Name
Title | ||
|---|---|---|
Combining Support Vector Machines and Segmentation Algorithms for Efficient Anomaly Detection: A Petroleum Industry Application |
Abstract | ||
|---|---|---|
Anomaly detection is the problem of finding patterns in data that do not conform to expected behavior. Similarly, when patterns are numerically distant from the rest of sample, anomalies are indicated as outliers. Anomaly detection had recently attracted the attention of the research community for real-world applications. 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 sense, 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. For dealing with this and with the lack of labeled data, in this paper we propose a combination of a fast and high quality segmentation algorithm with a one-class support vector machine approach for efficient anomaly detection in turbomachines. As result we perform empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-07995-0_27 | INTERNATIONAL JOINT CONFERENCE SOCO'14-CISIS'14-ICEUTE'14 |
Keywords | Field | DocType |
Anomaly detection,support vector machines,time series segmentation,oil industry application | Anomaly detection,Data mining,Computer science,Artificial intelligence,Labeled data,Empirical research,Time-series segmentation,Petroleum industry,Segmentation,Support vector machine,Outlier,Algorithm,Machine learning | Conference |
Volume | ISSN | Citations |
299 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Martí | 1 | 43 | 9.51 |
| Nayat Sánchez Pi | 2 | 48 | 15.93 |
| José Manuel Molina | 3 | 2 | 2.75 |
| Ana Cristina Bicharra Garcia | 4 | 247 | 50.45 |