<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based maintenance. Targeting the non-linear dynamic characteristics of faulty signals of rolling element bearings, a bearing performance degradation assessment approach based on improved fuzzy entropy (FuzzyEn) is proposed in this paper. FuzzyEn has less dependence on data length and achieves more freedom of parameter selection and more robustness to noise. However, it neglects the global trend of the signal when calculating similarity degree of two vectors, and thus cannot reflect the running state of the rolling element bearings accurately. Based on this consideration, the algorithm of FuzzyEn is improved in this paper and the improved FuzzyEn is utilized as an indicator for bearing performance degradation evaluation. The vibration data from run-to-failure test of rolling element bearings are used to validate the proposed method. The experimental results demonstrate that, compared with the traditional kurtosis and root mean square, the proposed method can detect the incipient fault in advance and can reflect the whole performance degradation process more clearly.</p></abstract>

Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based maintenance. Targeting the non-linear dynamic characteristics of faulty signals of rolling element bearings, a bearing performance degradation assessment approach based on improved fuzzy entropy (FuzzyEn) is proposed in this paper. FuzzyEn has less dependence on data length and achieves more freedom of parameter selection and more robustness to noise. However, it neglects the global trend of the signal when calculating similarity degree of two vectors, and thus cannot reflect the running state of the rolling element bearings accurately. Based on this consideration, the algorithm of FuzzyEn is improved in this paper and the improved FuzzyEn is utilized as an indicator for bearing performance degradation evaluation. The vibration data from run-to-failure test of rolling element bearings are used to validate the proposed method. The experimental results demonstrate that, compared with the traditional kurtosis and root mean square, the proposed method can detect the incipient fault in advance and can reflect the whole performance degradation process more clearly.

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

University of Shanghai for Science and Technology

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Rolling element bearings are an important unit in the rotating machines, and their performance degradation assessment is the basis of condition-based...

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Publié en ligne: 23 oct. 2017

Pages: 219 - 225

Reçu: 10 avr. 2017

Accepté: 20 sept. 2017

DOI: https://doi.org/10.1515/msr-2017-0026

Mots clés
Performance degradation assessment, improved fuzzy entropy, rolling element bearing

© 2017 Keheng Zhu et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy

Publié en ligne: 23 oct. 2017

Pages: 219 - 225

Reçu: 10 avr. 2017

Accepté: 20 sept. 2017

DOI: https://doi.org/10.1515/msr-2017-0026

Mots clésPerformance degradation assessment, improved fuzzy entropy, rolling element bearing

© 2017 Keheng Zhu et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Performance degradation assessment, improved fuzzy entropy, rolling element bearing