An early fault diagnosis method of common-rail injector based on improved CYCBD and hierarchical fluctuation dispersion entropy

Abstract

Abstract 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.