Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine

Railway switch machine is essential for maintaining the safety and punctuality of train operations.A data-driven fault diagnosis scheme for railway switch machine using tensor machine and multi-representation monitoring data is developed herein.Unlike existing methods, this approach takes into account the spatial information of the time series monitoring data, aligning with the domain expertise of on-site manual monitoring.Besides, a multisensor fusion tensor machine is designed to improve single signal data's limitations in insufficient information.First, one-dimensional signal data is preprocessed and transformed into two-dimensional images.Afterward, the fusion feature tensor is created by utilizing the images of the three-phase current and employing the CANDE-COMP/PARAFAC (CP) decomposition method.Then, the tensor learning-based model is built using the extracted fusion feature tensor.The developed fault diagnosis scheme is valid with the field three-phase current dataset.The experiment indicates an enhanced performance of the developed fault diagnosis scheme over the current approach, particularly in terms of recall, precision, and F1-score.