Vibration Signal-Based Fault Diagnosis of Railway Point Machines via Double-Scale CNN

In the railway transportation industry, fault diagnosis of railway point machines (RPMs) is vital. Because operational vibration signals can reflect the condition of various faults in mechanical devices, vibration sensing and monitoring and more importantly, vibration signal-based fault diagnosis for RPMs have attracted the attention of scholars and engineers. Most vibration signal-based fault-diagnosis methods for RPMs rely on data collected using high-sampling-rate sensors and manual feature extraction, hence are costly and insufficiently robust. To overcome these shortcomings, we propose a double-scale wide first-layer kernel convolutional neural network (DS-WCNN) for RPMs fault diagnosis using inexpensive and low-sampling-rate vibration sensors. The proposed wide first-layer kernels, which extract features from vibration observations, are particularly suitable for low-sampling-rate signals. Meanwhile, the proposed double-scale structure improves accuracy and noise suppression by combining two types of timescale features. Sufficient experiments, including noise addition and comparison, were conducted to demonstrate the robustness and accuracy of the proposed algorithm.