SSA优化深度双向门控循环单元网络的轴承性能退化趋势预测

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (20) : 12-18.

论文

陈仁祥1，陈国瑞1，徐向阳1，胡小林2，张雁峰1

作者信息 +

Bearing performance degradation trend prediction sparrow search algorithm optimization bidirectional gating cycle unit

CHEN Renxiang1,CHEN Guorui1,XU Xiangyang1,HU Xiaolin2,ZHANG Yanfeng1

Author information +

文章历史 +

摘要

为在非经验指导下获取双向门控循环单元网络中最优隐藏层单元数，实现滚动轴承性能退化趋势预测，提出基于麻雀搜索算法优化深度双向门控循环单元的轴承性能退化趋势预测方法。首先，在正向门控循环单元网络基础上，增加反向门控循环单元网络，以构建深度双向门控循环单元预测网络；然后将预测值与真实值的均方误差作为适应度值，根据麻雀发现者和捕食者进行参数更新，经优化后获得最优隐藏层单元参数下的深度双向门控循环单元网络预测模型；最后，通过全连接层实现性能退化趋势预测。在公共数据集与实测数据集上进行实验验证，验证了所提方法的有效性与可行性。

Abstract

In order to obtain the optimal number of hidden layer elements in bidirectional gated cyclic element network under non-empirical guidance and realize the performance degradation trend prediction of rolling bearings, a bearing performance degradation trend prediction method was proposed based on sparrow search algorithm to optimize the depth bidirectional gated cyclic element. Firstly, the forward gating loop unit network is added to the reverse gating loop unit network to construct the deep bi-directional gating loop unit prediction network. Then, the mean square error between the predicted value and the true value was used as the fitness value, and the parameters were updated according to the sparrows finder and predator. After training, the depth bidirectional gated cyclic element network with the optimal hidden layer element parameters was obtained. Finally, the performance degradation trend is predicted by the full connection layer. The validity and feasibility of the proposed method are verified by experiments on public and measured data sets.

导出引用

陈仁祥1，陈国瑞1，徐向阳1，胡小林2，张雁峰1. SSA优化深度双向门控循环单元网络的轴承性能退化趋势预测[J]. 振动与冲击, 2023, 42(20): 12-18

CHEN Renxiang1,CHEN Guorui1,XU Xiangyang1,HU Xiaolin2,ZHANG Yanfeng1. Bearing performance degradation trend prediction sparrow search algorithm optimization bidirectional gating cycle unit[J]. Journal of Vibration and Shock, 2023, 42(20): 12-18

参考文献

[1] GAO S, ZHANG S, ZHANG Y, et al. Operational reliability evaluation and prediction of rolling bearing based on isometric mapping and NoCuSa-LSSVM[J]. Reliability Engineering System Safety, 2020, 201:106968.
[2] LEI Y, LI N, GUO L, et al. Machinery health prognostics: a systematic review from data acquisition to RUL prediction[J].Mechanical Systems and Signal Processing, 2018, 104(MAY1):799-834.
[3] Qian Y , Yan R , Gao R X . A multi-time scale approach to remaining useful life prediction in rolling bearing[J]. Mechanical Systems & Signal Processing, 2017, 83:549-567.
[4] 彭宇, 刘大同. 数据驱动故障预测和健康管理综述[J]. 仪器仪表学报，2014(03):3-17.
Peng Yu, Liu Datong. Data-driven prognostics and health management: A review of recent advances[J]. Chinese Journal of Scientific Instrument, 2014, 35 ( 3 ):481-495．
[5] 王鹏, 邓蕾, 汤宝平, 等.基于自编码器和门限循环单元神经网络的滚动轴承退化趋势预测[J].振动与冲击, 2020, 39(17):106-111+133.
Wang Peng, Deng Lie, Tang Baoping, et al. Degradation trend prediction of rolling bearing based on auto-encoder and GRU neural network [J].Journal of Vibration and Shock, 2020, 39(17):106-111+133.
[6] HUANG Z, Xu Z , KE X, et al. Remaining useful life prediction for an adaptive skew-Wiener process model[J]. Mechanical Systems & Signal Processing, 2017, 87(PT.A):294-306.
[7] HE M , ZHOU Y , LI Y , et al. Long Short-term Memory Network with Multi-resolution Singular Value Decomposition for Prediction of Bearing Performance Degradation[J]. Measurement, 2020, 156:107582.
[8] 郑小霞, 钱轶群, 王帅. 基于优选小波包与马氏距离的滚动轴承性能退化GRU预测[J]. 振动与冲击, 2020, 39(17):39-46+63.
Zheng Xiaoxia, QIAN Yiqun, Wang Shuai. GRU prediction for performance degradation of rolling bearings based on optimalwavelet packet and Mahalanobis distance [J]. Journal of Vibration and Shock, 2020, 39(17):39-46+63.
[9] 申彦斌, 张小丽, 夏勇, 等. BiLSTM神经网络用于轴承剩余使用寿命预测研究[J]. 振动工程学报, 2021, 34(2):1-10.
Sheng Yanbin, Zhang Xiaoli, Xia Yong, et al. BiLSTM Neural Network For Prediction of Residual Service Life of Bearings[J]. Journal of Vibration Engineering, 2021, 34(2):1-10.
[10] 陈浩, 罗棕, 杜春, 等. 一种基于Bi-GRU的卫星对地观测任务可调度性预测方法[J]. 湖南大学学报:自然科学版, 2021.1-7.
Chen Hao, LUO Zong, Du Chun, et al. A prediction method for schedulability of satellite earth observation task based on Bi-GRU [J]. Journal of Hunan University: Natural Science, 2021.1-7.
[11] 刘弘历, 武森, 魏桂英, 等. 基于深度神经网络的点击率预测模型[J/OL]. 工程科学学报:1-10.
Liu Hongli, Wu Sen, Wei Guiying, et al. Click-through rate prediction model based on a deep neural network [J]. Chinese Journal of Engineering:1-10.
[12] Xue J , Shen B . A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering An Open Access Journal, 2020, 8(1):22-34.
[13] Guo L, Lei Y, Li N , et al. Machinery health indicator construction based on convolutional neural networks considering trend burr[J]. Neurocomputing, 2018, 292(MAY31):142-150.
[14] CHO K, MERRINBOER B V, LCERHRE C, et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. Computer Science2014(6):1-15.
[15] NECTOUX P, GOURIVEA R, MEDJAHER K, et al. PRONOSTIA: an experimental platform for bearings accelerated degradation tests[C] // IEEE International Conference on Prognostics and Health Management. Denver, 2012.1-8.
[16] 赵光权, 刘小勇, 姜泽东, 等. 基于深度学习的轴承健康因子无监督构建方法[J]. 仪器仪表学报, 2018, 039(006):82-88.
Zhao Guangquan, Liu Xiaoyong, Jiang Zedong, et al. Unsupervised health indicator of bearing based on deep learning [J]. Chinese Journal of Scientific Instrument, 2018, 039(006):82-88.