scholarly journals Early Fault Detection of Gas Turbine Hot Components Based on Exhaust Gas Temperature Profile Continuous Distribution Estimation

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5950
Author(s):  
Jinfu Liu ◽  
Mingliang Bai ◽  
Zhenhua Long ◽  
Jiao Liu ◽  
Yujia Ma ◽  
...  
Keyword(s):  
Fault Detection ◽  
Temperature Profile ◽  
Gas Turbine ◽  
Detection Method ◽  
Continuous Distribution ◽  
Detection Sensitivity ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Early Fault Detection ◽  
Exhaust Gas Temperature

Failures of the gas turbine hot components often cause catastrophic consequences. Early fault detection can detect the sign of fault occurrence at an early stage, improve availability and prevent serious incidents of the plant. Monitoring the variation of exhaust gas temperature (EGT) is an effective early fault detection method. Thus, a new gas turbine hot components early fault detection method is developed in this paper. By introducing a priori knowledge and quantum particle swarm optimization (QPSO), the exhaust gas temperature profile continuous distribution model is established with finite EGT measuring data. The method eliminates influences of operating and ambient condition changes and especially the gas swirl effect. The experiment reveals the presented method has higher fault detection sensitivity.

Early fault detection of gas turbine hot components under different ambient and operating conditions

2021 ◽  
pp. 095441002098689
Author(s):  
Jiao Liu ◽  
Jinfu Liu ◽  
Daren Yu ◽  
Zhongqi Wang ◽  
Weizhong Yan ◽  
...  
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Detection Method ◽  
Operating Conditions ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Spatial Correlations ◽  
Early Fault Detection ◽  
Exhaust Gas Temperature

Failure of hot components in gas turbines often causes catastrophic results. Early fault detection can prevent serious incidents and improve the availability. A novel early fault detection method of hot components is proposed in this article. Exhaust gas temperature is usually used as the indicator to detect the fault in the hot components, which is measured by several exhaust thermocouples with uniform distribution at the turbine exhaust section. The healthy hot components cause uniform exhaust gas temperature (EGT) profile, whereas the hot component faults could cause the uneven EGT profile. However, the temperature differences between different thermocouple readings are also affected by different ambient and operating conditions, and it sometimes has a greater influence on EGT than the faults. In this article, an accurate EGT model is presented to eliminate the influence of different ambient and operating conditions on EGT. Especially, the EGT profile swirl under different ambient and operating conditions is also included by considering the information of the thermocouples’ spatial correlations and the EGT profile swirl angle. Based on the developed EGT model, the detection performance of early fault detection of hot components in gas turbine is improved. The accuracy and effectiveness of the developed early fault detection method are evaluated by the real-world gas turbine data.

scholarly journals A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zhi-tao Wang ◽  
Ning-bo Zhao ◽  
Wei-ying Wang ◽  
Rui Tang ◽  
Shu-ying Li
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Clustering Algorithm ◽  
Support Vector ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Fcm Clustering ◽  
Gas Turbine Exhaust ◽  
Exhaust Gas Temperature ◽  
Turbine Exhaust

As an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT) can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM) clustering algorithm and support vector machine (SVM) classification model is proposed in this paper. Considering the distribution characteristics of gas turbine EGT, FCM clustering algorithm is used to realize clustering analysis and obtain the state pattern, on the basis of which the preclassification of EGT is completed. Then, SVM multiclassification model is designed to carry out the state pattern recognition and fault diagnosis. As an example, the historical monitoring data of EGT from an industrial gas turbine is analyzed and used to verify the performance of the fusion fault diagnosis approach presented in this paper. The results show that this approach can make full use of the unsupervised feature extraction ability of FCM clustering algorithm and the sample classification generalization properties of SVM multiclassification model, which offers an effective way to realize the online condition recognition and fault diagnosis of gas turbine EGT.

Early Fault Detection of Hot Components in Gas Turbines

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Liu Jinfu ◽  
Liu Jiao ◽  
Wan Jie ◽  
Wang Zhongqi ◽  
Yu Daren
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Working Environment ◽  
Detection Sensitivity ◽  
Normal Operation ◽  
Large Temperature ◽  
Ambient Conditions ◽  
Fisher Discriminant Analysis ◽  
Early Fault Detection

The working environment of hot components is the most adverse of all gas turbine components. Malfunction of hot components is often followed by catastrophic consequences. Early fault detection plays a significant role in detecting performance deterioration immediately and reducing unscheduled maintenance. In this paper, an early fault detection method is introduced to detect early fault symptoms of hot components in gas turbines. The exhaust gas temperature (EGT) is usually used to monitor the performance of the hot components. The EGT is measured by several thermocouples distributed equally at the outlet of the gas turbine. EGT profile is symmetrical when the unit is in normal operation. And the faults of hot components lead to large temperature differences between different thermocouple readings. However, interferences can potentially affect temperature differences, and sometimes, especially in the early stages of the fault, its influence can be even higher than that of the faults. To improve the detection sensitivity, the influence of interferences must be eliminated. The two main interferences investigated in this study are associated with the operating and ambient conditions, and the structure deviation of different combustion chambers caused by processing and installation errors. Based on the basic principles of gas turbines and Fisher discriminant analysis (FDA), a new detection indicator is presented that characterizes the intrinsic structure information of the hot components. Using this new indicator, the interferences involving the certainty and the uncertainty are suppressed and the sensitivity of early fault detection in gas turbine hot components is improved. The robustness and the sensitivity of the proposed method are verified by actual data from a Taurus 70 gas turbine produced by Solar Turbines.

Performance Characteristics of a Diesel Engine Fueled With Avio-Kerosene

2003 ◽  
Author(s):  
Giancarlo Chiatti ◽  
Ornella Chiavola
Keyword(s):  
Diesel Engine ◽  
Combustion Chamber ◽  
Gas Turbine ◽  
Power Output ◽  
Pressure Rise ◽  
Experimental Tests ◽  
Diesel Oil ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Exhaust Gas Temperature

A comparative series of experimental tests has been performed on a 4-stroke multi cylinder indirect injection diesel engine fueled with diesel oil, pure gas-turbine fuel and gas-turbine fuel with additives. The engine has been equipped aimed at monitoring both the overall performances and the variation with time of the pressure in the pre-combustion chamber. Some key parameters have been investigated at different engine speeds and loads (ignition delay, pressure rise in the pre-combustion chamber, power output, specific fuel consumption, exhaust gas temperature) and discussed results are presented.

scholarly journals Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2149 ◽  
Author(s):  
Jiao Liu ◽  
Jinfu Liu ◽  
Daren Yu ◽  
Myeongsu Kang ◽  
Weizhong Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Gas Turbine ◽  
Detection Sensitivity ◽  
Large Temperature ◽  
Gas Temperature ◽  
Ambient Conditions ◽  
Real World Data ◽  
The Impact

Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot components. Based on the distribution characteristics of EGT thermocouples, the circular padding method is developed in the CNN. The sensitivity of the developed method is verified by real-world data. Moreover, the developed method is visualized in detail. The visualization results reveal that the CNN effectively considers the influence of the EGT profile swirl.

Experimental Investigation on the Effects of Swirlers Configurations and Air Inlet Partitioning in a Partially Premixed Double High Swirl Gas Turbine Model Combustor

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Amir Mardani ◽  
Benyamin Asadi Rekabdarkolaei ◽  
Hamed Rezapour Rastaaghi
Keyword(s):  
Gas Turbine ◽  
Combustion Efficiency ◽  
Exhaust Gas ◽  
Liquefied Petroleum Gas ◽  
Gas Temperature ◽  
Air Inlet ◽  
Flow Channeling ◽  
German Aerospace ◽  
Exhaust Gas Temperature ◽  
Turbine Model

Abstract In this work, a double-high swirl gas turbine model combustor (GTMC) has been experimentally investigated to identify the effects of air partitioning and swirlers geometry on combustion characteristics in terms of flame stability, exhaust gas temperature, NOx generation, and combustion efficiency. This high swirl model combustor is originally developed in the German Aerospace Center (DLR) and known as GTMC and recently reconstructed at Sharif University's Combustion Laboratory (named as SGTMC). Here, SGTMC run for liquefied petroleum gas (LPG) fuel and air oxidizer at room temperature and atmospheric pressure. Eleven different burner geometries, M1–M11, are considered for the aims of this work. Furthermore, the effects of burner confinement are also investigated. The results show that under the confined state, the flame has a lower width and height than the unconfined one. Exchanging the swirlers of annular and central air inlets shows a more stable and lifted V type flame with almost zero levels of CO and CH4. In addition, measurement showed that the annular swirler removing leads to incomplete combustion. Moreover, an increment in discharged air velocity leads to more completed combustion and less pollutant exhaust gas but the attachment of flame to the burner hub. Strengthening the flow channeling is not reasonable in terms of emission aspects. Moreover, burner configuring to counterrotating swirlers leads to a more stable flame but with lower combustion efficiency. Among 11 test cases, the original configuration and the case of exchanging the swirlers of annular and central air inlets are the best choices in terms of combustion efficiency and stability. Measurements show the improvement of burner stability, 2–10%, due to inlet air preheating.

Gas-Turbine-Cycle District Heating/Cooling-Power System With Refrigerating Exhaust

2010 ◽  
Author(s):  
Branko Stankovic
Keyword(s):  
Power Generation ◽  
Gas Turbine ◽  
Heat Input ◽  
District Heating ◽  
Heat Output ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Ghg Emission ◽  
Exhaust Gas Temperature ◽  
Gas Turbine Cycle

A gas-turbine-cycle modification has been proposed, optimized primarily for (district) heating purposes, with a side-effect of obtaining gas-turbine exhaust gas at very low temperatures and potentially GHG-emission-free. Since its primary purpose is district heating without power generation, the associated gas-turbine-cycle equipment (compressors, turbines, heat exchangers) is typically arranged so that a maximum possible ratio of heat output and heat input is achieved. Whenever the heat ratio is greater than unity, that is, greater than 100% of the heat input, the exhaust gas temperature at the last gas-turbine exit is lower than atmospheric temperature. In other words, this means that it is possible to achieve greater heat output (or GT-cycle “waste heat”) than the heat input, at the “expense” of the cold GT exhaust gas (its internal energy). It is possible to arrange proposed GT-cycle modification in various configurations, such as: simple GT cycle, recuperated, intercooled, intercooled-recuperated, reheat-recuperated and intercooled-reheat-recuperated GT cycle. Maximum achievable ratio of heat output and heat input is estimated to about 1.15 (115%) and corresponding minimum GT exhaust gas temperature can be lower than the CO2 solidification temperature at atmospheric pressure (−78°C or 195 K or −108.4°F). This also means that the GT exhaust-gas stream could be entirely GHG-emission-free, without GHG-s like H2O and/or CO2, which could therefore be captured and sequestered in solid state, and in addition at very low refrigerating temperature. Such low-temperature GT exhaust gas could then be used for refrigeration purposes, or ultimately to refrigerate the Earth’s atmosphere and thus mitigate global-warming effects. The proposed GT-cycle heating system can operate also in the combined heating/cooling and power (CCHP) mode or in the stand-alone power generation mode using a combined-cycle configuration. In such operating modes/regimes, the heating part of the CHP system could still maintain its inherent advantages (achievement of the ratio of heat output and heat input greater than unity, potentially GHG-emission-free GT exhaust gas at refrigerating temperature levels), with CC thermal efficiencies only slightly lower than today’s typical values and with the CHP performance similar or better than modern GTCC or steam-turbine based CHP cycles.

Three- and Seven-Point Optimally Weighted Recursive Median Filters for Gas Turbine Diagnostics

2011 ◽  
Vol 133 (10) ◽  
Author(s):  
V. N. Guruprakash ◽  
Ranjan Ganguli
Keyword(s):  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Exhaust Gas ◽  
Window Length ◽  
Rotor Speed ◽  
Gas Temperature ◽  
Median Filters ◽  
Optimal Filters ◽  
Fuel Flow ◽  
Exhaust Gas Temperature

Measured health signals incorporate significant details about any malfunction in a gas turbine. The attenuation of noise and removal of outliers from these health signals while preserving important features is an important problem in gas turbine diagnostics. The measured health signals are a time series of sensor measurements such as the low rotor speed, high rotor speed, fuel flow, and exhaust gas temperature in a gas turbine. In this article, a comparative study is done by varying the window length of acausal and unsymmetrical weighted recursive median filters and numerical results for error minimization are obtained. It is found that optimal filters exist, which can be used for engines where data are available slowly (three-point filter) and rapidly (seven-point filter). These smoothing filters are proposed as preprocessors of measurement delta signals before subjecting them to fault detection and isolation algorithms.

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