Diagnostics-Oriented Modelling of Micro Gas Turbines for Fleet Monitoring and Maintenance Optimization

Rahman, Moksadur; Zaccaria, Valentina; Zhao, Xin; Kyprianidis, Konstantinos

doi:10.3390/pr6110216

Cited by 29 publications

(17 citation statements)

References 34 publications

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“…Moreover, the measurements often suffer from poor accuracy due to the use of low cost sensors. Rahman et al (2018) presented a hybrid approach for micro gas turbine diagnostics by combining gas path analysis and multiple linear regression. The authors used five different measurements (i.e.…”

Section: Gas Turbine Monitoring For Maintenance and Diagnosticsmentioning

confidence: 99%

“…The diagnostics scheme can be used to analyze installation effects on engine performance, which differentiate the operation of different units and can also be used to monitor engine performance degradation over time. The proposed diagnostics scheme was tested on artificially simulated faults in Rahman et al (2018). A data-driven approach for diagnostics was applied on real engine data collected from the field unit (Olsson et al, 2021).…”

Section: Application To Field Datamentioning

confidence: 99%

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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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Section: Gas Turbine Monitoring For Maintenance and Diagnosticsmentioning

confidence: 99%

Section: Application To Field Datamentioning

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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“…Rahman et al [36] recently presented a comprehensive study on the utilization of exchange rate analysis for a diagnostics-oriented modelling approach of micro gas turbines for fleet monitoring and maintenance optimization [36]. They used an exchange rate analysis to develop a scheme for fault identification and finally presented the correlation coefficients between exchange rates and signatures for their case studies with a single fault.…”

Section: Exchange Ratesmentioning

confidence: 99%

Exchange Rate Analysis for Ultra High Bypass Ratio Geared Turbofan Engines

et al. 2020

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This paper investigates the impact of thermal management methods on the design point and synthesis exchange rates of an ultra-high bypass ratio geared turbofan engine. In a typical thermal management system, where heat is managed by means of heat exchangers that transfer engine waste heat into oil, air, or fuel. However, the utilization of air–oil and fuel–oil heat exchangers has an adverse impact on engine performance. This paper investigates the impact on and engine’s specific fuel consumption and summarizes it into common exchange rates for different thermal management configurations. The results show that any pressure loss in the bypass duct results in a severe specific fuel consumption penalty (an increase of 1% pressure loss in the bypass duct causes a 2% specific fuel consumption increase at cruise conditions). In addition, quite severe is the impact of extracting air from the gas path, particularly when the bleed location is in the bypass duct or the high-pressure compressor. It is also found that the utilization of a fuel–oil heat exchanger improves the specific fuel consumption at a higher rate than an air–oil heat exchanger. For the performance characteristics of the examined engine, the specific fuel consumption benefit with the former is 1.33%, while for the latter it is 0.38%.

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“…Using dispatchable DG systems like fuel cells and microturbines, with their low emissions, high efficiency and fuel flexibility, has emerged as one of the promising contributors to address this issue [1]. However, the main concern in these systems is to sustain a high availability and reliability while minimizing the maintenance cost [2]. This concern has become even more critical than before, as these units will more often be operated in transient conditions to balance the variable renewables, which, consequently, will lead to lifetime reduction of the equipment [3].…”

Section: Introductionmentioning

confidence: 99%

“…Primarily, there are two main different approaches to develop a model for monitoring systems, namely the physics-based and data-driven approaches. Physics-based approaches [2,5,6], which are derived from the first principle, are usually complex, and their accuracy decreases as the system complexity and modeling uncertainties increase. Moreover, the increase of the model complexities combined with the limitations of the mathematical techniques decreases their reliability for real-time monitoring tasks.…”

Section: Introductionmentioning

confidence: 99%

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

et al. 2020

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To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.

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Diagnostics-Oriented Modelling of Micro Gas Turbines for Fleet Monitoring and Maintenance Optimization

Cited by 29 publications

References 34 publications

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

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

Exchange Rate Analysis for Ultra High Bypass Ratio Geared Turbofan Engines

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

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