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 research. 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 computational 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.
State-of-the-art train delay prediction systems do not exploit historical train movements data collected by the railway information systems, but they rely on static rules built by expert of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven train delay prediction system for large-scale railway networks which exploits the most recent Big Data technologies and learning algorithms. In particular, we propose a fast learning algorithm for predicting train delays based on the Extreme Learning Machine that fully exploits the recent in-memory large-scale data processing technologies. Our system is able to rapidly extract nontrivial information from the large amount of data available in order to make accurate predictions about different future states of the railway network. Results on real world data coming from the Italian railway network show that our proposal is able to improve the current state-of-the-art train delay prediction systems.
Current Train Delay Prediction Systems (TDPSs) do not take advantage of state-of-the-art tools and techniques for extracting useful insights from large amounts of historical data collected by the railway information systems. Instead, these systems rely on static rules, based on classical univariate statistic, built by experts of the railway infrastructure. The purpose of this book chapter is to build a data-driven TDPS for large-scale railway networks, which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.
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