The Fault Diagnosis of a Switch Machine Based on Deep Random Forest Fusion

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.