A More Realistic Scheme of Deviation Error Representation for Gas Turbine Diagnostics

Loboda, Igor; Yepifanov, Sergiy; Feldshteyn, Yakov

doi:10.1515/tjj-2013-0006

Cited by 5 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gas turbine fault recognition is usually performed in the space of measurement deviations, and many publications that illustrate the representation of fault classes in this space can be found. 21,26,33,34 However, pattern recognition techniques are not commonly used in the dũ-space, and it is of interest to analyze the distribution of fault patterns in this space. As an example, the analysis of deviations dũ Ã of Case 1 (simulated deviation noise and one-point diagnostic analysis) and Case 5 (real deviation noise and multi-point diagnostic analysis) is carried out.…”

Section: Examples Of Fault Class Representationmentioning

confidence: 99%

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

Pérez-Ruiz

Loboda

González-Castillo

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

View full text Add to dashboard Cite

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

show abstract

Section: Examples Of Fault Class Representationmentioning

confidence: 99%

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

Pérez-Ruiz

Loboda

González-Castillo

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the use of simulated deviation measurement noise in gas turbine diagnostic algorithms is a common practice, real deviation errors can present different distributions that can affect the final diagnosis reliability. A procedure proposed to extract error components from deviations working with real data can be found in Loboda et al 5 A degraded engine model Y(U, t) obtained by the least-squares method and input data including multiple operating points with different degradation severity are required. Using this model, a real deviation error can be given as follows…”

Section: Diagnostic Technique Evaluation Proceduresmentioning

confidence: 99%

“…Over the past years, fault identification algorithms have been developed based on diverse pattern recognition and machine learning techniques. [2][3][4][5] Since gas turbines are very complex machines and need to be monitored, exhaustive comparative studies about diagnostic techniques can give clearer and more solid recommendations on how to construct an effective monitoring system. 3,4 Considering this necessity, this investigation evaluates two types of gas path diagnostic techniques: support vector machines (SVMs) and artificial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of gas turbine diagnostic techniques under variable fault conditions

Pérez-Ruiz

Loboda

Miró-Zárate

et al. 2017

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

The aim of this study is to evaluate gas path diagnostic techniques using a principle of variable structure classification applied to cover possible fault scenarios in gas turbine maintenance. This principle allows creating more versatile and realistic fault conditions relative to existing studies such as complex fault classifications, a new boundary for fault severity, and real deviation errors. The techniques analyzed are included into a special procedure that repeats a diagnostic process many times and computes for each fault class a probability of correct diagnosis. Using this probability averaged for all the classes as the evaluation criterion, the techniques are tested under the conditions of four comparative studies. The results show that (a) there is no single technique significantly outperforming all others over the full range of diagnostic conditions even if engine operating modes, fault simulation data, fault classifications, multiple-class boundaries or the scheme of deviation errors are varied; (b) the common level of diagnosis accuracy greatly depends on the fault classification used; (c) significant influence of fault severity boundary is found. The boundary proposed makes the level of accuracy much more realistic compared to simplified boundaries previously used; and (d) the use of real deviation noise in fault class description instead of simulated errors further approaches the diagnostic conditions and results to the level expected in practice.

show abstract

Investigation of Performance Degradation in a Mixed Flow Low Bypass Turbofan Engine

Rath

Mishra

Kushari

2023

J Fail. Anal. and Preven.

View full text Add to dashboard Cite

A More Realistic Scheme of Deviation Error Representation for Gas Turbine Diagnostics

Cited by 5 publications

References 0 publications

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

Evaluation of gas turbine diagnostic techniques under variable fault conditions

Investigation of Performance Degradation in a Mixed Flow Low Bypass Turbofan Engine

Contact Info

Product

Resources

About