Title :
Towards predictive maintenance and management in rail sector: A clustering approach
Author :
Antony, J. John Victor ; Nasira, G.M.
Author_Institution :
Supervisors´ Training Centre, South Western Railway, Bangalore, India
Abstract :
It is accepted beyond doubt that the back bone of mass transportation in India is Railways and it is regarded as the lifeline of the nation. Indian Railways is using various information technology based systems to maintain and manage its infrastructural resources, leading to generation and accumulation of voluminous data. The data captured is expected to possess hidden meaning and trend which, if properly explored and modelled, can aid in implementing the new paradigm called Predictive Maintenance and Management. The thrust to usher in the new paradigm amply manifests in its Vision 2020 statement, too. The paper aims to showcase a methodology using Clustering, a Data Mining Technique that can bring in a maintenance plan built upon the predictive behaviour exhibited by failure data. The model outlined here has been tested on actual failure data pertaining to passenger carrying vehicles of trains.
Keywords :
data mining; maintenance engineering; pattern clustering; planning; railways; Indian Railways; Vision 2020; clustering; data mining technique; failure data; information technology; maintenance plan; mass transportation; passenger carrying vehicles; predictive maintenance and management; rail sector; trains; Clustering algorithms; Data mining; Data models; Maintenance engineering; Market research; Rails; Vehicles; Clustering; Data Analysis; Data Description; Data Mining; K Means algorithm; Pattern Analysis; Railways; Transportation;
Conference_Titel :
Recent Trends in Information Technology (ICRTIT), 2013 International Conference on
Conference_Location :
Chennai
DOI :
10.1109/ICRTIT.2013.6844254
