further research.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings

Zhu,

Liu,

Zhu

2024

Adaptive Control & Signal

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SummarySince bearing defects usually occur which threaten the stable operation of the machinery, bearing fault detection is of great importance. However, the bearing fault signals inevitably exhibit strong interference components due to the complex structures of the real equipment, which leads to difficulty in fault feature detection. To address the problem, a fault diagnosis method based on the ensemble average of enhanced generalized envelope spectrum (EAEGES) was constructed. First, to suppress the irrelevant components, the optimal analysis frequency band was determined based on the variable frequency band division criteria. Second, the optimal signal was divided into numerous sub‐signals, the EAEGES was established based on the principles of the generalized envelope spectrum to strengthen the capability in detecting bearing fault features, while the improved pulse extraction operator was utilized as the target. Finally, abundant fault information can be distinguished in the feature map acquired using the proposed technique. This technique shows high effectiveness in extracting the defect signatures of the rolling element bearing, which is demonstrated using the simulation and experimental signals and can be applied to real applications.

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“…Resonance demodulation is a common method for fault diagnosis of rotating machinery, and accurate identification of the central frequency and bandwidth of the resonance frequency band of rotating machinery is the key to the effective diagnosis of rolling bearing faults. 3 However, due to the influence of noise interference, signal transmission path, and other factors, when the rotating machinery fails, the excited transient impacts are relatively weak and easy to be submerged by other components, resulting in the resonance frequency band being not prominent. 4 Antoni et al 5,6 proposed a method to detect transient impact in vibration signals by kurtosis.…”

Section: Introductionmentioning

confidence: 99%

Harmonic spectral correlated kurtosis and an adaptive matching extraction strategy of multi-fault features for rotating machinery

Cai

Ran

Tang

et al. 2023

Structural Health Monitoring

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Rotating machinery is an important and easily damaged component in large-scale equipment. Under the coupling action of system components, the occurrence rate of compound faults is very high, which seriously endangers equipment safety. The vibration signals of the damaged rotating machine include equipment operation vibration information, periodic impacts, environmental noise, and even accidental impacts. To effectively extract multi-fault features from compound fault signals, a multi-period pulse detection indicator called harmonic spectrum correlation kurtosis (HSCK) is proposed in this paper. Based on this, an adaptive matching extraction strategy for multiple fault features is proposed. By introducing variational mode decomposition, an adaptive plane paving method for signal components is designed, and an enhanced cyclic frequency estimation method is proposed to pre-determine the fault characteristic frequency as a prior parameter of HSCK, so as to obtain the optimal center frequency and bandwidth of multiple resonance bands. The implementation of this strategy can obtain more periodic pulse information with a high signal-to-noise ratio. Simulation results show that the strategy is accurate and effective. The data of wheel-bearing compound fault and bearing multi-element compound fault indicate that the proposed strategy can be used for compound fault diagnosis of rotating machinery.

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Product envelope spectrum optimization-gram: An enhanced envelope analysis for rolling bearing fault diagnosis

Cited by 52 publications

References 57 publications

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Ensemble average of enhanced generalized envelope spectrum for fault detection of rolling element bearings

Harmonic spectral correlated kurtosis and an adaptive matching extraction strategy of multi-fault features for rotating machinery

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