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Open AccessArticle

Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data

School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
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Sensors 2020, 20(9), 2692; https://doi.org/10.3390/s20092692
Received: 31 March 2020 / Revised: 1 May 2020 / Accepted: 7 May 2020 / Published: 9 May 2020
(This article belongs to the Section Fault Diagnosis & Sensors)
In this study, we propose a methodology for the identification of potential fault occurrences of railway point-operating machines, using unlabeled signal sensor data. Data supplied by Network Rail, UK, is processed using a fast Fourier transform signal processing approach, coupled with the mean and max current levels to identify potential faults in point-operating machines. The method developed can dynamically adapt to the behavioral characteristics of individual point-operating machines, thereby providing bespoke condition monitoring capabilities in situ and in real time. The work described in this paper is not unique to railway point-operating machines, rather the data pre-processing and methodology is readily applicable to any motorized device fitted with current sensing capabilities. The novelty of our approach is that it does not require pre-labelled data with historical fault occurrences and therefore closely resembles problems of the real world, with application for smart city infrastructure. Lastly, we demonstrate the problems faced with handling such data and the capability of our methodology to dynamically adapt to diverse data presentations. View Full-Text
Keywords: condition monitoring; signal processing; fast Fourier transform; railway point-operating machines; turnout; fault detection; unlabeled data; smart sensors condition monitoring; signal processing; fast Fourier transform; railway point-operating machines; turnout; fault detection; unlabeled data; smart sensors
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Mistry, P.; Lane, P.; Allen, P. Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data. Sensors 2020, 20, 2692.

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