Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Mina Bagherzade Ghazvini (), Miquel Sànchez-Marrè, Davood Naderi and Cecilio Angulo
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Mina Bagherzade Ghazvini: Computer Science Department, Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Miquel Sànchez-Marrè: Computer Science Department, Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Davood Naderi: Siemens Energy S.L., Slottsvägen 2-6, 612 31 Finspang, Sweden
Cecilio Angulo: Automatic Control Department, Intelligent Data Science and Artificial Intelligence Research Centre (IDEAI), Universitat Politècnica de Catalunya, 08028 Barcelona, Spain

Energies, 2024, vol. 17, issue 3, 1-20

Abstract: Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.

Keywords: novelty detection; anomaly detection; time series; Matrix Profile; cluster based; data-driven method; gas turbine process (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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