Automatic Gas Turbine Matching Scheme Adaptation for Robust GPA Diagnostics

Stenfelt, Mikael; Zaccaria, Valentina; Kyprianidis, Konstantinos

doi:10.1115/gt2019-91018

Cited by 6 publications

(10 citation statements)

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“…To determine a baseline matching scheme, i.e., a system that is mathematically solvable and robust in the presence of measurement noise, a modified version of the framework presented in [49] is adopted. This ensures a functional setup is provided regarding numerical stability and convergence.…”

Section: Matching Schemementioning

confidence: 99%

“…This ensures a functional setup is provided regarding numerical stability and convergence. From the framework in [49], a matching scheme is obtained by discarding both measurements and health parameters if they have a too high correlation to any other parameter until a determined system is achieved. In this paper, there are two differences from this method.…”

Section: Matching Schemementioning

confidence: 99%

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Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Stenfelt

Kyprianidis

2022

Machines

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In gas turbines used for airplane propulsion, the number of sensors are kept at a minimum for accurate control and safe operation. Additionally, when data are communicated between the airplane main computer and the various subsystems, different systems may have different constraints and requirements regarding what data transmit. Early in the design process, these parameters are relatively easy to change, compared to a mature product. If the gas turbine diagnostic system is not considered early in the design process, it may lead to diagnostic functions having to operate with reduced amount of data. In this paper, a scenario where the diagnostic function cannot obtain airplane installation effects is considered. The installation effects in question is air intake pressure loss (pressure recovery), bleed flow and shaft power extraction. A framework is presented where the unknown installation effects are estimated based on available data through surrogate models, which is incorporated into the diagnostic framework. The method has been evaluated for a low-bypass turbofan with two different sensor suites. It has also been evaluated for two different diagnostic schemes, both determined and underdetermined. Results show that, compared to assuming a best-guess constant-bleed and shaft power, the proposed method reduce the RMS in health parameter estimation from 26% up to 80% for the selected health parameters. At the same time, the proposed method show the same degradation pattern as if the installation effects were known.

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Section: Matching Schemementioning

confidence: 99%

Section: Matching Schemementioning

confidence: 99%

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Stenfelt

Kyprianidis

2022

Machines

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“…The measurement subset concept supplements the outcome of the sensitivity and correlation analyses with visualization and priority. In another study, Stenfelt et al (2019) proposed a measurement selection method that automatically modifies the selection under sensor malfunction to ensure robust diagnostics. The authors emphasized that the use of correlated measurements in gas path diagnostics should be avoided.…”

Section: Gas Turbine Monitoring For Maintenance and Diagnosticsmentioning

confidence: 99%

Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements

et al. 2021

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The energy generation landscape is changing, pushed by stricter regulations for emissions control and green energy generation. The limitations of renewable energy sources, however, require flexible energy production sources to supplement them. Micro gas turbine based combined heat and power plants, which are used for domestic applications, can fill this gap if they become more reliable. This can be achieved with the use of an engine monitoring and diagnostics system: real-time engine condition monitoring and fault diagnostics results in reduced operating and maintenance costs and increased component and engine life. In order to allow the step change in the connection of small engines to the grid, a fleet monitoring system for micro gas turbines is required. A proposed framework combines a physics-based model and a data-driven model with machine learning capabilities for predicting system behavior, and includes a purpose-developed diagnostic tool for anomaly detection and classification for a multitude of engines. The framework has been implemented on a fleet of micro gas turbines and some of the lessons learned from the demonstration of the concept as well as key takeaways from the general literature are presented in this paper. The extension of fleet monitoring to optimal operation and production planning in relation to the needs of the grid will allow the micro gas turbines to fit in the future green energy system, connect to the grid, and trade in the energy market. The requirements on the system level for the widespread use of micro gas turbines in the energy system are addressed in the paper. A review of the current solutions in fleet monitoring and diagnostics, generally developed for larger engines, is included, with an outlook into a sustainable future.

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“…It is important to note that the Jacobian matrix is square, hence the same number of target measurement variables and deviation factors is necessary. An observable influence of each deviation factor on at least a measured variable is also a necessary condition as explained in (Stenfelt, Zaccaria, & Kyprianidis, 2019).…”

Section: Adaptive Gas Path Analysismentioning

confidence: 99%

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

Fentaye

Zaccaria

Kyprianidis

2021

IJPHM

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Advanced engine health monitoring and diagnostic systems greatly benefit users helping them avoid potentially expensive and time-consuming repairs by proactively identifying shifts in engine performance trends and proposing optimal maintenance decisions. Engine health deterioration can manifest itself in terms of rapid and gradual performance deviations. The former is due to a fault event that results in a short-term performance shift and is usually concentrated in a single component. Whereas the latter implies a gradual performance loss that develops slowly and simultaneously in all engine components over their lifetime due to wear and tear. An effective engine life-cycle monitoring and diagnostic system is therefore required to be capable of discriminating these two deterioration mechanisms followed by isolating and identifying the rapid fault accurately. In the proposed solution, this diagnostic problem is addressed through a combination of adaptive gas path analysis and artificial neural networks. The gas path analysis is applied to predict performance trends in the form of isentropic efficiency and flow capacity residuals that provide preliminary information about the deterioration type. Sets of neural network modules are trained to filter out noise in the measurements, discriminate rapid and gradual faults, and identify the nature of the root cause, in an integrated manner with the gas path analysis. The performance of the proposed integrated method has been demonstrated and validated based on performance data obtained from a three-shaft turbofan engine. The improvement achieved by the combined approach over the gas path analysis technique alone would strengthen the relevance and long-term impact of our proposed method in the gas turbine industry.

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Automatic Gas Turbine Matching Scheme Adaptation for Robust GPA Diagnostics

Cited by 6 publications

References 0 publications

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Estimation and Mitigation of Unknown Airplane Installation Effects on GPA Diagnostics

Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements

Discrimination of Rapid and Gradual Deterioration for an Enhanced Gas Turbine Life-cycle Monitoring and Diagnostics

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