Intelligent Fault Diagnosis Method Of Common Rail Injector Based On Composite Hierarchical Dispersion Entropy And Improved Least Squares Support Vector Machine

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. (C) 2021 Elsevier Inc. All rights reserved.