Bearing Fault Diagnosis Based on Optimized Feature Mode Decomposition and Improved Deep Belief Network

被引：3

作者：

Jia G. ^{[1
]}

Meng Y. ^{[1
]}

Qin Z. ^{[1
]}

机构：

[1] School of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang

来源：

SDHM Structural Durability and Health Monitoring | 2024年 / 18卷 / 04期

关键词：

deep belief network; fault diagnosis; feature mode decomposition; Rolling bearing; whale optimization algorithm;

D O I：

10.32604/sdhm.2024.049298

中图分类号：

学科分类号：

摘要：

The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is crucial in the application of deep belief networks. The improved deep belief networks are proposed based on Hard swish activation function and the improved whale optimization algorithm. The optimized diagnosis model is applied to weak fault diagnosis of bearings. The experimental results show that this method has faster convergence speed and anti-noise performance under the interference of −5 db noise, and effectively improves the adaptive feature extraction ability of the model and the accuracy of fault diagnosis. Under different conditions, the average accuracy reached 97.8% and 97.6%, with good generalization performance. © 2024 Tech Science Press. All rights reserved.

引用

页码：445 / 463

页数：18

共 32 条

[1]

Wei Y., Li Y. Q., Xu M. Q., Huang W. H., A review of early fault diagnosis approaches and their applications in rotating machinery, Entropy, 21, 4, (2019)

[2]

Jiang H. K., Li C. J., Li H. X., An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis, Mechanical Systems and Signal Processing, 36, 2, pp. 225-239, (2013)

[3]

Jin Z. Z., He D. Q., Wei Z. X., Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN, Engineering Applications of Artificial Intelligence, 110, 7, (2022)

[4]

Feng Z., Chen X., Wang T., Time-varying demodulation analysis for rolling bearing fault diagnosis under variable speed conditions, Journal of Sound and Vibration, 400, 2, pp. 71-85, (2017)

[5]

Wang J. X., Zhan C. S., Li S. P., Zhao Q. C., Liu J. Q., Adaptive variational mode decomposition based on Archimedes optimization algorithm and its application to bearing fault diagnosis, Measurement, 191, 1-2, (2022)

[6]

Huang N., Shen Z., Long S., The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454, 1971, pp. 903-995, (1998)

[7]

Li H. G., Hu Y., Li F. C., Meng G., Succinct and fast empirical mode decomposition, Mechanical Systems and Signal Processing, 85, 3, pp. 879-895, (2017)

[8]

Zhang K., Xu Y. G., Chen P., Feature extraction by enhanced analytical mode decomposition based on order statistics filter, Measurement, 173, 1, (2021)

[9]

Lei Y. G., He Z. J., Zi Y. Y., Application of the EEMD method to rotor fault diagnosis of rotating machinery, Mechanical Systems and Signal Processing, 23, 4, pp. 1327-1338, (2009)

[10]

Liu F., Gao J., Liu H., The feature extraction and diagnosis of rolling bearing based on CEEMD and LDWPSO-PNN, IEEE Access, 8, pp. 19810-19819, (2020)

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