Support vector machines have a simple theory and strong practicability, which are thus widely used in fault diagnosis. In the process of analyzing the influence of support vector machine parameters on classification results, it is found that inappropriate parameter selection often leads to poor classification results. The adverse effects of artificial selection can be avoided by using a heuristic optimization method. However, taking the equivalent interval distance as the optimization goal is prone to result in “over learning”. Taking the equivalent interval distance, the number of support vectors and the misclassification rate as optimization objectives at the same time, this study proposes a multi-objective support vector machine method based on particle swarm optimization. The strategies of timed restart and dynamic learning factor are used to improve the global optimization ability of the algorithm. In this way, the overall structural risk can be reduced. The proposed method is applied to the fault diagnosis of a complex diesel engine with strong correlation and coupling of multiple faults. The experimental results show that this method can effectively diagnose the fault of abnormal noise from diesel engines in the case of small samples and incomplete or uncertain symptoms, and the comprehensive optimal solution obtained by screening is more in line with people’s expectations.