An Assessment Methodology for Data-Driven and Model-Based Techniques for Engine Health Monitoring

Butler, Steven W.; Pattipati, Krishna R.; Volponi, Allan J.; Hull, Jon; Rajamani, Ravi; Siegel, Jason B.

doi:10.1115/gt2006-91096

Cited by 15 publications

(15 citation statements)

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“…Many pattern classification techniques are applied to diagnose gas turbines, including Artificial Neural Net works (ANNs) [7][8][9][10][11], Genetic Algorithms [7], Support Vector Machines [8], and Bayesian Approach (BA) [10]. The ANNs are mainly used and among them the Multilayer Perceptron (MLP) is most widespread [12].…”

Section: Introductionmentioning

confidence: 99%

“…The ANNs are mainly used and among them the Multilayer Perceptron (MLP) is most widespread [12]. Other network types, for instance, Probabilistic Neural Network (PNN) [5,8], are also applied for diagnosing gas turbines. The PNN utilizes the criterion of a fault probability to classify fault patterns, therefore every diagnosis is accompanied by a confidence probability.…”

Section: Introductionmentioning

confidence: 99%

“…Many fault isolation algorithms and numerous publi cations follow the approach [7][8][9][10][11][12][13][14] based on the pattern classification theory. The necessary fault classes are con structed and a diagnostic decision is made in the space of deviations of the monitored variables.…”

Section: Introductionmentioning

confidence: 99%

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Gas Turbine Fault Diagnosis Using Probabilistic Neural Networks

Loboda

Olivares-Robles

2015

International Journal of Turbo &Amp; Jet-Engines

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Efficiency of gas turbine monitoring systems pri marily depends on the accuracy of employed algorithms, in particular, pattern classification techniques for diag nosing gas path faults. In recent investigations many tech niques have been applied to classify gas path faults, but recommendations for selecting the best technique for real monitoring systems are still insufficient and often con tradictory. In our previous work, three classification tech niques were compared under different conditions of gas turbine diagnosis. The comparative analysis has shown that all these techniques yield practically the same accu racy for each comparison case. The present contribution considers a new classification technique, Probabilistic Neural Network (PNN), and we compare it with the tech niques previously examined. The results for all compari son cases show that the PNN is not inferior to the other techniques. We recommend choosing the PNN for real monitoring systems because it has an important advan tage of providing confidence estimation for every diagnos tic decision made.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gas Turbine Fault Diagnosis Using Probabilistic Neural Networks

Loboda

Olivares-Robles

2015

International Journal of Turbo &Amp; Jet-Engines

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“…The sensor fusion technologies using learning machine are developed, such as the integration of multiple types NNs, or combination of NN and fuzzy logic [32,33]. The essence of these fusion approaches are the data-based ones, and aero-thermodynamic characteristics of gas turbine engine is not taken into account [34]. These data-based sensor fusion methods would be still failed to new fault modes due to the lack of very training samples.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

et al. 2016

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Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster-Shaffer evidence theory (WDS) is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM) is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL) by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.

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“…Thus, methodologies for the improvement in HI determination accuracy are required [43,44,[49][50][51][52]. In particular, since the methodology makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Health State Determination: Methodology Approach and Field Application

Pinelli

Spina

Venturini

2012

International Journal of Rotating Machinery

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A reduction of gas turbine maintenance costs, together with the increase in machine availability and the reduction of management costs, is usually expected when gas turbine preventive maintenance is performed in parallel to on-condition maintenance. However, on-condition maintenance requires up-to-date knowledge of the machine health state. The gas turbine health state can be determined by means of Gas Path Analysis (GPA) techniques, which allow the calculation of machine health state indices, starting from measurements taken on the machine. Since the GPA technique makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability. In this paper, a comprehensive approach for both the measurement validation and health state determination of gas turbines is discussed, and its application to a 5 MW gas turbine working in a natural gas compression plant is presented.

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An Assessment Methodology for Data-Driven and Model-Based Techniques for Engine Health Monitoring

Cited by 15 publications

References 0 publications

Gas Turbine Fault Diagnosis Using Probabilistic Neural Networks

Gas Turbine Fault Diagnosis Using Probabilistic Neural Networks

A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

Gas Turbine Health State Determination: Methodology Approach and Field Application

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