摘要
柴油机缸盖振动信号中包含着丰富的柴油机工作状态信息,利用缸盖振动信号诊断柴油机工作状态是一种有效方法。针对缸盖振动信号的特点,提出用经验模式分解方法对获取的缸盖振动信号进行分解,选取前3阶模式分量的边际谱、重心频率、重心幅值、偏度以及峭度等构成柴油机工作状态特征向量,基于BP网络对柴油机故障进行分类诊断。经对实测柴油机故障进行诊断表明,正确率达到85%以上,验证了诊断方法的可行性。
It is a more convenient way to use vibration signals for the fault diagnosis of diesel engine since such signals contain a lot of useful information which can reflect the status of the diesel engine. Considering the characteristics of the cylinder head vibration signals, the empirical mode decomposition was used to decompose the signals obtained, the main IMFs of signals were selected to approximately replace the original signals, and their cy, center of gravity amplitude, skewness and kurtosis were used as the feature vec Based on BP neural network, the diesel engine fault Marginal spectrums, gravity frequen- tor of the status of the diesel engine. diagnosis was conducted applying vectors obtained in the method presented in this paper. Diagnostic accuracy rate reached above 85 % , which verified the feasibility of the method
出处
《农机化研究》
北大核心
2013年第6期193-197,共5页
Journal of Agricultural Mechanization Research
基金
国家自然科学基金项目(41101201)
关键词
柴油机
边际谱
故障诊断
BP网络
diesel engine
marginal spectrum
fault diagnosis
BP neural network engineering
