Early fault diagnosis of common rail injectors is essential to reduce diesel engine testing and maintenance costs. Therefore, this paper proposes a new common rail injector early fault diagnosis method, which combines the Maximum Second-order Cyclostationary Blind Deconvolution (CYCBD) optimized by the Seagull Optimization Algorithm (SOA) and Hierarchical Fluctuation Dispersion Entropy (HFDE). First, we use SOA adaptively to seek the optimal filter length of CYCBD and use the optimal CYCBD to filter the fuel pressure signal of the high-pressure fuel pipe. Then, in order to make up for the shortcomings of Multi-scale Fluctuation Dispersion Entropy (MFDE) ignoring high-frequency component information, this paper proposes HFDE to extract the fault characteristics after filtering. Finally, we input the fault characteristics into Least Squares Support Vector Machines (LSSVM) for classification and recognition. Through the analysis of experimental data, the method proposed in this paper can effectively identify the early failure state of the common rail injector. Compared with the existing methods, the proposed method has a higher fault recognition rate.

共轨喷油器的早期故障诊断对于降低柴油发动机的测试和维护成本至关重要。因此，本文提出了一种新的共轨喷油器早期故障诊断方法，该方法结合了由海鸥优化算法（SOA）和分层波动色散熵（HFDE）优化的最大二阶循环平稳盲反褶积（CYCBD）。首先，我们自适应地使用SOA来寻找CYCBD的最佳过滤长度，并使用最佳CYCBD来过滤高压燃油管的燃油压力信号。然后，为了弥补多尺度波动色散熵（MFDE）忽略高频成分信息的缺点，提出了HFDE滤波后提取故障特征的方法。最后，我们将故障特征输入到最小二乘支持向量机（LSSVM）中进行分类和识别。通过对实验数据的分析，本文提出的方法可以有效地识别共轨喷油器的早期失效状态。与现有方法相比，该方法具有较高的故障识别率。