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Sensors 2015, 15(2), 2774-2797; doi:10.3390/s150202774

Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

1
Department of Electrical Engineering, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
2
Instituto de Lógica, Filosofia e Teoria da Ciéncia (ILTC), Niterói 24020-042, Brazil
3
Department of Informatics, Universidad Carlos III de Madrid, Colmenarejo, Madrid 28270, Spain
4
ADDLabs, Universidade Federal Fluminense, Niterói 24210-340, Brazil
*
Author to whom correspondence should be addressed.
Received: 30 September 2014 / Accepted: 19 January 2015 / Published: 27 January 2015
(This article belongs to the Section Physical Sensors)
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Abstract

Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA), a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection. View Full-Text
Keywords: anomaly detection; big data; time-series segmentation; outlier detection; oil industry applications anomaly detection; big data; time-series segmentation; outlier detection; oil industry applications
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Martí, L.; Sanchez-Pi, N.; Molina, J.M.; Garcia, A.C.B. Anomaly Detection Based on Sensor Data in Petroleum Industry Applications. Sensors 2015, 15, 2774-2797.

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