Automatic Fault Diagnosis Method for Wind Turbine Generator Systems Driven by Vibration Signals

doi:10.23940/ijpe.18.07.p17.15301541

Abstract

Abstract:

An automatic fault diagnosis method for the wind turbine generator system (WTGS) driven by vibration signal is proposed in this paper. In this method, the vibration signal is used to drive the notch filter network directly, and the frequency selection characteristics of the notch filter are used to extract the fault feature frequency of WTGS components. Then, the extracted fault feature frequency is encoded and a neural network classifier is used to achieve the automatic fault diagnosis of WTGS. In addition, the vibration intensity is calculated to evaluate the fault degree of the WTGS. The innovation of this paper is that the fault feature frequency of the WTGS is derived from parameters of the notch filter rather than the vibration signal itself. The practical on-site application shows the effectiveness of the proposed method, which is of great significance for improving the efficiency of fault diagnosis of WTGS and realizing the batch diagnosis of the fault of WTGS.

Submitted on March 8, 2018; Revised on April 12, 2018; Accepted on May 2, 2018
References: 10

Yu Pang, Limin Jia, Zhan Liu, and Qianyun Gao. Automatic Fault Diagnosis Method for Wind Turbine Generator Systems Driven by Vibration Signals [J]. Int J Performability Eng, 2018, 14(7): 1530-1541.

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References 0

	Z.Chen, C.Li, and R.V.Sánchez , “Multi-layer Neural Network with Deep Belief Network for Gearbox Fault Diagnosis,” Journal of Vibroengineering, vol.17, no.5, pp.2379–2392, 2015
	P.Chen, L.Song, and H.Wang, “Intelligent Condition Diagnosis Method for Rotating Machinery Based on Probability Density and Discriminant Analyses”, IEEE Signal Processing Letters, vol.23, no.8, pp.1111–1115, 2016
	Center for Intelligent Maintenance Systems, 2017, Retrieved from http://www.imscenter.net
	S.Devendiran, C.Rajeswari, and B.Sathiyabhama, “Diagnostics of Gear Faults using Ensemble Empirical Mode Decomposition, Hybrid Binary Bat Algorithm and Machine Learning Algorithms,” Journal of Vibroengineering, vol.17, no.3, pp.1169–1187, 2015
	A.Fasana, L.Garibaldi, and A.Tabrizi, “Early Damage Detection of Roller Bearings using Wavelet Packet Decomposition, Ensemble Empirical Mode Decomposition and Support Vector Machine,” Meccanica, vol.50, no.3, pp.865–874, 2015
	S.H.Kim, S.Y.Kim, and I.H.Ra, “Design of Wind Turbine Fault Detection System Based on Performance Curve,” in SCIS-ISIS, Kobe, Japan, November 20-24, 2012
	B.Kim, J.S.LEE, and J.Y.Park, “Design and Application of Condition Monitoring System for Wind Turbines,” in Proc. of 11th International Conference on Control, Automation and Systems, Gyeoggido, Korea, Oct.26-29, 2011
	Y.Kang, C.C.Liao, and C.C.Wang, “Using Bayesian Networks in Gear Fault Diagnosis,” Applied Mechanics and Materials, vol.43, no.26, pp.284–287, 2013
	C.P.Lim, S.Nahavandi, and M.Seera ,“Condition Monitoring of Induction Motors: A Review and an Application of an Ensemble of Hybrid Intelligent Models,” Expert Systems with Applications, vol.41, no.10, pp.4891–4903, 2014
	Y.Liu and D.T. Yang, “Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion,” Shock and Vibration, pp.1-9, 2016