CAJ | 학술논문

针对汽车发动机气门间隙异常故障，探讨了应用小波分解后求各尺度小波系数信息熵，和RBF神经网络对发动机进行不解体故障诊断的方法。由此，对某汽油发动机进行了故障模拟试验，分别在正常工况和三种故障工况下测取了缸盖表面振动信号。对所采集信号进行Stein无偏估计消噪处理，利用小波系数信息熵提取特征向量，进行归一化处理，然后用RBF神经网络对处理后的振动信号进行分类识别。发动机气门间隙故障的诊断实例表明，在不同工况下利用小波系数信息熵提取故障特征向量、进行基于RBF神经网络的故障诊断方法现实可行，对实现发动机不解体故障诊断具有一定的应用价值。
침대기차발동궤기문간극이상고장，탐토료응용소파분해후구각척도소파계수신식적，화RBF신경망락대발동궤진행불해체고장진단적방법。유차，대모기유발동궤진행료고장모의시험，분별재정상공황화삼충고장공황하측취료항개표면진동신호。대소채집신호진행Stein무편고계소조처리，이용소파계수신식적제취특정향량，진행귀일화처리，연후용RBF신경망락대처리후적진동신호진행분류식별。발동궤기문간극고장적진단실례표명，재불동공황하이용소파계수신식적제취고장특정향량、진행기우RBF신경망락적고장진단방법현실가행，대실현발동궤불해체고장진단구유일정적응용개치。
Aiming at valve clearance fault in abnormal automotive engine, a method of engine fault diagnosis without disassembly was studied with the application of information entropy of wavelet coefficients of different scales after wavelet decomposition and RBF neural network. The failure simulation test was performed on a gasoline engine. The vibration sig-nals in cylinder head surface were measured in a normal working condition and three abnormal working conditions respec-tively. The collected signals were processed to eliminate the noise by Stein unbiased estimation. The characteristic vectors were extracted and normalized by using the wavelet coefficient information entropy. Then, the processed vibration signals were classified and identified by means of RBF neural network. The application example of diagnosis of the engine valve clearance faults shows that the proposed method is practical and feasible. It can be used for fault diagnosis of engines with-out disassembly.