Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Zhu, Keheng; Jiang, Xin; Chen, Liang; Li, Haolin

doi:10.1515/msr-2017-0026

Cited by 15 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4.5−0.13 × log 10 P 10 −15 10 −20 < P ≤ 10 −15 5.8−0.13 × log 10 P 10 −20 10 −30 < P ≤ 10 −20 10 P 10 −30 (13) where E and P denotes the criticality and similarity, respectively.…”

Section: Online Performance Evaluation By Fuzzy Comprehensive Evamentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

Huang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In order to track and grasp the operation situations of the gearboxes, the vertical vibration signals of three different gear fault states, normal, worn and broken teeth, are collected via a gearbox vibration experiment. An online diagnosis and performance evaluation model with hidden Markov model (HMM) and fuzzy comprehensive evaluation is proposed. To address the limitation of maximum membership principle in the case of equal membership or the membership is very close to each other, a closeness evaluation strategy is proposed by defining the likelihood ratio of HMM as a similarity and selecting an combined membership function of the semi-trapezoidal and intermediate-ridge distribution. Results show that the online diagnosis has achieved a good performance with the similarity strategy. Compared with the evaluation strategy of the maximum membership principle, the proposed gearbox performance model with the closeness evaluation strategy is more accurately distinguished from the evaluation results of the broken teeth state and the worn state, especially for the case of the equal membership.

show abstract

Section: Online Performance Evaluation By Fuzzy Comprehensive Evamentioning

confidence: 99%

“…Ren et al [12] proposed a performance evaluation method by manifold space fuzzy k-master curve similarity analysis. Zhu et al [13] proposed a performance degradation method by improved fuzzy entropy. Li et al [14] applied a general mathematical morphology particle as a new indicator for bearing performance degradation evaluation.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

Huang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…1,2 Therefore, to ensure the safety and stability of a mechanical system, the gear performance evaluation has received more and more attention from industry and academia. 3…”

Section: Introductionmentioning

confidence: 99%

Fault feature extraction method of gear based on spectral amplitude modulation and autocorrelation analysis

Zhong

Mei

Xiang

et al. 2022

Noise & Vibration Worldwide

View full text Add to dashboard Cite

Due to the strong noise interference and amplitude modulation effect, it is difficult to extract the impact characteristics of the fault gear from the spectrum of the fault signal. To tackle these issues, a gear fault feature extraction method based on spectral amplitude modulation (SAM) and autocorrelation analysis is proposed. First, the SAM method is used to decompose the signal into different components according to energy, and the optimal component with the most fault information is determined by combining kurtosis index and magnitude order selection range. Then, the optimal component is denoised using the autocorrelation function. Finally, the fault feature frequency is extracted by calculating the squared envelope spectrum of the denoised signal. The superiority of the method is verified by simulating the signal. Furthermore, the effectiveness and superiority of the method in gear fault diagnosis are verified by comparison with the fast kurtogram, cepstrum pre-whitening, and SAM.

show abstract

“…PE is obviously faster than SE, but the amplitude relationship between time series is not taken into account [21]. The feature extraction ability of FuzzyEn is limited by fuzzy membership function [22]. Significantly, DE has the most prominent advantage, its computational efficiency is significantly higher than SE, PE and FuzzyEn, and entropy estimation is stable and effective.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

Dong¹,

zhang²,

Hu³

et al. 2021

Preprint

View full text Add to dashboard Cite

The fault diagnosis of gearbox and bearing in wind turbine is crucial to improve service life and reduce maintenance cost. This paper proposes a novel fault diagnosis method based on refined generalized composite multi-scale state joint entropy (RGCMSJE), robust spectral learning framework for unsupervised feature selection (RSFS) and extreme learning machine (ELM) to identify the different health conditions of gearboxes, including feature extraction, feature reduction and pattern recognition. In this method, MAED is firstly adopted to assist RGCMSJE in parameter selection. Second, RGCMSJE is utilized to extract the multi-scale features of gearbox vibration signal and construct high-dimension feature set. Thirdly, RSFS method is used to reduce the dimension of high-dimensional RGCMSJE feature set. In the end, the obtained low-dimensional features are input to the ELM classifier to realize fault pattern recognition. Through two gearbox fault diagnosis experiments, the effectiveness of the fault diagnosis method is verified. The analysis results show that this method can effectively and accurately identify different fault types of wind turbine gearbox.

show abstract

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Cited by 15 publications

References 24 publications

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

Fault feature extraction method of gear based on spectral amplitude modulation and autocorrelation analysis

Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

Contact Info

Product

Resources

About