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In Situ Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions
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Anomaly detection based on sensor data in petroleum industry applications.

Luis Martí1, Nayat Sanchez-Pi2, José Manuel Molina3

  • 1Department of Electrical Engineering, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro 22451-900, Brazil. lmarti@ele.puc-rio.br.

Sensors (Basel, Switzerland)
|January 31, 2015
PubMed
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This study introduces a novel anomaly detection method combining Yet Another Segmentation Algorithm (YASA) and a one-class support vector machine for turbomachinery. The approach efficiently identifies anomalies in sensor data, even with limited labeled training data.

Area of Science:

  • Data Science
  • Machine Learning
  • Industrial Monitoring

Background:

  • Anomaly detection identifies unusual data patterns, crucial for applications like fraud and fault detection.
  • Turbomachinery in the petroleum industry requires intensive monitoring for damage prevention.

Purpose of the Study:

  • To develop an efficient anomaly detection method for turbomachinery.
  • To address the challenge of limited labeled training data in industrial applications.

Main Methods:

  • A novel approach combining Yet Another Segmentation Algorithm (YASA) with a one-class support vector machine (SVM).
  • YASA is a fast, high-quality segmentation algorithm.
  • One-class SVM is used for anomaly detection with unlabeled data.

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Last Updated: Apr 18, 2026

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Main Results:

  • The proposed method demonstrates efficient anomaly detection in turbomachinery.
  • Empirical studies show competitive performance against other methods on benchmark and real-life data.

Conclusions:

  • The YASA and one-class SVM combination offers an effective solution for anomaly detection in turbomachinery.
  • This approach is valuable for industrial monitoring and damage prevention, especially with scarce labeled data.