基于量子加权门限重复单元神经网络的性态退化趋势预测
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摘要
提出基于量子加权门限重复单元神经网络(Quantum Weight Gated Recurrent Unit Neural Network,QWGRUNN)的旋转机械性态退化趋势预测方法。采用小波降噪-排列熵法构建性态退化指标集,将该指标集输入QWGRUNN完成旋转机械性态退化趋势预测。QWGRUNN在门限重复单元(Gated Recurrent Unit,GRU)基础上引入量子位来表示网络权值和活性值并构造量子相移门以实现权值量子位和活性值量子位的更新,改善了网络泛化能力,进而提高了所提出的性态退化趋势预测方法的预测精度;采用与自身结构相适应的动态学习参数,改善了网络收敛速度,进而提高了所提出的预测方法的计算效率。滚动轴承性态退化趋势预测实例验证了该方法的有效性。
A novel performance degradation trend prediction method of rotating machinery was proposed based on the quantum weighted gated recurrent unit neural network( QWGRUNN). Firstly,the performance degradation index set for rotating machinery was constructed by using the wavelet denoise-permutation entropy method. Then,this index set was input in to QWGRUNN to accomplish the performance degradation trend prediction of rotating machinery. On the basis of gated recurrent unit( GRU),qubits were introduced in QWGRUNN to represent network weights and activity values,quantum phase-shift gates were constructed to update weight-qubits and activity-qubits, and improve the network generalization capacity and the performance degradation trend prediction accuracy of the proposed method. Finally,the dynamic learning parameter appropriate to the structure of QWGRUNN was adopted to improve the network convergence speed and the computation efficiency of the proposed method. The example of performance degradation trend prediction for rolling bearing verified the effectiveness of the proposed method.
A novel performance degradation trend prediction method of rotating machinery was proposed based on the quantum weighted gated recurrent unit neural network( QWGRUNN). Firstly,the performance degradation index set for rotating machinery was constructed by using the wavelet denoise-permutation entropy method. Then,this index set was input in to QWGRUNN to accomplish the performance degradation trend prediction of rotating machinery. On the basis of gated recurrent unit( GRU),qubits were introduced in QWGRUNN to represent network weights and activity values,quantum phase-shift gates were constructed to update weight-qubits and activity-qubits, and improve the network generalization capacity and the performance degradation trend prediction accuracy of the proposed method. Finally,the dynamic learning parameter appropriate to the structure of QWGRUNN was adopted to improve the network convergence speed and the computation efficiency of the proposed method. The example of performance degradation trend prediction for rolling bearing verified the effectiveness of the proposed method.
引文
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[2]夏法锋,焦金龙,马春阳,等.基于AR模型的NI-TIN纳米镀层显微硬度预测研究[J].功能材料,2012,43(2):140-143.XIA Fafeng,JIAO Jinlong,MA Chunyang,et al.Forecast the miacrohardnesses of the NI-TIN nanocoatings by AR model[J].Journal of Functional Materials,2012,43(2):140-143.
[3]杜占龙,李小民.基于多渐消因子强跟踪UKF和约束AR模型的故障估计与预测[J].控制与决策,2014,29(9):1667-1672.DU Zhanlong,LI Xiaomin.Fault estimation and prediction based on multiple fading factors strongtracking UKF and constrained AR model[J].Control and Decision,2014,29(9):1667-1672.
[4]马小骏,任淑红,左洪福,等.基于LS-SVM算法和性能可靠性的航空发动机在翼寿命预测方法[J].交通运输工程学报,2015,15(3):92-99.MA Xiaojun,REN Shuhong,ZUO Hongfu,et al.Prediction method of aero-engine life on wing based on LS-SVMalgorithm and performance reliability[J].Journal of Traffic and Transportation Engineering,2015,15(3):92-99.
[5]陈法法,杨勇,马婧华,等.信息熵与优化LS-SVM的轴承性能退化模糊粒化预测[J].仪器仪表学报,2016,37(4):779-787.CHEN Fafa,YANG Yong,MA Jinghua,et al.Fuzzy granulation prediction for bearing performance degradation based on information entropy and optimized LS-SVM[J].Chinese Journal of Scientific Instrument,2016,37(4):779-787.
[6]肖婷,汤宝平,秦毅,等.基于流形学习和最小二乘支持向量机的滚动轴承退化趋势预测[J].振动与冲击,2015,34(9):149-153.XIAO Ting,TANG Baoping,QIN Yi,et al.Degradation trend prediction of rolling bearing based on manifold learning and least squares support vector machine[J].Journal of Vibration and Shock,2015,34(9):149-153.
[7]刘厚林,吴贤芳,王勇,等.基于BP神经网络的离心泵关死点功率预测[J].农业工程学报,2012,28(11):45-49.LIU Houlin,WU Xianfang,WANG Yong,et al.Power prediction for centrifugal pumps at shut off condition based on BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2012,28(11):45-49.
[8]秦国华,张运建,叶海潮.基于神经网络的薄壁零件加工变形预测方法[J].兵工学报,2013,34(7):840-845.QIN Guohua,ZHANG Yunjian,YE Haichao.A neural network-based prediction method of machining deformation for thin-walled workpiece[J].Acta Armamentarii,2013,34(7):840-845.
[9]GUO Liang,LI Naipeng,JIA Feng,et al.A recurrent neural network based health indicator for remaining useful life prediction of bearings[J].Neurocomputing,2017,240:98-109.
[10]CHO K,VAN MERENBOER B,GULCEHRE C,et al.Learning phrase repre-sentations using RNN ecoder-decoder for statistical machine translation[C]∥Proceedings of the2014 Conference on Empirical Methods in Natural Language Processing,Asso-ciation for Computational Linguistics.Doha,2014.
[11]ESKANDARIA E,AHMADI A,GOMAR S.Effect of spiketiming-dependent plasticity on neural assembly computing[J].Neurocomputing,2016,191:107-116.
[12]李鹏华,柴毅,熊庆宇.量子门Elman神经网络及其梯度扩展的量子反向传播学习算法[J].自动化学报,2013,39(9):1511-1522.LI Penghua,CHAI Yi,XIONG Qingyu.Quantum gate Elman neural network and its quantized extended gradient backpropagation training algorithm[J].Acta Automatica Sinica,2013,39(9):1511-1522.
[13]李士勇,李盼池.量子计算与量子优化算法[M].哈尔滨:哈尔滨工业大学出版社,2009.
[14]冯辅周,饶国强,司爱威,等.排列熵算法的应用与发展[J].装甲兵工程学院学报,2012,26(2):34-38.FENG Fuzhou,RAO Guoqiang,SI Aiwei,et al.Application and development of permutation entropy algorithm[J].Journal of Academy of Armored Force Engineering,2012,26(2):34-38.
[15]LEE J,QIU H,YU G,et al.R exnord technical services,“bearing data set”,IMS,university of cincinnati,NASA ames prognostics datarepository[J/OL].http://ti.arc.nasa.gov/project/prognostics-data-repository,NASA Ames,Moffett Field,CA,2007.
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