Sequential anomalies: a study in the Railway Industry. Ribeiro, R. P., Pereira, P., & Gama, J. Machine Learning, 105(1):127–153, October, 2016. ![Sequential anomalies: a study in the Railway Industry [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Concerned with predicting equipment failures, predictive maintenance has a high impact both at a technical and at a financial level. Most modern equipments have logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for anomaly and novelty detection enables us to explore those datasets, building predictive systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case, we use a failure detection system to predict train door breakdowns before they happen using data from their logging system. We use sensor data from pneumatic valves that control the open and close cycles of a door. Still, the failure of a cycle does not necessarily indicates a breakdown. A cycle might fail due to user interaction. The goal of this study is to detect structural failures in the automatic train door system, not when there is a cycle failure, but when there are sequences of cycle failures. We study three methods for such structural failure detection: outlier detection, anomaly detection and novelty detection, using different windowing strategies. We propose a two-stage approach, where the output of a point-anomaly algorithm is post-processed by a low-pass filter to obtain a subsequence-anomaly detection. The main result of the two-level architecture is a strong impact in the false alarm rate.
@article{ribeiro_sequential_2016,
title = {Sequential anomalies: a study in the {Railway} {Industry}},
volume = {105},
issn = {1573-0565},
shorttitle = {Sequential anomalies},
url = {https://doi.org/10.1007/s10994-016-5584-6},
doi = {10.1007/s10994-016-5584-6},
abstract = {Concerned with predicting equipment failures, predictive maintenance has a high impact both at a technical and at a financial level. Most modern equipments have logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for anomaly and novelty detection enables us to explore those datasets, building predictive systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case, we use a failure detection system to predict train door breakdowns before they happen using data from their logging system. We use sensor data from pneumatic valves that control the open and close cycles of a door. Still, the failure of a cycle does not necessarily indicates a breakdown. A cycle might fail due to user interaction. The goal of this study is to detect structural failures in the automatic train door system, not when there is a cycle failure, but when there are sequences of cycle failures. We study three methods for such structural failure detection: outlier detection, anomaly detection and novelty detection, using different windowing strategies. We propose a two-stage approach, where the output of a point-anomaly algorithm is post-processed by a low-pass filter to obtain a subsequence-anomaly detection. The main result of the two-level architecture is a strong impact in the false alarm rate.},
language = {en},
number = {1},
urldate = {2022-02-23},
journal = {Machine Learning},
author = {Ribeiro, Rita P. and Pereira, Pedro and Gama, João},
month = oct,
year = {2016},
pages = {127--153},
}
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