Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO—SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO—SVM approach.

In the stage of character recognition, it is necessary to select the algorithm that matches the character features of train number. The train number characters of railway freight trains are all printed in straight style. Compared with handwritten characters, each character has relatively fixed features. However, due to the complex operation environment of railway freight trains, the character regions are cracked, tilted and partially missing. Therefore, it is very important to select an algorithm with good robustness. In view of the above analysis, this paper proposes a research on railway freight car number recognition algorithm based on GSO-BP algorithm, the experimental results show that the proposed method is effective in character recognition.