Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis. Fumeo, E., Oneto, L., & Anguita, D. Procedia Computer Science, 53:437–446, January, 2015. ![Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Streaming Data Analysis (SDA) of Big Data Streams (BDS) for Condition Based Maintenance (CBM) in the context of Rail Transportation Systems (RTS) is a state-of-the-art field of re- search. SDA of BDS is the problem of analyzing, modeling and extracting information from huge amounts of data that continuously come from several sources in real time through com- putational aware solutions. Among others, CBM for Rail Transportation is one of the most challenging SDA problems, consisting of the implementation of a predictive maintenance system for evaluating the future status of the monitored assets in order to reduce risks related to failures and to avoid service disruptions. The challenge is to collect and analyze all the data streams that come from the numerous on-board sensors monitoring the assets. This paper deals with the problem of CBM applied to the condition monitoring and predictive maintenance of train axle bearings based on sensors data collection, with the purpose of maximizing their Remaining Useful Life (RUL). In particular we propose a novel algorithm for CBM based on SDA that takes advantage of the Online Support Vector Regression (OL-SVR) for predicting the RUL. The novelty of our proposal is the heuristic approach for optimizing the trade-off between the accuracy of the OL-SVR models and the computational time and resources needed in order to build them. Results from tests on a real-world dataset show the actual benefits brought by the proposed methodology.
@article{fumeo_condition_2015,
series = {{INNS} {Conference} on {Big} {Data} 2015 {Program} {San} {Francisco}, {CA}, {USA} 8-10 {August} 2015},
title = {Condition {Based} {Maintenance} in {Railway} {Transportation} {Systems} {Based} on {Big} {Data} {Streaming} {Analysis}},
volume = {53},
issn = {1877-0509},
url = {https://www.sciencedirect.com/science/article/pii/S1877050915018244},
doi = {10.1016/j.procs.2015.07.321},
abstract = {Streaming Data Analysis (SDA) of Big Data Streams (BDS) for Condition Based Maintenance (CBM) in the context of Rail Transportation Systems (RTS) is a state-of-the-art field of re- search. SDA of BDS is the problem of analyzing, modeling and extracting information from huge amounts of data that continuously come from several sources in real time through com- putational aware solutions. Among others, CBM for Rail Transportation is one of the most challenging SDA problems, consisting of the implementation of a predictive maintenance system for evaluating the future status of the monitored assets in order to reduce risks related to failures and to avoid service disruptions. The challenge is to collect and analyze all the data streams that come from the numerous on-board sensors monitoring the assets. This paper deals with the problem of CBM applied to the condition monitoring and predictive maintenance of train axle bearings based on sensors data collection, with the purpose of maximizing their Remaining Useful Life (RUL). In particular we propose a novel algorithm for CBM based on SDA that takes advantage of the Online Support Vector Regression (OL-SVR) for predicting the RUL. The novelty of our proposal is the heuristic approach for optimizing the trade-off between the accuracy of the OL-SVR models and the computational time and resources needed in order to build them. Results from tests on a real-world dataset show the actual benefits brought by the proposed methodology.},
language = {en},
urldate = {2021-03-21},
journal = {Procedia Computer Science},
author = {Fumeo, Emanuele and Oneto, Luca and Anguita, Davide},
month = jan,
year = {2015},
keywords = {Big Data Streams, Condition Based Maintenance, Data Analytics, Intelligent Transporta- tion Systems, Model Selection, Online Learning},
pages = {437--446},
}
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