Railroad switch machines are essential electromechanical equipment in a railway network, and the occurrence of failures in such equipment can cause railroad interruptions and lead to potential economic losses. Thus, early diagnosis of these failures can represent a reduction in costs and an increase in productivity. This paper aims to propose a predictive model based on computational intelligence techniques, to solve this problem. The applied methodology includes feature extraction and selection procedures based on hypothesis tests and unsupervised machine learning models. The proposed model was tested in a database made available by a Brazilian railway company and proved to be efficient once it has considered critical operations conducted in the vicinity of the ones classified as faults.

铁路开关机是铁路网络中必不可少的机电设备，此类设备发生故障会导致铁路中断并导致潜在的经济损失。因此，对这些故障的早期诊断可以降低成本并提高生产率。本文旨在提出一种基于计算智能技术的预测模型，以解决该问题。应用的方法包括基于假设检验和无监督机器学习模型的特征提取和选择程序。该提议的模型在巴西铁路公司提供的数据库中进行了测试，并且一旦考虑了在分类为故障的地方附近进行的关键操作，便被证明是有效的。