Abstract:To deal with the problem of small-sample modeling in equipment condition on-line monitoring,an on-line monitoring method based on dynamic regression extreme learning machine(DR-ELM) is proposed.Condition data of mechanical equipment are used to train a prediction model based on DR-ELM.In an iterative manner,the latest condition data are adopted and the oldest condition data are abandoned,to achieve the DR-ELM prediction model training on-line.Thus, the current condition of mechanical equipment can be effectively predicted by the method.Simulation on chaotic time series prediction and fan condition monitoring based on time series prediction indicate that the method has better performance in training computational cost and prediction accuracy in comparison with conventional condition monitoring methods based on extreme learning machine(ELM) and on-line sequential extreme learning machine(OS-ELM).
张弦, 王宏力. 适用于小子样时间序列预测的动态回归极端学习机[J]. 信息与控制, 2011, 40(5): 704-709.
ZHANG Xian, WANG Hongli. Dynamic Regression Extreme Learning Machine and Its Application to Small-sample Time Series Prediction. Information and control, 2011, 40(5): 704-709.
Huang G B,Chen L.Enhanced random search based incremental extreme learning machine[J].Neurocomputing,2008,71(16/17/18):3460-3468.
[15]
Liu N,Wang H.Ensemble based extreme learning machine[J].IEEE Signal Processing Letters,2010,17(8):754-757.
[4]
Zhao F G,Chen J,Guo L,et al.Neuro-fuzzy based condition prediction of bearing health[J].Journal of Vibration and Control,2009,15(7):1079-1091.
[17]
Lan Y,Soh C Y,Huang G B.Two-stage extreme learning machine for regression[J].Neurocomputing,2010,73(16/17/18):3028-3038.
[2]
Tran V T,Yang B S,Tan A C C.Multi-step ahead direct prediction for the machine condition prognosis using regression trees and neuro-fuzzy systems[J].Expert Systems with Applications,2009,36(5):9378-9387.
[6]
Liu Z,Zou S Y,Liu S Q,et al.Condition prediction of hydroelectric generating unit based on immune optimized RBFNN[M]//Lecture Notes in Computer Science:vol.5263.Berlin,Germany:Springer,2008:864-872.
[19]
张贤达.矩阵分析与应用[M].北京:清华大学出版社,2005.Zhang X D.Matrix analysis and applications[M].Beijing:Tsinghua University Press,2005.
[3]
Feng F Z,Zhu D D,Jiang P C,et al.GA-EMD-SVR condition prediction for a certain diesel engine[C]//Proceedings of 2010 Prognostics and System Health Management Conference.Piscataway,NJ,USA:IEEE,2010:1-8.
[7]
陈果.用结构自适应神经网络预测航空发动机性能趋势[J].航空学报,2007,28(3):535-539.Chen G.Forecasting engine performance trend by using structure self-adaptive neural network[J].Acta Aeronautica et Astronautica Sinica,2007,28(3):535-539.
[13]
Feng G,Huang G B,Lin Q P,et al.Error minimized extreme learning machine with growth of hidden nodes and incremental learning[J].IEEE Transactions on Neural Networks,2009,20(8):1352-1357.
李德刚.设备预知维护的体系理论及支撑技术研究[D].长沙:湖南大学,2006.Li D G.Research on the system theory and support technology of plant predictive maintenance[D].Changsha:Hunan University,2006.
[8]
Huang G B,Zhu Q Y,Siew C K.Extreme learning machine:Theory and applications[J].Neurocomputing,2006,70(1/2/3):489-501.
[9]
Huang G B,Liang N Y,Rong H J,et al.On-line sequential extreme learning machine[C]//Proceedings of the IASTED International Conference on Computational Intelligence.Anaheim,CA,USA:Acta Press,2005:4-6.
[10]
Liang N Y,Huang G B,Saratchandran N,et al.A fast and accurate on-line sequential learning algorithm for feedforward networks[J] IEEE Transactions on Neural Networks,2006,17(6):1411-1423.
[12]
Chen L,Zhou L F,Pung H K.No-reference image quality assessment using modified extreme learning machine classifier[J].Applied Soft Computing,2009,9(2):541-552.
[1]
Caesarendra W,Niu G,Yang B S.Machine condition prognosis based on sequential Monte Carlo method[J].Expert Systems with Applications,2010,37(3):2412-2420.
[5]
Yan G H,Zhu Y S.Application research of local support vector machines in condition trend prediction of reactor coolant pump[M]//Advances in Soft Computing:vol.116.Berlin,Germany:Springer,2009:35-43.
[18]
Malathi V,Marimuthu N S,Baskar S.Intelligent approaches using support vector machine and extreme learning machine for transmission line protection[J].Neurocomputing,2010,73(10/11/12):2160-2167.
[16]
Lan Y,Soh C Y,Huang G B.Constructive hidden nodes selection of extreme learning machine for regression[J].Neurocomputing,2010,73(16/17/18):3191-3199.