Real-Time Electrostatic Monitoring of Wear Debris for Wind Turbine Gearbox

Engineering practice has shown that early faults of gearboxes are a leading maintenance cost driver that can easily lower the profit from a wind turbine operation.A novel oil-lubricated electrostatic monitoring of wear debris for a wind turbine gearbox is presented.The continuous wavelet transform(CWT)is used to eliminate the noises of the original electrostatic signal.The kurtosis and root mean square(RMS)values of the time domain signal are extracted as the characteristic parameters to reflect the deterioration of the gearbox.The overall tendency of electrostatic signals in accelerated life test is analyzed.In the eighth cycle,the abnormal wear in the wind turbine gearbox is detected by electrostatic monitoring.A comparison with the popular MetalScan monitoring is given to illustrate the effectiveness of the electrostatic monitoring method.The results demonstrate that the electrostatic monitoring method can detect the fault accurately.

Reliability Analysis of Wind Turbine Gearbox Based on the Optimal Confidence Limit Method

Based on the zero-failure data of 30 Chinese 1. 5 MW wind turbine gearboxes( WTGs),the optimal confidence limit method was developed to predict the reliability and reliability lifetime of WTG. Firstly,Bayesian method and classical probability estimation method were introduced to estimate the value interval of shape parameter considering the engineering practice. Secondly,taking this value interval into the optimal confidence limit method,the reliability and reliability lifetime of WTG could be obtained under different confidence levels. Finally,the results of optimal confidence limit method and Bayesian method were compared. And the comparison results show that the rationality of this estimated range.Meantime, the rule of confidence level selection in the optimal confidence limit method is provided, and the reliability and reliability lifetime prediction of WTG can be acquired.

Reliability Copula Model for Wind Turbine Gearbox Based on Failure Correlation

On the basis of each gear's failure correlation, the reliability Copula model of a wind turbine gearbox is established and a 1.5 MW wind turbine gearbox is taken as the research object. Firstly, based on the dynamic reliability model of mechanical parts, each gear's life distribution function of a wind turbine gearbox is obtained.The life distribution function can be used as the marginal distributions of the system's joint distribution. Secondly,Copula function is introduced to describe the failure correlation between parts, and the appropriate Copula function is selected according to the shape characters of Copula probability density function. Finally, the wind turbine gearbox system is divided into three parts according to the failure correlation of each gear. The Sklar theorem and the thought of step by step analysis are used to obtain the reliability Copula model for a wind turbine gearbox based on failure correlation.

A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

The planetary gearbox is a critical part of wind turbines,and has great significance for their safety and reliability.Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data.However,the data collected from the diagnosed devices are always unlabeled,and the acquisition of fault data from real gearboxes is time-consuming and laborious.As some gearbox faults can be conveniently simulated by a relatively precise dynamic model,the data from dynamic simulation containing some features are related to those from the actual machines.As a potential tool,transfer learning adapts a network trained in a source domain to its application in a target domain.Therefore,a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes.In the method,a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal,while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification.Various groups of transfer diagnosis experiments of planetary gearboxes are carried out,and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.