this problem can be solved by providing experts and consistent diagnostic advice. Second, the study of quantitative fault diagnosis methods based on models is the focus of current fault diagnosis research. This type of research mainly uses machine learning algorithms to diagnose fault parameters based on data characteristics. Conventional fault diagnosis methods include BP neural network and SVM support vector machine [5; 6; 7]. For example, [PERSON] et al. [5] propose a new fault diagnosis method for marine diesel engine system. First, the marine diesel engine system is structured in several subcategories. Then, the state of each subsystem is diagnosed based on the SVM support vector machine algorithm, and then, an association rule mining algorithm is utilized to mine the implicit fault relationship between subsystems and realize the fault diagnosis of the marine diesel engine. [PERSON] et al. [8] propose a method to address the problem of diesel engine intake and exhaust system failure under different conditions. The method utilizes improved error back-propagation neural network and wavelet theory to collect cylinder head vibration signals at different measurement positions to realize fault diagnosis. [PERSON] et al. [9] propose a diesel engine fault diagnosis method based on SVM support vector machine and fuzzy C-means clustering principle. According to the diesel engine vibration signal and fault characterization technology realizing the fault diagnosis, experiments show that this method can accurately and effectively analyze the valve clearance faults. [PERSON] et al. [2] propose a fault diagnosis method based on fuzzy theory and SVM support vector machine. It shows that SVM can deal with fuzzy information of fault samples and can solve the inseparable problem in SVM classification. [PERSON] et al. [10] propose a genetic algorithm based on high-frequency demodulation analysis training support vector machine for diesel engine cylinder head fault diagnosis. The genetic algorithm is used to determine the training parameters of support vector machine. The intelligent diagnosis model of support vector machine (SVM) can be optimized. The performance of the GSVM system proposed in this study was evaluated by the cylinder head of a diesel engine in a firewood production facility. The application of diesel engine fault diagnosis shows the effectiveness of this method. [PERSON] et al. [11] propose a fault diagnosis method based on the PSO-SVM model and the centroid location algorithm. First, extract the fault features and analyze the data under normal conditions. Second, particle swarm optimization (PSO) is used to search the optimal parameters of support vector machine (SVM) and set fault diagnosis model. Then, the SVM classification interface is fitted to the curve to obtain the boundary conditions for fault diagnosis. Finally, the typical faults of the diesel engine lubrication system are diagnosed by the proposed fault diagnosis algorithm. The results show that the PSO-SVM model proposed achieves a classification accuracy of more than 95% and diagnoses faults of two typical lubrication systems in diesel engines according to the proposed diagnostic method. [PERSON] et al. [12] introduce the support vector machine (SVM) theory into diesel engine fault diagnosis and propose a new particle swarm optimization (PSO) and support vector machine (SVM) hybrid PSO-SVM model to improve the accuracy of engine diagnosis. The results show that, compared with BPNN and RBFNN, the prediction accuracy of PSO-SVM is significantly improved. Therefore, PSO-SVM is developed as an effective tool, so various support vector machines can be conveniently used as a PSO-SVM for diagnosing diesel engines core. [PERSON] et al. [13] provide effective model-based analysis and design methods for fault diagnosis and fault tolerance control. Structural models are used to analyze the propagation of faults throughout the process, test fault detection capabilities, and find redundancy in the process that can be utilized to ensure fault tolerance.