Spectrum Sparseness: A New Method for Frequency Spectrum Analysis Applied to Equipment Condition Assessment

Liu, Chang; Yang, Tangfeng; Liu, Tao

doi:10.1109/icisce.2016.302

Cited by 2 publications

(1 citation statement)

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“…A new wavelet spectrum analysis was applied to incipient bearing fault diagnostics [10]. The frequency spectrum analysis method employing spectrum sparseness was proposed in [11], which was applied to the rolling bearing performance degradation assessment. Combining the segmentation point obtained from pretreatment and the spectrum analysis, SU Wei-Jun et al proposed a new rolling bearing fault feature extraction method employing the local spectrum [12].…”

Section: Introductionmentioning

confidence: 99%

Applications of Fractional Lower Order Frequency Spectrum Technologies to Bearing Fault Analysis

Long

Wang

2019

Mathematical Problems in Engineering

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The traditional spectral analysis method is used to study the characteristics of bearing fault signals in frequency domain, which is reasonable and effective in general cases. However, it is proved that the fault signals have heavy tails in this paper, which are α stable distribution, and 1<α<2, and even the noises belong to α stable distribution. Then the conventional spectral analysis methods degenerate and even fail under α stable distribution environment. Several improved frequency spectral analysis methods are proposed employing fractional lower order covariation or fractional lower order covariance in this paper, including fractional lower order Blackman-Tukey covariation spectrum (FLOBTCS), fractional lower order periodogram covariation spectrum (FLOPCS), and fractional lower order welch covariation spectrum (FLOWCS). In order to suppress side lobe and improve resolution, we present novel fractional lower order autoregression (FLO-AR) and fractional lower order autoregressive moving average (FLO-ARMA) parameter model frequency spectrum methods, and the calculation steps are summarized. The proposed spectrum methods are compared with the existing methods based on second-order statistics under Gaussian and SαS distribution environments, and the results show that the new algorithms have better performance than the traditional methods. Finally, the improved methods are applied to estimate frequency spectrums of the normal and outer race fault signals, and it is demonstrated that they are effective for fault diagnosis.

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Section: Introductionmentioning

confidence: 99%