Detection and clustering of neutral section faults using machine learning techniques for SMART railways

Kennedy Phala, Wesley Doorsamy, Babu Sena Paul

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Fault detection and diagnosis plays an important role particularly in railways were abnormal events are detected and a detailed root causes analysis is performed to prevent similar occurrence. The current method used to detect immediate and long-term faults is through foot inspections and inspection trolleys fitted with cameras proving to be inefficient and time consuming when analyzing the data. This paper examines the smart fault detection system on the overhead wires by applying machine learning techniques for accurate assessment of the neutral section before and after failure thereby grouping the events into fault bins. Modern computational intelligence has enabled the fault diagnostic and fault detection to be accurate from the data generated and sensors. The interaction between the pantograph and contact wire will be monitored using accelerometers and non-contact infrared thermometer sensors were should there be a deviation from the normal signal spectrum it will be detected. The measured data from onsite will be conveyed to ThingSpeak for cloud computation thereby providing notifications in real-time which allows the end user to visualize, analyze and act on data online. A prototype has been built and tested which shows that the system works reasonably with data collected from sensors.

Original languageEnglish
Title of host publication2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781728145778
DOIs
Publication statusPublished - Nov 2019
Event6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019 - Johannesburg, South Africa
Duration: 19 Nov 201920 Nov 2019

Publication series

Name2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019

Conference

Conference6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019
Country/TerritorySouth Africa
CityJohannesburg
Period19/11/1920/11/19

Keywords

  • Accelerometer
  • Data aggregation
  • Fault diagnostic
  • K-means
  • Machine learning
  • Neutral section

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Computational Mathematics
  • Modeling and Simulation

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