摘要
船舶动力设备因故障监测信号样本少、变化缓慢、数据特征呈非线性,使得设备故障模式的准确识别和状态预测比较难。尤其是柴油机气阀间隙异常的故障诊断,由于柴油机气阀间隙振动信号噪声多,利用SVM对柴油机气阀间隙进行预测时需要进行特征提取。鉴于此,研究了基于小波能量谱分析的SVM柴油机气阀间隙的故障诊断方法,结果表明上述模型具有较高的识别率,能准确预测船舶动力设备当前状态。
Ship power equipment makes fault pattern recognition and state prediction more difficult due to few samples, slow changes and the nonlinear structure of data of fault monitoring signal. Especially for diesel engine valve gap abnormal fault diagnosis, due to the vibration and noise of diesel engine valve gap is much, feature extraction is needed when using SVM to predict the valve gap of diesel engine. In view of this, a fault diagnosis method of SVM diesel engine valve clearance based on wavelet energy spectrum analysis is studied. The results show that the above model has a high recognition rate, and it can accurately predict the current state of marine power equipment.
作者
蒋佳炜
胡以怀
柯赟
陈彦臻
JIANG Jiawei;HU Yihuai;KE Yun;CHEN Yanzhen(Shanghai Maritime University,Shanghai 201306,China)
出处
《机电设备》
2018年第4期58-65,共8页
Mechanical and Electrical Equipment
