基于EEMD的发动机失火故障检测

针对发动机失火故障信息难以提取的问题, 提出了一种基于集合经验模态分解(EEMD: Ensemble Empirical Mode Decomposition)的发动机失火故障检测方法。该方法能自适应地将曲轴转速信号分解为若干个本征模态函数(IMF: Intrinsic Mode Function), 确定包含故障信息的IMF, 通过该IMF 幅值的异常波动, 可以较准确地判断发动机发生失火故障的时间。并通过AMESim 建立了发动机仿真模型, 从中采集了3 种情况的曲轴转速信号, 分别利用EEMD 分解并最终检测失火故障。实验结果表明, 该方法能有效提取故障信息, 实现失火故障的离线检测, 并可以作为在线检测的基础。

Misfire Detection of Engine Based on EEMD

Crankshaft speed signals of engine are non-stationary, and it is difficult to extract misfire fault information from them effectively. For this purpose, a misfire detection method of engine based on EEMD (Ensemble Empirical Mode Decomposition) is proposed. The EEMD method can adaptively decompose a crankshaft signal into several IMFs ( Intrinsic Mode Function). The IMF component which contains fault information can be determined. Through observing the abnormal amplitude fluctuations of the IMF, the time range of engine misfire can be apparently estimated. Besides, a simulation model of engine is built by AMESim, and the crankshaft speed signals of three conditions are collected. Then these signals are decomposed by EEMD respectively to detect misfire fault. The results show that this method can effectively extract fault information to accomplish the off-line detection of misfire fault, and it can also be used as the foundation of on-line detection.