Fault Detection System of Automobile Engine Based on Correlation Dimension Feature Extraction

Abstract

According to the high fault rate and the great difficulty of diagnosis for the automobile engine, an automobile engine faults detection system was designed. Because the vibration signal of the engine could reflect the faults types to a great extent, a fault detection method was proposed based on the extraction of the vibration signal correlation dimension. The collected vibration signal which was from different type of automobile engines was processed and analyzed. The correlation dimension was extracted and an improved correlation algorithm was proposed in the system, the computational accuracy was improved, and the standard deviation of the improved algorithm lowers about 50% in comparison with the traditional algorithm, the classification performance is raised variously, the excellent detection performance was showed in the system. The detection result shows that the correlation dimension feature extraction method that this paper proposed can detect and diagnose different types of automobile engine faults such as start subsystem fault, ignition subsystem fault, fuel supply subsystem, etc. The detection conclusion was stable and the simulation result has much great application performance.