Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

Chen, Xihui; Cheng, Gang; Shan, Xianlei; Hu, Xiao; Guo, Qiang; Liu, Houguang

doi:10.1016/j.measurement.2015.05.007

Cited by 128 publications

(59 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…But in actual using process, due to its complex structure, heavy workload, closed and bad operating environment, it is found of highly failure such as wear, ablation and other faults. Therefore, the dynamic characteristics, fault mechanism, fault diagnosis and feature extraction of the planetary gear transmission should be paid more attention [14][15][16].…”

Section: Methods and Application Objectmentioning

confidence: 99%

Rigid-flexible coupling dynamics simulation of planetary gear transmission based on MFBD

Hao¹,

Gao²,

Sun³

et al. 2017

J. vibroeng.

View full text Add to dashboard Cite

This paper deals with the problem of getting dynamic characteristics in complex mechanical multi-body system. Based on the MFBD (Multi-Flexible-Body Dynamics) technology, the rigid-flexible coupling dynamic simulation method is proposed, and then the method is applied to the planetary gear transmission. The results show that the dynamic stress distribution of planetary gear can be obtained to determine the dangerous location, and the dynamic response characteristics are more obvious. Then the simulation model of planetary gear transmission with broken tooth is established, the fault feature extraction in time-frequency domain is carried out using the acceleration signal. In addition, industrial data is also used to validate the effectiveness of the proposed method.

show abstract

Section: Methods and Application Objectmentioning

confidence: 99%

Rigid-flexible coupling dynamics simulation of planetary gear transmission based on MFBD

Hao¹,

Gao²,

Sun³

et al. 2017

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…Therefore, CEEMDAN not only can overcome the modal aliasing, but can also eliminate the incompleteness of EEMD. The reconstructed signal is identical to the original signal [9,10]. The decomposition process of CEEMDAN can be described as follows:…”

Section: Complete Ensemble Empirical Mode Decomposition With Adaptivementioning

confidence: 99%

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

Chen

Liu

2017

ITM Web Conf.

Self Cite

View full text Add to dashboard Cite

Abstract.A method of planetary gear fault diagnosis based on the fuzzy entropy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer perceptron (MLP) neural network is proposed. The vibration signal is decomposed into multiple intrinsic mode functions (IMFs) by CEEMDAN, and the fuzzy entropy that combines the fuzzy function and sample entropy is proposed and used to extract the feature information contained in each IMF. The fuzzy entropies of each IMF are defined as the input of the MLP neural network, and the planetary gear status can be recognized by the output. The experiments prove the proposed method is effective.

show abstract

“…The planetary gear is widely used in all kinds of drive transmissions of large electromechanical equipment, and it is the most critical part of a drive transmission system. A fault in a planetary gear will affect the reliability of the equipment, and even causes accidents [1]. Therefore, to study planetary gear fault diagnosis has great significance.…”

Section: Introductionmentioning

confidence: 99%

Research of Planetary Gear Fault Diagnosis Based on Multi-Scale Fractal Box Dimension of CEEMD and ELM

Chen¹,

Li²,

Li³

2017

SV-JME

Self Cite

View full text Add to dashboard Cite

The planetary gear is the most critical part of a drive transmission system, and its faults will affect the reliability of the equipment, and even cause accidents. Therefore, it is of great significance to study the fault diagnosis of the planetary gear. A method of planetary gear fault diagnosis based on the multi-scale fractal box dimension of complementary ensemble empirical mode decomposition (CEEMD) and extreme learning machine (ELM) is proposed. The original vibration signal is decomposed by CEEMD, and a series of intrinsic mode functions (IMFs) are obtained. Some effective IMFs are extracted, and their reconstructed signal associated with the feature information is obtained. The reconstructed signal is analysed with multi-scale analysis, and the fault feature information contained in the signals with different scales is quantified and extracted via a fractal box dimension. The status recognition of planetary gear is achieved by combining ELM. The experiments show that the proposed method is effective at diagnosing planetary gear faults. Highlights • A method of planetary gear fault diagnosis based on the multi-scale fractal box dimensions of CEEMD and ELM is proposed.• The effective IMFs which associate with the shock feature information are selected by the kurtosis criterion.• The multi-scale fractal box dimension of the reconstructed signal is defined as the fault feature information.• The status recognition of a planetary gear is achieved by combining ELM.

show abstract

Research of weak fault feature information extraction of planetary gear based on ensemble empirical mode decomposition and adaptive stochastic resonance

Cited by 128 publications

References 18 publications

Rigid-flexible coupling dynamics simulation of planetary gear transmission based on MFBD

Rigid-flexible coupling dynamics simulation of planetary gear transmission based on MFBD

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

Research of Planetary Gear Fault Diagnosis Based on Multi-Scale Fractal Box Dimension of CEEMD and ELM

Contact Info

Product

Resources

About