Condition Monitoring of Motorised Devices for Smart Infrastructure Capabilities

Research output: Contribution to journalConference article

Abstract

This paper presents a signal processing methodology based on fast Fourier transform for the early fault detection of electrically motorised devices. We used time-stamped, current draw data provided by Network Rail, UK, to develop a methodology that may identify imminent faults in point machine operations. In this paper we describe the data, preprocessing steps and methodology developed that can be used with similar motorised devices as a means of identifying potential fault occurrences. The novelty of our method is that it does not rely on labelled
data for fault detection. This method could be integrated into smart city infrastructure and deployed to provide automated asset maintenance management capabilities.
LanguageEnglish
Number of pages13
JournalCommunications in Computer and Information Science
Publication statusAccepted/In press - 18 Aug 2019
Event7th International Conference on Smart City and Informatization - Guangzhou, China
Duration: 12 Nov 201915 Nov 2019
Conference number: 7
http://www.isci-conf.org/iSCI2019/

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Condition Monitoring
Condition monitoring
Fault detection
Infrastructure
Fault Detection
Methodology
Fault
Fast Fourier transforms
Rails
Signal processing
Data Preprocessing
Fast Fourier transform
Signal Processing
Maintenance
Smart city

Cite this

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title = "Condition Monitoring of Motorised Devices for Smart Infrastructure Capabilities",
abstract = "This paper presents a signal processing methodology based on fast Fourier transform for the early fault detection of electrically motorised devices. We used time-stamped, current draw data provided by Network Rail, UK, to develop a methodology that may identify imminent faults in point machine operations. In this paper we describe the data, preprocessing steps and methodology developed that can be used with similar motorised devices as a means of identifying potential fault occurrences. The novelty of our method is that it does not rely on labelleddata for fault detection. This method could be integrated into smart city infrastructure and deployed to provide automated asset maintenance management capabilities.",
keywords = "Internet of Things, Cloud Computing, Distributed Systems, Raspberry Pi, Arduino, Smart Agriculture",
author = "Pritesh Mistry and Philip Lane and Paul Allen and Hussain Al-Aqrabi and Graham Hill",
year = "2019",
month = "8",
day = "18",
language = "English",
journal = "Communications in Computer and Information Science",
issn = "1865-0929",
publisher = "Springer Verlag",

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AU - Mistry, Pritesh

AU - Lane, Philip

AU - Allen, Paul

AU - Al-Aqrabi, Hussain

AU - Hill, Graham

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Y1 - 2019/8/18

N2 - This paper presents a signal processing methodology based on fast Fourier transform for the early fault detection of electrically motorised devices. We used time-stamped, current draw data provided by Network Rail, UK, to develop a methodology that may identify imminent faults in point machine operations. In this paper we describe the data, preprocessing steps and methodology developed that can be used with similar motorised devices as a means of identifying potential fault occurrences. The novelty of our method is that it does not rely on labelleddata for fault detection. This method could be integrated into smart city infrastructure and deployed to provide automated asset maintenance management capabilities.

AB - This paper presents a signal processing methodology based on fast Fourier transform for the early fault detection of electrically motorised devices. We used time-stamped, current draw data provided by Network Rail, UK, to develop a methodology that may identify imminent faults in point machine operations. In this paper we describe the data, preprocessing steps and methodology developed that can be used with similar motorised devices as a means of identifying potential fault occurrences. The novelty of our method is that it does not rely on labelleddata for fault detection. This method could be integrated into smart city infrastructure and deployed to provide automated asset maintenance management capabilities.

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KW - Cloud Computing

KW - Distributed Systems

KW - Raspberry Pi

KW - Arduino

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