个人信息Personal Information
教授
博士生导师
硕士生导师
性别:女
毕业院校:英国Wolverhampton大学
学位:博士
所在单位:机械工程学院
学科:机械设计及理论. 机械制造及其自动化
办公地点:机械大方楼9017
电子邮箱:jiangwei@dlut.edu.cn
A dynamic reliability assessment methodology of gear transmission system of wind turbine
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论文类型:期刊论文
发表时间:2020-08-19
发表刊物:ENGINEERING COMPUTATIONS
收录刊物:SCIE
卷号:37
期号:8
页面范围:2685-2710
ISSN号:0264-4401
关键字:Wind turbine; Dynamic reliability; Failure dependency; Gear transmission; Random errors
摘要:Purpose
The purpose of this paper is to present a new methodology, used for dynamic reliability analysis of a gear transmission system (GTS) of wind turbine (WT), which could be used for assembly decision-making of the parts with errors to improve the GTS's performance.
Design/methodology/approach
This paper involves the dynamic and dynamic reliability analysis of a GTS. The history curves of dynamic responses of the parts are obtained with the developed gear-bearing coupling dynamic model considering the random errors, failure dependency and random load. Then, the surrogate models of the mean and standard deviation of responses are presented by statistics, rain flow counting method and corrected-partial least squares regression response surface method. Further, a novel dynamic reliability model based on the maximum extreme theory, a theory of sequential statistics, equivalent principles and the inverse transform theory of random variable sampling, is developed to overcome the limitations of traditional methods.
Findings
The dynamic reliability of GTS considering the different impact factors are evaluated. The proposed reliability methodology not only overcomes the limitations associated with traditional approaches but also provides good guidance to assembly the parts in a GTS to its best performance.
Originality/value
Instead of constant errors, this paper considers the randomness of the impact factors to develop the dynamic reliability model. Further, instead of the limitation of the normal distribution of the random parameters in the traditional method, the proposed methodology can deal with the problems with non-normal distribution parameters, which is more suitable for the real engineering problems.