As the key equipment for train operation, the switch machine plays a vital role in the safe and punctual operation of the trains. Nowadays, the fault diagnosis methods of switch machine turnout are mostly based on single-source data. However, it is difficult to fully characterize the fault characteristics using single-source data. In this article, a deep random forest fusion (DRFF) method is proposed to fuse the vibration signals in three directions of the switch machine, which can effectively improve the fault diagnosis accuracy of the switch machine. The fault features are extracted by the wavelet transform method. Subsequently, the features are further optimized by the deep Boltzmann machine. Meanwhile, the DRFF model is formed by using the RFF method to fuse the 3D vibration signals at the feature level. Compared with single-source data and other methods, it is proved that the diagnosis accuracy of the proposed method (98.13%) is far higher than that of other methods, indicating the feasibility of the proposed method, which can greatly improve the fault diagnosis accuracy of the switch machine.

中文翻译：

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基于深度随机森林融合的转辙机故障诊断

转辙器作为列车运行的关键设备，对列车安全、准时运行起着至关重要的作用。目前，转辙器道岔的故障诊断方法大多基于单源数据。然而，单源数据难以全面刻画故障特征。本文提出了一种深度随机森林融合（DRFF）方法对转辙机三个方向的振动信号进行融合，可以有效提高转辙机故障诊断的准确率。通过小波变换方法提取故障特征。随后，通过深度玻尔兹曼机进一步优化特征。同时，采用RFF方法在特征层对3D振动信号进行融合，形成DRFF模型。