Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure

Yong, Duan; Wang, Chengdong; Chen, Yong; Liu, Peisen

doi:10.3390/app9091888

Cited by 10 publications

(10 citation statements)

References 28 publications

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“…The ratio correction method uses the ratio of two spectral lines with a difference of 1 near the peak of the main lobe of the window spectral function after frequency normalization to establish an equation with the normalized correction frequency as a variable, solve the normalized correction frequency, and then perform spectrum correction 19 , 20 . If the spectral function of the normalized window function is W1(f1), then the amplitude of the window spectral function is W1(∇f1).…”

Section: Double Correction Methods Of Spectrum Signalmentioning

confidence: 99%

Beat frequency extraction method of high speed and high noise Doppler signal based on double correction

Sun

Wang

et al. 2023

Opt. Eng.

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When detecting ultrahigh speed micro targets, because the photon Doppler signal will be seriously interfered with by high-frequency noise, it is difficult to accurately extract the beat frequency information, resulting in serious speed demodulation error. Therefore, we propose a beat frequency extraction method for high-speed and high-noise Doppler signals based on double correction, which can accurately correct the beat frequency signals of high-frequency noise interference. First, the ratio correction method is used for the first spectrum correction to screen out the normalized spectrum of high-frequency noise interference; then, according to the degree of noise interference, the energy barycenter correction method or linear interpolation method is used for the second spectrum correction. Numerical simulation results show that this method can reduce the spectral error by 1.6% to 2.1%. We used this method to process the Doppler signal from the detonation small flyer experiment in Initiating explosive device testing.The results show that it can reduce the beat frequency error by 1.83 times at most under highfrequency noise interfered, equivalent to the instantaneous velocity error of 1955 m/s can be decreased after velocity demodulation. Therefore, the beat frequency extraction method based on double correction has good noise resistance and reliability. We corrected the different regions' frequency spectrums according to the noise interference intensity, which provides an idea for the beat frequency extraction of high-speed and high-noise Doppler signals.

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Section: Double Correction Methods Of Spectrum Signalmentioning

confidence: 99%

Beat frequency extraction method of high speed and high noise Doppler signal based on double correction

Sun

Wang

et al. 2023

Opt. Eng.

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“…This model can be implemented in real time for monitoring failures in roller bearings. 93 A frequency matching linear transform technique is reported for bearing fault detection under variable rotating speeds 94 and Huffman coding technique is also used to identify bearing defect severity. 95…”

Section: Decision Tree Random Forest Ensemble Modelmentioning

confidence: 99%

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Manikandan

Duraivelu

2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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Fault diagnosis of various rotating equipment plays a significant role in industries as it guarantees safety, reliability and prevents breakdown and loss of any source of energy. Early identification is a fundamental aspect for diagnosing the faults which saves both time and costs and in fact it avoids perilous conditions. Investigations are being carried out for intelligent fault diagnosis using machine learning approaches. This article analyses various machine learning approaches used for fault diagnosis of rotating equipment. In addition to this, a detailed study of different machine learning strategies which are incorporated on various rotating equipment in the context of fault diagnosis is also carried out. Mainly, the benefits and advance patterns of deep neural network which are applied to multiple components for fault diagnosis are inspected in this study. Finally, different algorithms are proposed to propagate the quality of fault diagnosis and the conceivable research ideas of applying machine learning approaches on various rotating equipment are condensed in this article.

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“…Bearing elements fault detection: This Special Issue includes eight excellent examples of improving bearing fault detection. In the first paper, Duan et al [39] improved the fault detection rate of a general rolling bearing by combining the local mean decomposition (LMD) and the ratio correction methods. Then, Cui et al [40] discussed the diagnosis of multiple defects in a rolling bearing via vibration analysis; Shi et al [41] reported a frequency matching linear transform technique for bearing fault detection under variable rotating speeds; moreover, Yin et al [42] proposed a Huffman coding technique to identify bearing defect severity.…”

Section: Contentmentioning

confidence: 99%

Fault Diagnosis of Rotating Machine

Królczyk

Antonino-Daviu

2020

Applied Sciences

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Improving the Accuracy of Fault Frequency by Means of Local Mean Decomposition and Ratio Correction Method for Rolling Bearing Failure

Cited by 10 publications

References 28 publications

Beat frequency extraction method of high speed and high noise Doppler signal based on double correction

Beat frequency extraction method of high speed and high noise Doppler signal based on double correction

Fault diagnosis of various rotating equipment using machine learning approaches – A review

Fault Diagnosis of Rotating Machine

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