液体火箭发动机振动故障特征信号提取方法
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摘要
液体火箭发动机试验是一项高成本、高风险的工程,由于设计缺陷、零件加工误差、工作过程及机械连接结构的影响,导致试验过程发动机大振动现象的发生。基于发动机振动信号分析的状态监测与故障诊断方法是提高发动机可靠性和降低试验成本的重要手段。在给出液体火箭发动机振动故障诊断数学模型的基础上,详细介绍了发动机试验过程中7种特征信号提取方法,即:振幅特征提取、功率谱特征提取、频谱分析(谐频识别和边频识别)、突频特征提取、状态特征提取、小波特征提取和高阶谱特征提取,并结合发动机实际故障诊断方案,给出了发动机特征信号提取算法应用实例,通过发动机热试车验证了液体火箭发动机振动故障诊断的特征信号提取方法的正确性。
The test of liquid rocket engine is a high cost and high risk project,because of the design defects,parts processing errors,working process and mechanical connection structure,which resulting in the large vibration phenomenon during engine testing. The state monitoring and fault diagnosis method based on engine vibration signal analysis is an important means to improve engine reliability and reduce test cost. A mathematical model for vibration fault diagnosis of liquid rocket engine is presented in this paper. On this basis,seven characteristic signal extraction methods in the process of engine test are introduced in detail. They are amplitude feature extraction,power spectrum feature extraction,spectrum analysis( harmonic frequency identification and edge frequency identification),fundamental frequency feature extraction,state feature extraction,wavelet feature extraction and high-order spectral feature extraction. In addition,combined with the actual fault diagnosis scheme of the engine,an application example of the engine characteristic signal extraction algorithm is given,and this method is verified by the engine hot-fire test.
The test of liquid rocket engine is a high cost and high risk project,because of the design defects,parts processing errors,working process and mechanical connection structure,which resulting in the large vibration phenomenon during engine testing. The state monitoring and fault diagnosis method based on engine vibration signal analysis is an important means to improve engine reliability and reduce test cost. A mathematical model for vibration fault diagnosis of liquid rocket engine is presented in this paper. On this basis,seven characteristic signal extraction methods in the process of engine test are introduced in detail. They are amplitude feature extraction,power spectrum feature extraction,spectrum analysis( harmonic frequency identification and edge frequency identification),fundamental frequency feature extraction,state feature extraction,wavelet feature extraction and high-order spectral feature extraction. In addition,combined with the actual fault diagnosis scheme of the engine,an application example of the engine characteristic signal extraction algorithm is given,and this method is verified by the engine hot-fire test.
引文
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[2]张惠军.液体火箭发动机故障检测与诊断技术综述[J].火箭推进,2004,30(5):40-45.ZHANG H J.Study on liquid rocket engine fault detection and diagnostic technology[J].Journal of Rocket Propulsion,2004,30(5):40-45.
[3]张贤达.信号处理[M].北京:清华大学出版社,1995.
[4]闫俊伏,谭兴昀.振动测量参数在故障诊断中的分析[J].山西电力,2008(2):32-33.
[5]朱恒伟.液体推进剂火箭发动机地面试车故障检测与诊断研究「D].长沙:国防科技大学,2003.
[6]申弢,黄树红.振动故障信号奇异性指数的统计特征研究[J].振动工程学报,2003,16(4):476-479.
[7]谢廷峰,刘洪刚,吴建军.液体火箭发动机分布式健康监控系统的分析与设计[J].火箭推进,2004,30(3):7-12.XIE T F,LIU H G,WU J J.Analysis and design of distributed health monitoring system for liquid propellant rocket engine[J].Journal of Rocket Propulsion,2004,30(3):7-12.
[8]钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社,2007.
[9]杨福生.小波变换的工程分析与应用[M].北京:科学出版社,1999.
[10]李志农,丁启全,吴昭同,等.旋转机械典型故障的高阶谱特性分析[J].汽轮机技术,1989,44(1):42-44.
[11]张桂才,史铁林,杨叔子.基于高阶统计量的机械故障特征提取方法研究[J].华中理工大学学报,1999,27(3):628.
[12]王洪杰.齿轮泵的振动信号研究[J].哈尔滨工业大学学报,1996,28(4):17-21.