Optimal frequency band selection using blind and targeted features for spectral coherence-based bearing diagnostics: A comparative study

Chen, Bingyan; Cheng, Yao; Zhang, Weihua; Gu, Fengshou; Mei, Guiming

doi:10.1016/j.isatra.2021.08.025

Cited by 38 publications

(20 citation statements)

References 34 publications

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“…The diagnostic performance of the proposed methods has been verified using different axle bearing In addition, to quantitatively evaluate the feature enhancement performance, a metric based on the envelope spectrum, the frequency-domain signal-to-noise ratio (FDSNR) 57 , is employed, as follows:…”

Section: Performance Analysismentioning

confidence: 99%

Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics

Chen

Zhang

Song

et al. 2023

Structural Health Monitoring

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A sparse squared envelope is crucial for efficient and accurate diagnosis of bearing faults. Blind deconvolution (BD) is a well-established sparse feature enhancement method for the diagnostics of rolling bearings. Traditional BD methods, such as minimum entropy deconvolution (MED), are susceptible to random transients, making it difficult to enhance fault features of rolling bearings subject to strong random shocks. Deconvolution methods that take the fault characteristic frequency (or fault impulse period) of interest as an algorithm input parameter, such as maximum second-order cyclostationarity blind deconvolution (CYCBD), can alleviate this deficiency. However, bearing fault features are difficult to be enhanced by these methods when the specified characteristic frequency deviates from the actual value greatly. To overcome these problems, the modified smoothness index (MSI) of the squared envelope is proposed as the objective function of the deconvolution method, and a new BD method is developed to achieve a sparse squared envelope for fault diagnosis of rolling bearings. Furthermore, the methodology is extended to the frequency domain, and another new BD method that utilizes the MSI of the squared envelope spectrum as the objective function is established to achieve a sparse squared envelope spectrum for bearing diagnostics. These two proposed BD methods are robust to random transients and do not require characteristic frequency or impulse period as an input parameter for feature enhancement. The performance of the two proposed BD methods is verified on experimental datasets from two different railway axle bearing test rigs and compared with the state-of-the-art deconvolution methods. The results show that the two proposed methods can effectively enhance repetitive transient features in noisy vibration signals and accurately diagnose different faults of railway axle bearings.

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Section: Performance Analysismentioning

confidence: 99%

Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics

Chen

Zhang

Song

et al. 2023

Structural Health Monitoring

Self Cite

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“…However, the experimental results under complex operating conditions show that it is difficult for traditional sparsity measures to have strong random transient resistibility and repetitive transient discriminability simultaneously. For example, kurtosis and L2/L1 norm have strong capability to identify repetitive transients, but are easily affected by high-intensity random transients; GI and the reciprocal of smoothness index are robust to outliers, but show insufficient repetitive transient discriminability under low signal-to-noise ratio (SNR) 22,24 .…”

Section: Introductionmentioning

confidence: 99%

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Chen

Zhang

et al. 2023

Structural Health Monitoring

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The Gini index (GI), GI II, and GI III are proven to be effective sparsity measures in the fields of machine condition monitoring and fault diagnosis, and they can be reformulated as the ratio of different quasi-arithmetic means (RQAM). Under this framework, generalized Gini indices (GGIs) have been developed for sparse quantification by applying nonlinear weights to GI, and another generalized form of GI, referred to here as power function-based Gini indices I (PFGI1s), has been introduced by using power function as the generator of quasi-arithmetic means. The GGIs with different weight parameters exhibit reliable sparse quantization capability for repetitive transient features, while their repetitive transient discriminability is lower than kurtosis and negentropy under noise contamination. PFGI1 achieves enhanced repetitive transient discriminability with increasing power exponent, showing the advantage of the generalization approach. In this paper, based on RQAM, a single-parameter generalization method for generating PFGI1s is introduced into GI II and GI III from the perspective of the quasi-arithmetic mean generator, which leads to the power function-based Gini indices II and III (PFGI2s and PFGI3s) constructed from GI II and GI III, respectively. Mathematical derivation proves that PFGI2s and PFGI3s satisfy at least five of six typical attributes of sparsity measures and are two new families of sparsity measures. Simulation analysis shows that, similar to PFGI1s, PFGI2s and PFGI3s can monotonically estimate the sparsity of the data sequence and can simultaneously achieve strong random transient resistibility and high repetitive transient discriminability compared with traditional sparsity measures. The experimental results of bearing run-to-failure demonstrate that PFGI1s, PFGI2s, and PFGI3s with appropriate power exponents can effectively quantify the repetitive transient features caused by bearing faults and can accurately characterize the bearing degradation status compared with the state-of-the-art sparsity measures.

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“…This method has been applied to the vibration signal analysis of rotating machinery and has achieved effective diagnostics performance under different conditions [26,27,28,29]. Similarly, Chen et al [30]developed an FCF-based method to identify the optimal spectral frequency band of the SCoh for bearing fault detection, and systematically compared the performances of various blind and target features under the framework of IESFOgram. Furthermore, Zhang et al [31] proposed an FCF-based strategy to construct the envelope spectrum tool by integrating the SCoh over the full spectral frequency band with weights.…”

Section: Introductionmentioning

confidence: 99%

Early fault identification of rolling element using IESCFFOgram

Sahoo¹,

Sudhahar²,

Kumar³

2023

Int. J. Sci. Res. Arch.

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Early fault identification of the rolling element bearings remains difficult because the repetitive transient signature generated via localized incipient damage is easily submerged by various interference components and strong noise. Spectral coherence (SCoh) is a break- through approach for revealing the second-order cyclostationary of bearing faults by displaying the energy flow of vibration signal jointly in a two-dimensional plane comprising the resonance frequency and bearing fault frequency. Considering the non-uniformity of fault in- formation distribution in the whole spectral frequency band, the enhanced envelope spectrum (EES) obtained by integrating over the full spectral frequency band is vulnerable to strong background noise. Thus, how to identify an informative spectral frequency band for constructing a diagnostic improved envelope spectrum (IES) is crucial to accurately identify bearing faults. To address this issue, a feature- adaptive method called IES via Candidate Fault Frequency Optimization-gram (IESCFFOgram) is proposed to determine the in- formative spectral frequency band from SCoh for bearing fault diagnosis. The innovation of this method is to fully excavate the fault information hidden in the SCoh and adaptively determine the informative spectral frequency band according to the identified candidate fault frequencies. The proposed method is tested and validated on simulated signals, vibration datasets obtained from artificial fault bearing experiments, and accelerated bearing degradation tests. In addition, comparisons with state-of-the art methods have been conducted to highlight the superiority of the proposed methodology.

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Optimal frequency band selection using blind and targeted features for spectral coherence-based bearing diagnostics: A comparative study

Cited by 38 publications

References 34 publications

Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics

Squared envelope sparsification via blind deconvolution and its application to railway axle bearing diagnostics

Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring

Early fault identification of rolling element using IESCFFOgram

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