Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method

Li, Fēi; Wang, Hongzhi; Zhou, Guowen; Yu, Daren; Li, Jiangzhong; Gao, Hong

doi:10.3390/en10050724

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…Symbolic dynamics filtering (SDF) is a data-driven processing technique characterized by capturing the underlying dynamic behavior of a time series [7]- [9]. A (finite length) continuous time series is converted into a discrete sequence over a finite alphabet using a symbolic dynamicsbased analysis.…”

Section: Introductionmentioning

confidence: 99%

Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning

Santos,

Chaves,

Pimentel

2024

IEEE Access

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Time series are the natural way for accessing information about dynamical systems or processes in a variety of scientific, engineering and financial applications. Due to their complexity, the use of data-based methods is imperative. An example of this method is the symbolic dynamic filtering technique, which involves the determination of Markovian models to express the causal structure of the observed dynamic behavior. We propose a new algorithm for obtaining these models based on variable length Markov chains, machine learning algorithms and graph minimization techniques. To validate the algorithm, we provide modeling examples from simulated and experimental dataset, showing that the obtained model is superior to those generated by other techniques. Finally, we apply the proposed framework for anomalous detection of rotating machines.INDEX TERMS Anomaly detection, clustering, graph minimization, probabilistic finite state automaton, time series analysis, variable length Markov models.

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Section: Introductionmentioning

confidence: 99%

Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning

Santos,

Chaves,

Pimentel

2024

IEEE Access

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“…Data-mining techniques have been widely employed across a range of domains to extract relevant knowledge from large datasets, in order to support informed decision-making processes [6]. In particular, increasing attention has been directed towards the application of data-mining techniques for knowledge extraction from time series data-i.e., an ordered set of observations recorded over time pertaining to a particular phenomenon and measured across a defined time span [7,8].…”

Section: Introductionmentioning

confidence: 99%

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

Ghazvini,

Sànchez-Marrè,

Naderi

et al. 2024

Energies

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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.

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“…When an abnormal change in data is detected, several abnormal data are combined to make decisions. Such systems include oil drilling early warning systems [1], industrial sensor systems [2], internet data stream [3], medical surveillance systems [4], gas turbine fuel systems [5], the Internet of Things [6], and wind turbines [7]. A general decision system flowchart is shown in Figure 1, as follows: As shown in Figure 1, machine learning can obtain the decision model by mining the rule of abnormal change of the parameters.…”

Section: Introductionmentioning

confidence: 99%

The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection

Wang

Yue

2018

Symmetry

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The effect of the application of machine learning on data streams is influenced by concept drift, drift deviation, and noise interference. This paper proposes a data stream anomaly detection algorithm combined with control chart and sliding window methods. This algorithm is named DCUSUM-DS (Double CUSUM Based on Data Stream), because it uses a dual mean value cumulative sum. The DCUSUM-DS algorithm based on nested sliding windows is proposed to satisfy the concept drift problem; it calculates the average value of the data within the window twice, extracts new features, and then calculates accumulated and controlled graphs to avoid misleading by interference points. The new algorithm is simulated using drilling engineering industrial data. Compared with automatic outlier detection for data streams (A-ODDS) and with sliding nest window chart anomaly detection based on data streams (SNWCAD-DS), the DCUSUM-DS can account for concept drift and shield a small amount of interference deviating from the overall data. Although the algorithm complexity increased from 0.1 second to 0.19 second, the classification accuracy receiver operating characteristic (ROC) increased from 0.89 to 0.95. This meets the needs of the oil drilling industry data stream with a sampling frequency of 1 Hz, and it improves the classification accuracy.

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Anomaly Detection in Gas Turbine Fuel Systems Using a Sequential Symbolic Method

Cited by 10 publications

References 37 publications

Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning

Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning

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

The Application of a Double CUSUM Algorithm in Industrial Data Stream Anomaly Detection

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