Research on the application of artificial intelligence method in automobile engine fault diagnosis

Abstract

The application of artificial intelligence methods in fault diagnosis is becoming more and more extensive, and exploring and researching intelligent fault diagnosis methods for automobile engines is also a hot spot in the field of automotive engineering. Different machine learning methods have their own advantages and disadvantages. By extracting different characteristic parameters and optimizing the combination of multiple algorithms, faster and stable diagnosis results can be achieved, so that faults can be discovered and repaired in time. Aiming at the potential fluctuation and impact characteristics of vibration plus signal caused by different failure states of automobile engines, this paper proposes two engine fault identification methods using vibration acceleration signals as diagnostic parameters. They are Cross Validation -Support Vector Machine (CV-SVM)and Particle Swarm Optimization-Probabilistic Neural Network (PSO-PNN) engine fault identification methods. The advantages and disadvantages of the two methods are compared and analyzed. Obtain the amplitude corresponding to the frequency multiplication of the vibration acceleration signal through the spectrum analysis method, which is used as the main component of the input feature vector, and establish the SVM fault diagnosis model combined with the cross-validation method (CV); In addition, multiple one-dimensional arrays composed of time-domain signals are directly used as input feature vectors, and the particle swarm optimization (PSO) parameter optimization is used to obtain the best Probabilistic Neural Network(PNN) fault diagnosis model. The results show that both the CV-SVM (small sample) method and the PSO-PNN method (large sample) can realize the identification and diagnosis of the established engine fault type. The CV-SVM method has more advantages in diagnostic fault tolerance, but the PSO-PNN method can simplify the process of feature sample preparation, save a lot of manual feature extraction tasks, and is more convenient in practical application.