基于多源信息融合的柴油机典型故障诊断方法OA北大核心

A fault diagnosis method based on t-distributed stochastic neighbor embedding(t-SNE)and multi-source information fusion was proposed because it is difficult to distinguish the faults of different components of diesel engines based on a single vibration signal.The fault simulation model of the diesel engine was calibrated through experiments.Based on the simulation model,the thermal parameters and cylinder head vibration under different fault conditions were obtained.Thermal parameters with low correlation were selected,and the time domain and frequency domain characteristic parameters of the vibration signal were extracted.The vibration characteristic parameters and thermal parameters were fused and dimensionally reduced using t-SNE.The data after dimensional reduction was classified and recognized based on the support vector machine(SVM)method to construct a fault diagnosis model for the diesel engine,and a fault recognition accuracy of 95.7%was finally achieved.Compared with the fault diagnosis method based on a single vibration signal,multi-source information fusion can effectively distinguish different fault categories and improve the fault recognition accuracies of the diesel engine.