Abstract: Failure diagnosis of oil pumping machine has low identification accuracy due to various faults and complex system, which increases the difficulty of fault diagnosis. After clarifying the working principle of SVM, the ant colony algorithm is carefully studied to adjust the penalty coefficient of SVM(Support Vector Machine) and the kernel function parameters. The ant colony algorithm has the problem of easy to fall into the local optimal solution, which introduces the idle ant to update the pheromone again after the ant colony algorithm fails to enable the ant group to obtain new paths. In order to further reduce the problem of local optimal solution of ant colony algorithm and improve the search speed of ordinary ants in the early stage of optimization, idle ants are optimized by using chaotic initialization and chaotic perturbation. The test data of the pumping machine shows that the proposed fault diagnosis system has high fault identification accuracy.

Key words: pumping unit, fault diagnosis, support vector machine ( SVM ), ant colony algorithm, Chaotic algorithm