The fault diagnosis of the common rail injector is an important means to ensure the safe operation of the diesel engine. In order to quickly and accurately identify the fault status of common rail injectors, this paper proposes an intelligent fault diagnosis method for common rail injectors based on Composite Hierarchical Dispersion Entropy (CHDE) and Improved Grasshopper Optimization Algorithm based Least Squares Support Vector Machine (IGOA-LSSVM). First, in order to avoid the inherent shortcomings of Hierarchical Dispersion Entropy, we calculate CHDE as a characteristic parameter to construct a fault characteristic set. Then, this paper proposes the IGOA-LSSVM multi-classifier for pattern recognition, which has higher recognition accuracy and stability than other classifiers. Finally, we use the proposed method to analyze the common rail injector failure data. The results show that the proposed method can not only effectively realize the common rail injector intelligent fault diagnosis but also has a higher fault recognition rate than existing methods.

共轨喷射器的故障诊断是确保柴油机安全运行的重要手段。为了快速，准确地识别共轨喷油器的故障状态，本文提出了一种基于复合层次离散熵（CHDE）和改进的基于最小二乘支持向量机的蚱hopper优化算法的共轨喷油器智能故障诊断方法。 LSSVM）。首先，为了避免分层色散熵的固有缺点，我们将CHDE计算为特征参数，以构造故障特征集。然后，本文提出了一种用于模式识别的IGOA-LSSVM多分类器，该分类器具有比其他分类器更高的识别精度和稳定性。最后，我们使用提出的方法来分析共轨喷油器故障数据。结果表明，所提出的方法不仅可以有效地实现共轨喷油器的智能故障诊断，而且比现有方法具有更高的故障识别率。