Research on Real-time Diagnosis and Early Warning Technology of Automobile Faults Based on Fractional Order Differential Operator

Automobiles have become the main means of transportation for human beings, and their failures in the process of operation are directly related to the life and property safety of drivers. Therefore, automobile fault diagnosis and early warning technologies have become urgent problems in the current academic world. The premise of real-time accurate diagnosis and early warning of automobiles is to obtain high-quality information data in real time, but the automobile operating environment is complex and changeable, resulting in the measured information data under the influence of multiple factors, such as equipment performance and signal interference. There is an unpredictable measurement error, which greatly affects the reliability of fault diagnosis and early warning systems. In this paper, on the basis of studying the structure and operation characteristics of automobiles, we design a method that can be used for real-time diagnosis and early warning of automobile faults; through the study of fractional-order calculus theory, we establish a mathematical model of information data fusion based on fractional-order differential operators. By providing high-quality information data to automobile fault diagnosis and early warning systems, real-time and accurate diagnosis and early warning functions for automobile faults can be realized. The feasibility and effectiveness of the method were verified through an experiment applying the technology in automobile fault diagnosis and early warning. The research results are of great significance for promoting the development of the automobile industry and ensuring the safety of drivers' lives and property.