小波包分解在氢发动机异常燃烧诊断中的应用  

Application of Wavelet Packet Decomposing on Abnormal Combustion Diagnoses of Hydrogen-fueled Engine

作　　者：王丽君[1] 杨振中[2] 田光旭[2] 葛临东[1] 

机构地区：[1]解放军信息工程大学,河南郑州450000 [2]华北水利水电学院,河南郑州450011

出　　处：《车用发动机》2007年第6期80-82,86,共4页Vehicle Engine

基　　金：美国GM中国科学研究基金(国家自然科学基金50322262);清华大学汽车安全与节能国家重点实验室开放基金(KF2006-01);河南省科技计划项目(0524260028);河南省高等学校青年骨干教师资助计划项目(教高[2007]335号-88);郑州市科技攻关项目(郑科计[2007]8号-3-3)资助

摘　　要：氢发动机燃烧压力信号包含了丰富的燃烧信息,基于压力信号可以应用小波变换法提取异常燃烧信息。鉴于小波包分解继承了小波变换所有的时频局部化优点,并且可以有效地提取微弱燃烧信息,从而能够为信号提供更精细的分析方法。对氢发动机正常燃烧和异常燃烧压力信号进行了小波包分解,提取出小波包能量。通过构造小波包能量特征向量,对氢发动机异常燃烧进行了有效诊断,为消除氢发动机的异常燃烧提供了技术基础。The pressure signals of hydrogen-fueled engines contain abundant combustion information that can be extracted by wavelet transform. Wavelet packet has not only the same merits as wavelet transform but also advantages that are able to analyze weak combustion signals over wavelet transform. Thus it can bring more detail information for signal analysis. The pressure signals of normal and abnormal combustion were decomposed by wavelet packet and the wavelet packet energy was extracted. By constructing the eigenvector of wavelet packet energy, the abnormal combustion of hydrogen-fueled engine was diagnosed effectively. It was a technical basis to eliminate the abnormal combustion.

关 键 词：氢发动机 异常燃烧 小波包能量 故障诊断 

分 类 号：TK91[动力工程及工程热物理]