Fault Diagnosis Scheme for Railway Switch Machine Using Multi-Sensor Fusion Tensor Machine
Source abstract
Abstract
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 multi-sensor 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 CANDECOMP/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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Vehicle License Plate Recognition · Power Systems and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
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