Gear fault trend prediction based on FGM(1, 1) model

Gears have the highest rate of occurrence of failures in the wind turbine components. Therefore, strengthening the judgment of the gear running state in the future and forecasting its developing trend is of great importance. The required data of grey forecasting model is less and the prediction precision is high. Besides the method is simple, we can more accurately describe the inherent law of practical problems. However, it has been found that the actual phenomena tend to be irregular in practice. People usually use the fractional order to replace the integer order. This paper adopts the accumulated generating operation to weaken the original sequence randomness, which makes the disturbances of solutions of grey forecasting model smaller, and fractional order FGM(1,1) model is established to predict the trend of gear fault. The results of GM(1,1) model, Grey Neural Network model and fractional order FGM(1,1) model are compared respectively, and we analyze the differences of fitting precision between the three kinds of models mentioned above. The results illustrate that fractional order FGM(1,1) model has a high prediction capability compared with GM(1,1) model and Grey Neural Network model.

[1]  Jakob Stoustrup,et al.  Active and passive fault-tolerant LPV control of wind turbines , 2010, Proceedings of the 2010 American Control Conference.

[2]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[3]  Min Liu,et al.  Gear Remaining Useful Life Prediction Based on Grey Neural Network , 2015 .

[4]  Changjun Zhu,et al.  Prediction of Groundwater Quality Using Organic Grey Neural Network Model , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[5]  J. Singh,et al.  Application of Grey System Theory in the Development of a Runoff Prediction Model , 2005 .

[6]  Kathryn E. Johnson,et al.  Wind turbine fault detection and fault tolerant control - An enhanced benchmark challenge , 2013, 2013 American Control Conference.

[7]  Keisuke Hanaki,et al.  Using grey system theory to evaluate transportation effects on air quality trends in Japan , 2007 .

[8]  Okyay Kaynak,et al.  Grey system theory-based models in time series prediction , 2010, Expert Syst. Appl..

[9]  Kin Keung Lai,et al.  An improved grey neural network model for predicting transportation disruptions , 2016, Expert Syst. Appl..

[10]  Dingyu Xue,et al.  Continuous fractional-order grey model and electricity prediction research based on the observation error feedback , 2016 .

[11]  Zhou Xiu-jie.  Prediction for air pollution index based on organicgrey neural network model , 2004 .

[12]  Leon Cohen,et al.  What is a multicomponent signal? , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  Migdat Hodzic,et al.  Grey Predictor Reference Model For Assisting Particle Swarm Optimization for Wind Turbine Control , 2016 .

[14]  Sifeng Liu,et al.  Grey system model with the fractional order accumulation , 2013, Commun. Nonlinear Sci. Numer. Simul..

[15]  Zhigeng Fang,et al.  Properties of the GM(1, 1) with fractional order accumulation , 2015, Appl. Math. Comput..

[16]  Liu Si-feng,et al.  The GM models that x(n) be taken as initial value , 2004 .

[17]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[18]  Yi Lin,et al.  Grey Systems: Theory and Applications , 2010 .