Car assembly line fault diagnosis based on modified support vector classifier machine

摘要

It is difficult to obtain accurately the solution to parameter b in the final decision-making function of support vector classifier (SVC) machine. By a proposed transformation, parameter b is considered into confidence interval of ν-SVC model. Then this paper proposes a new ν-support vector classifier machine (Nν-SVC). To seek the optimal parameter of Nν-SVC, particle swarm optimization (PSO) is proposed. The results of application in fault diagnosis of car assembly line show the hybrid diagnosis model based on Nν-SVC and PSO is effective and feasible, the comparison between the method proposed in this paper and other ones is also given, which proves this method is equivalent to standard ν-SVC.