Research on Fault Location of Distribution Network Based on MRSVD

Zou, Qiwu; Ye, Liangpeng; Xie, Xue‐Jun; Huang, Yu-Chu; Li, Haoquan; Sun, Yong

doi:10.1109/ihmsc.2019.00038

Cited by 1 publication

(2 citation statements)

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“…Any signal can be decomposed by the MRSVD algorithm according to the contribution rate step by step. The vibration signal can be decomposed in multi-levels and without loss, and weak fault information can be quickly and efficiently extracted from the acquisition vibration signal [54].…”

Section: Mrsvd Algorithmmentioning

confidence: 99%

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Real-time identification of performance degradation stages of rolling element bearings by RVCFI

Meng

Yan

Wen

et al. 2022

Meas. Sci. Technol.

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Rolling element bearing is a critical component, and most of the faults in rotary machines result from the failure of rolling element bearings. The performance degradation identification strategy of bearing based on condition monitoring is able to improve the safety of equipment and can give a reasonable maintenance plan in optimal time. Two novel monitoring indicators are proposed to identify the initial fault time and performance degradation stages of bearing in real time. Firstly, the multiple features can be calculated based on the envelope spectrum of the vibration signal. A fused indicator can be obtained by the weight-sum of multiple standardized features. Then, the online variation coefficient of the fused indicator and its rate are calculated. Finally, the Monitoring Indicator of Initial Fault (MIIF) and the Monitoring Indicator of Degradation Stages (MIDS) can be constructed by using variation coefficient and Cumulative Sum (CUMSUM) based on the Rate of Variation Coefficient of Fused Indicator (RVCFI) in real time, respectively. The proposed methods are verified by using two kinds of tested data packets. It is shown from the results that the methods are able to identify efficiently and timely the Initial Fault Time (IFT) and performance degradation stages of bearing.

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Section: Mrsvd Algorithmmentioning

confidence: 99%

“…MRSVD has been applied to denoise or extract the weak fault information in a vibration signal for bearing diagnosis [42,54]. However, the processing effect of the vibration signal is usually dependent on the number of decomposition levels.…”

Section: Adaptive Mrsvdmentioning

confidence: 99%