[1] 任浩,屈剑锋,柴毅,等.深度学习在故障诊断领域中的研究现状与挑战[J].控制与决策,2017,32(8):1345-1358. (REN H, QU J F, CHAI Y, et al. Deep learning for fault diagnosis:The state of the art and challenge[J]. Control and Decision, 2017, 32(8):1345-1358.) [2] 赵春晖,王福利,姚远,等.基于时段的间歇过程统计建模、在线监测及质量预报[J].自动化学报,2010,36(3):366-374. (ZHAO C H, WANG F L, YAO Y, et al. Phase-based statistical modeling, online monitoring and quality prediction for batch processes[J]. Acta Automatica Sinica, 2010, 36(3):366-374.) [3] HUNG H, WU P, TU I, et al. On multilinear principal component analysis of order-two tensors[J]. Biometrika, 2012, 99(3):569-583. [4] WANG J, HE Q P, QIN S J, et al. Recursive least squares estimation for run-to-run control with metrology delay and its application to STI etch process[J]. IEEE Transactions on Semiconductor Manufacturing, 2005, 18(2):309-319. [5] YU J. Fault detection using principal components-based Gaussian mixture model for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2011, 24(3):432-444. [6] JACKSON J E, MUDHOLKAR G S. Control procedures for residuals associated with principal component analysis[J]. Technometrics, 2012, 21(3):341-349. [7] 王建林,马琳钰,邱科鹏,等.基于SVDD的多时段间歇过程故障检测[J].仪器仪表学报,2017,38(11):2752-2761. (WANG J L, MA L Y, QIU K P, et al. Multi-phase batch processes fault detection based on support vector data description[J]. Chinese Journal of Scientific Instrument, 2017, 38(11):2752-2761.) [8] HE Q P, WANG J. Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2007, 20(4):345-354. [9] GRAVES A. Supervised Sequence Labelling with Recurrent Neural Networks[M]. Berlin:Springer, 2012:37-45. [10] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507. [11] WU S, ZHANG L, ZHENG W, et al. A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents[J]. Journal of Natural Gas Science and Engineering, 2016, 34:139-158. [12] SUN J, XIAO Z, XIE Y. Automatic multi-fault recognition in TFDS based on convolutional neural network[J]. Neurocomputing, 2017, 222:127-136. [13] LU C, WANG -Y, QIN W-L, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130:377-388. [14] de TIM B, VERBERT K, BABUSKA R. Railway track circuit fault diagnosis using recurrent neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(3):523-533. [15] TALEBI N, SADRNIA M A, DARABI A. Robust fault detection of wind energy conversion systems based on dynamic neural networks[J]. Computational Intelligence and Neuroscience, 2014, 4(7):580972 [16] PASCANU R, MIKOLOV T, BENGIO Y. On the difficulty of training recurrent neural networks[C]//Proceedings of the 30th International Conference on Machine Learning:Vol. 28. Atlanta, GA:JMLR, 2013, 28:1310-1318. [17] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning:Vol. 37. Atlanta, GA:JMLR, 2015:448-456. [18] GOODFELLOW I, BENGIO Y, COURVILLE A, et al. Deep learning[M]. Cambridge, UK:MIT Press, 2016:172-187. [19] DUCHI J, HAZAN E, SINGER Y. Adaptive subgradient methods for online learning and stochastic optimization[J]. Journal of Machine Learning Research, 2011, 12:2121-2159. [20] WISE B M, GALLAGHER N B, BUTLER S W, et al. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process[J]. Journal of Chemometrics, 1999, 13(3/4):379-396. [21] 常玉清,王姝,谭帅,等.基于多时段MPCA模型的间歇过程监测方法研究[J].自动化学报,2010,36(9):1312-1320. (CHANG Y Q, WANG S, TAN S, et al. Research on multistage-based MPCA modeling and monitoring method for batch processes[J]. Acta Automatica Sinica, 2010, 36(9):1312-1320.) [22] 陶栋琦,薄翠梅,易辉.基于多时段MPCA的半导体蚀刻过程监测方法[J].传感技术学报,2015,28(6):798-802. (TAO D Q, BO C M, YI H. Semiconductor etch process monitoring based on multi-stage MPCA[J]. Chinese Journal of Sensors and Actuators, 2015, 28(6):798-802.) [23] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks[C]//Proceedings of the 2011 Fourteenth International Conference on Artificial Intelligence and Statistics:Vol. 15. Atlanta, GA:JMLR, 2011:315-323. |