The automobile engine is a high-speed vehicle. Its working principle and process are to generate gas through fuel burning, and then convert the chemical energy contained in the exhaust gas into heat energy. However, in reality, due to various reasons, serious wear and tear inside the engine and sharp reduction of mechanical strength have occurred. This paper mainly introduces a method based on particle swarm optimization algorithm to simplify the complex structure between various systems in the car and outside. The analysis shows that this method has the advantages of accuracy and reliability, and can effectively diagnose and predict it. The advantage of large workload and small space is very prominent. After that, this paper designs a fault diagnosis model of automobile engine based on particle swarm optimization algorithm, and tests the model. The test results show that the fault diagnosis time of this model is short, and the fault diagnosis accuracy is high, which can meet the user needs.

Sridevi Sujata. Fusion Particle Swarm Optimization Algorithm in Automobile Engine Fault Diagnosis. Kinetic Mechanical Engineering (2023), Vol. 4, Issue 1: 48-56. https://doi.org/10.38007/KME.2023.040106.

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