Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

Shen, Zhaoyang; Shi, Zhanqun; Zhen, Dong; Zhang, Hao; Gu, Fengshou

doi:10.3390/s20082433

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Therefore, considering the shortcomings of a single index when selecting IMF components, this paper proposes a comprehensive evaluation index to select IMF components, which avoids wrong signal selection in the process of single index evaluation. The EEMD principle is used to decompose the original signal into a series of IMF components, and the CC, kurtosis, RMS, energy ratio (the ratio of the IMF component to the original signal) of the i-th component [39] is defined as H i,1 ,H i,2 ,H i,3 ,H i,4 . In the process of feature extraction of rotating machinery, due to the complex working environment, different indicators have different diagnostic capabilities for different fault degrees.…”

Section: Plos Onementioning

confidence: 99%

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Gao

et al. 2021

PLoS ONE

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Aiming at the problem that the weak features of non-stationary vibration signals are difficult to extract under strong background noise, a multi-layer noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed. First, the original vibration signal is decomposed by EEMD, and the main intrinsic modal components (IMF) are selected using comprehensive evaluation indicators; the second layer of filtering uses wavelet threshold denoising (WTD) to process the main IMF components. Finally, the virtual noise channel is introduced, and FastICA is used to de-noise and unmix the IMF components processed by the WTD. Next, perform spectral analysis on the separated useful signals to highlight the fault frequency. The feasibility of the proposed method is verified by simulation, and it is applied to the extraction of weak signals of faulty bearings and worn polycrystalline diamond compact bits. The analysis of vibration signals shows that this method can efficiently extract weak fault characteristic information of rotating machinery.

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Section: Plos Onementioning

confidence: 99%

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Gao

et al. 2021

PLoS ONE

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“…This characteristic can be conducted to extract the informative SVCSs. Many literatures have mentioned that due to the structural characteristics of planetary gearbox, its vibration signal is mutual modulated [32,33]. As a rotating machine, the useful vibration characteristics of planetary gearboxes have periodic modulation characteristics.…”

Section: Introductionmentioning

confidence: 99%

Informative singular value decomposition and its application in fault detection of planetary gearbox

Shen

Shi

Shen

et al. 2022

Meas. Sci. Technol.

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The faults of rotating machineries are generally characterized by the periodic singular impulses. However, the fault features of planetary gearbox are often complex modulated and submerged by other components, for its vibration signal has the characteristics of multi-source and multi transmission path. An informative singular value decomposition (ISVD) filter based on the modulation performance of singular value component signal (SVCS) is developed to enhance the fault characteristics of planetary gearbox and weaken the interference in this paper. The fault features can be detected by the envelope spectrum analysis (ESA). In this method, a fast SVD is firstly performed to decompose the vibration signal into a series of SVCSs. Secondly, the information detector of negentropy combined with cyclic autocorrelation (CA) is applied to estimate the informativeness of each SVCS. Then the ISVD filtered signal can be obtained by the reconstruction of amplified informative SVCSs. Finally, the fault features can be extracted by ESA from the ISVD filtered signal. The performance of the proposed method is verified by simulation study and experimental data measured from a planetary gearbox. Results show that the proposed ISVD-ESA strategy can enhance and extract the weak features of multi-modulation, and it can accurately extract the faults of tooth tip pitting and misalignment of sun gear of the planetary gearbox.

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Current Analysis Using a Modulation Signal Bispectrum for Machining Status Monitoring

Zou

Huang

Zou

et al. 2021

Mechanisms and Machine Science

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Fault Diagnosis of Planetary Gearbox Based on Adaptive Order Bispectrum Slice and Fault Characteristics Energy Ratio Analysis

Cited by 6 publications

References 31 publications

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Application of multi-layer denoising based on ensemble empirical mode decomposition in extraction of fault feature of rotating machinery

Informative singular value decomposition and its application in fault detection of planetary gearbox

Current Analysis Using a Modulation Signal Bispectrum for Machining Status Monitoring

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