Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/9/2692 |
| _version_ | 1797568466105925632 |
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| author | Pritesh Mistry Phil Lane Paul Allen |
| author_facet | Pritesh Mistry Phil Lane Paul Allen |
| author_sort | Pritesh Mistry |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T19:57:19Z |
| format | Article |
| id | doaj.art-488cd8d8effa46219e6d6074ffaddfc0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:57:19Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-488cd8d8effa46219e6d6074ffaddfc02023-11-19T23:52:04ZengMDPI AGSensors1424-82202020-05-01209269210.3390/s20092692Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor DataPritesh Mistry0Phil Lane1Paul Allen2School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKSchool of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKSchool of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UKIn 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.https://www.mdpi.com/1424-8220/20/9/2692condition monitoringsignal processingfast Fourier transformrailway point-operating machinesturnoutfault detection |
| spellingShingle | Pritesh Mistry Phil Lane Paul Allen Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data Sensors condition monitoring signal processing fast Fourier transform railway point-operating machines turnout fault detection |
| title | Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data |
| title_full | Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data |
| title_fullStr | Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data |
| title_full_unstemmed | Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data |
| title_short | Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data |
| title_sort | railway point operating machine fault detection using unlabeled signaling sensor data |
| topic | condition monitoring signal processing fast Fourier transform railway point-operating machines turnout fault detection |
| url | https://www.mdpi.com/1424-8220/20/9/2692 |
| work_keys_str_mv | AT priteshmistry railwaypointoperatingmachinefaultdetectionusingunlabeledsignalingsensordata AT phillane railwaypointoperatingmachinefaultdetectionusingunlabeledsignalingsensordata AT paulallen railwaypointoperatingmachinefaultdetectionusingunlabeledsignalingsensordata |