基于GA-BRBPNN的航空自耦变压整流器故障诊断方法  

Fault diagnosis method for auto-transformerrectifier unit based on GA-BRBPNN

作　　者：董慧芬 郑坤 杨占刚 Dong Huifen;Zheng Kun;Yang Zhangang(College of Electronics Information and Automation,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学电子信息与自动化学院,天津300300

出　　处：《电子测量与仪器学报》2022年第9期217-225,共9页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(51377161);中国民航大学实验技术创新基金(2020CXJJ87)项目资助。

摘　　要：航空自耦变压整流器(auto-transformer rectifier unit, ATRU)是飞机高压直流电网关键电能变换装置,在运行过程中受高温、机械应力、荷载波动等因素持续影响,其内部元件可能出现相应故障,进而威胁飞机可靠运行及持续适航。针对ATRU整流部分故障信号频谱难以区分、诊断准确率不高问题,提出一种遗传算法(genetic algorithm, GA)与贝叶斯正则化反向传播神经网络(Bayesian regularisation back propagation neural network, BRBPNN)相结合的故障诊断识别方法。首先,实现ATRU故障仿真,以时频分析方式处理所得信号,从而挖掘不同故障状态的特征信息;随后采用GA算法优化BRBPNN初始权阈值并建立最优GA-BRBNPNN诊断模型,将特征样本输入诊断模型进行故障分类识别,测试模型性能;最后,搭建故障模拟实验平台对实测数据进行模型验证。实验结果分析可知,对于仿真故障,该模型诊断准确率可达99.46%,对于实测故障,该模型可全部诊断识别待测样本;由此表明提出的GA-BRBPNN优化模型诊断效果好,具有较高实用价值。Aeronautical auto-transformer rectifier unit(ATRU) is the key power conversion device of aircraft high-voltage DC power grid. It is continuously affected by high temperature, mechanical stress, load fluctuation and other factors during operation, then its internal components may appear corresponding failure, which can lead to threaten the reliable operation and continued airworthiness of the aircraft. The spectrum of the fault signal in the rectifier part of ATRU is difficult to distinguish and the diagnostic accuracy is low, a fault diagnosis method based on genetic algorithm(GA) combined with Bayesian regularization back propagation neural network(BRBPNN) is proposed. Firstly, an ATRU fault simulation model is implemented and then the collected signals are processed by means of time-frequency analysis so as to mine the feature information of different fault states. Subsequently, genetic algorithm is used to optimize the initial weights and thresholds of BRBPNN and the optimal GA-BRBPNN diagnosis model is established. The feature samples are introduced into the diagnosis model for fault identification and model performance testing. Finally, the experiment platform of fault simulation is built and the measured fault data is used to validate the method. The experimental results show that the diagnostic accuracy of the proposed method can reach 99.46% for the simulated faults and the method can diagnose and identify all the samples to be tested for the actual faults. Therefore, the method based on GA-BRBPNN has good diagnostic effect and high practical value.

关 键 词：航空自耦变压整流器 BP神经网络 遗传算法 贝叶斯正则化 故障诊断 

分 类 号：TM461[电气工程—电器] TN06[电子电信—物理电子学]