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.
[1] 徐玉秀，原培新，杨文平. 基于柯氏熵的汽车发动机状态预测的可行性研究[J]. 振动与冲击，2004, 23(3):101-104.
XU Yu-xiu,YUAN Pei-xin,YANG Wen-ping. Study on condition of automobile engine performance prediction based on Kovmogolov entropy[J]. Journal of vibration and shock, 2004, 23(3):101-104.
[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, 2 (43):140-143.
XIA Fa-feng, JIAO Jin-long, MA Chun-yang, et al.Forecast the miacrohardnesses of the Ni-TIN nanocoatings by AR model[J]. Journal of Functional Materials ,2012, 2(43):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 Zhan-long, LI Xiao-min. 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 Xiao-jun, REN Shu-hong, Zuo Hong-fu, et al. Prediction method of aero-engine life on wing based on LS-SVM algorithm 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 Fa-fa, YANG Yong, Ma Jing-hua, at 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 Bao-ping,QIN Yi, et al. Degradation trend prediction of rolling bearing based on manifold learning and least squares support vector machine, 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 Hou-lin, Wu Xian-fang, 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 Guo-hua,ZHANG Yun-jian,YE Hai-chao. 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] K. Cho, B. van Meri¨enboer, C. Gulcehre,et al. Learning phrase repre-sentations using RNN encoder-decoder for statistical machine translation[C]//In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Asso- ciation for Computational Linguistics, Doha, Qatar,pp. 1724–1734,2014.
[11] Elahe Eskandaria, Arash Ahmadi , Shaghayegh Gomar. Effect
of spike-timing-dependent plasticity on neural assembly com-
puting[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 Peng-Hua, Chai Yi, Xiong Qing-Yu. Quantum gate Elman neural network and its quantized extended gradient back-propagationtraining algorithm. 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.
LI Shi-yong, LI Pan-chi. Quantum computation and quantum optimization algorithms[M].Harbin:Harbin Institute of Technology Press,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.
ZHOU Fu-guo, RAO Guo-qiang, SI Ai-wei, 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,http://ti.arc.nasa.gov/project/prognostics-data-repository, NASA Ames,Moffett Field,CA,2007.
