TY - GEN
T1 - A Complete Streaming Pipeline for Real-time Monitoring and Predictive Maintenance
AU - Le-Nguyen, Minh Huong
AU - Turgis, Fabien
AU - Fayemi, Pierre Emmanuel
AU - Bifet, Albert
N1 - Publisher Copyright:
© ESREL 2021. Published by Research Publishing, Singapore.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Railway maintenance is changing as predictive maintenance (PdM) comes to prominence. In particular, the rapid progress of learning algorithms, commonly known as Machine learning (ML), strongly motivates data-driven PdM applications. However, traditional ML struggles with the large amount of data that arrive at high velocity in realtime streams. Facing big data-related issues, Stream learning (SL) is a new learning paradigm that adapts ML to the handling of fast, unbounded, and dynamic data streams. We deem SL suitable for online monitoring and relevant to our need of having incremental, drift-aware algorithms that quickly detect and predict anomalies. Aiming to enhance railway PdM with SL, we propose a complete streaming pipeline for real-time monitoring, anomaly detection, and anomaly prediction. A partial implementation of this pipeline has resulted in an interactive application named InterCE. Preliminary results on two real-world datasets supplied by a French railway company show that InterCE helps to improve the accuracy of the learning process.
AB - Railway maintenance is changing as predictive maintenance (PdM) comes to prominence. In particular, the rapid progress of learning algorithms, commonly known as Machine learning (ML), strongly motivates data-driven PdM applications. However, traditional ML struggles with the large amount of data that arrive at high velocity in realtime streams. Facing big data-related issues, Stream learning (SL) is a new learning paradigm that adapts ML to the handling of fast, unbounded, and dynamic data streams. We deem SL suitable for online monitoring and relevant to our need of having incremental, drift-aware algorithms that quickly detect and predict anomalies. Aiming to enhance railway PdM with SL, we propose a complete streaming pipeline for real-time monitoring, anomaly detection, and anomaly prediction. A partial implementation of this pipeline has resulted in an interactive application named InterCE. Preliminary results on two real-world datasets supplied by a French railway company show that InterCE helps to improve the accuracy of the learning process.
KW - Human-in-the-loop
KW - Machine learning
KW - Monitoring
KW - Predictive maintenance
KW - Railway
KW - Stream learning
UR - https://www.scopus.com/pages/publications/85135458626
U2 - 10.3850/978-981-18-2016-8_400-cd
DO - 10.3850/978-981-18-2016-8_400-cd
M3 - Conference contribution
AN - SCOPUS:85135458626
SN - 9789811820168
T3 - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
SP - 2112
EP - 2119
BT - Proceedings of the 31st European Safety and Reliability Conference, ESREL 2021
A2 - Castanier, Bruno
A2 - Cepin, Marko
A2 - Bigaud, David
A2 - Berenguer, Christophe
PB - Research Publishing, Singapore
T2 - 31st European Safety and Reliability Conference, ESREL 2021
Y2 - 19 September 2021 through 23 September 2021
ER -