Aiming at the problem that the common rail injector’s early fault characteristics are very weak and susceptible to random noise and other signal interference, this paper proposes a new common rail injector weak fault diagnosis method based on multipoint optimal minimum entropy deconvolution adjusted based on modified grasshopper optimization algorithm optimization algorithm (MGOA-MOMEDA), improved hierarchical dispersion entropy, and least square support vector machine. First, the fault period T is determined using the multipoint kurtosis spectrum. Through the MGOA optimization algorithm, the optimal filter length L of MOMEDA is obtained adaptively, and the optimal performance filter is used for filter processing. Then, improved hierarchical discrete entropy is used to measure the complexity of the filtered fuel pressure signal to extract weak fault features. Finally, the fault feature vector is input into the LS-SVM multi-classifier to realize the weak fault diagnosis and recognition of the common rail injector. Through experimental verification, the proposed method can effectively achieve the weak fault diagnosis of the common rail injector.

针对共轨喷油器的早期故障特性很弱，易受随机噪声和其他信号干扰的影响，提出了一种基于改进的蚱hopper优化调整后的多点最优最小熵反卷积的共轨喷油器弱故障诊断方法。算法优化算法（MGOA-MOMEDA），改进的分层色散熵和最小二乘支持向量机。首先，使用多点峰度谱确定故障时段T。通过MGOA优化算法，自适应地获得MOMEDA的最优滤波器长度L，并将最优性能滤波器用于滤波处理。然后，改进的分层离散熵用于测量滤波后的燃油压力信号的复杂度，以提取弱故障特征。最后，将故障特征向量输入到LS-SVM多分类器中，以实现对共轨喷油器的弱故障诊断和识别。通过实验验证，该方法可以有效地实现共轨喷油器的弱故障诊断。