Combining Support Vector Machines and Segmentation Algorithms for Efficient Anomaly Detection: A Petroleum Industry Application View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Luis Martí , Nayat Sanchez-Pi , José Manuel Molina , Ana Cristina Bicharra García

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. More... »

PAGES

269-278

References to SciGraph publications

  • 1999-06-11. Knowledge-Based Event Detection in Complex Time Series Data in ARTIFICIAL INTELLIGENCE IN MEDICINE
  • 1997-09. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2002. A Geometric Framework for Unsupervised Anomaly Detection in APPLICATIONS OF DATA MINING IN COMPUTER SECURITY
  • 1947-06. Note on the sampling error of the difference between correlated proportions or percentages in PSYCHOMETRIKA
  • Book

    TITLE

    International Joint Conference SOCO’14-CISIS’14-ICEUTE’14

    ISBN

    978-3-319-07994-3
    978-3-319-07995-0

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-07995-0_27

    DOI

    http://dx.doi.org/10.1007/978-3-319-07995-0_27

    DIMENSIONS

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