The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study

2003 ◽  
Vol 125 (4) ◽  
pp. 917-924 ◽  
Author(s):  
A. J. Volponi ◽  
H. DePold ◽  
R. Ganguli ◽  
C. Daguang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Gas Turbine ◽  
Kalman Filters ◽  
Fault Isolation ◽  
Turbine Performance ◽  
Reasonable Degree ◽  
Engine System ◽  
Module Performance ◽  
Performance Diagnostics

The goal of gas turbine performance diagnositcs is to accurately detect, isolate, and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. The method has been applied to a wide variety of commercial and military engines in the three decades since its inception as a diagnostic tool and has enjoyed a reasonable degree of success. During that time many methodologies and implementations of the basic concept have been investigated ranging from the statistically based methods to those employing elements from the field of artificial intelligence. The two most publicized methods involve the use of either Kalman filters or artificial neural networks (ANN) as the primary vehicle for the fault isolation process. The present paper makes a comparison of these two techniques.

The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study

2000 ◽  
Author(s):  
Allan J. Volponi ◽  
Hans DePold ◽  
Ranjan Ganguli ◽  
Chen Daguang
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Gas Turbine ◽  
Engine Operation ◽  
The Past ◽  
Fuel Flow ◽  
Turbine Performance ◽  
Engine System ◽  
Module Performance ◽  
Performance Diagnostics

The goal of Gas Turbine Performance Diagnostics is to accurately detect, isolate and assess the changes in engine module performance, engine system malfunctions and instrumentation problems from knowledge of measured parameters taken along the engine’s gas path. Discernable shifts in engine speeds, temperatures, pressures, fuel flow, etc., provide the requisite information for determining the underlying shift in engine operation from a presumed nominal state. Historically, this type of analysis was performed through the use of a Kalman Filter or one of its derivatives to simultaneously estimate a plurality of engine faults. In the past decade, Artificial Neural Networks (ANN) have been employed as a pattern recognition device to accomplish the same task. Both methods have enjoyed a reasonable success.

Comparison of Linear and Non-Linear Gas Turbine Performance Diagnostics

2003 ◽  
Author(s):  
Ph. Kamboukos ◽  
K. Mathioudakis
Keyword(s):  
Gas Turbine ◽  
Condition Monitoring ◽  
Theoretical Background ◽  
Diagnostic Methods ◽  
Turbine Performance ◽  
Different Types ◽  
Non Linear ◽  
Computational Procedures ◽  
Monitoring And Diagnostics ◽  
Performance Diagnostics

The features of linear performance diagnostic methods are discussed, in comparison to methods based on full non-linear calculation of performance deviations, for the purpose of condition monitoring and diagnostics. First, the theoretical background of linear methods is overviewed to establish a relationship to the principles used by non-linear methods. Then computational procedures are discussed and compared. The effectiveness of determining component performance deviations by the two types of approaches is examined, on different types of diagnostic situations. A way of establishing criteria to define whether non-linear methods have to be employed is presented. An overall assessment of merits or weaknesses of the two types of methods is attempted, based on the results presented in the paper.

scholarly journals Gas Turbine Diagnosis Using a Fault Isolation Enhanced GPA

2004 ◽  
Author(s):  
Y. G. Li
Keyword(s):  
Path Analysis ◽  
Gas Turbine ◽  
Fault Isolation ◽  
Diagnostic Process ◽  
Matching Method ◽  
Performance Simulation ◽  
Turbine Engine ◽  
Turbine Performance ◽  
Path Component ◽  
Long Time

Gas Path Analysis (GPA) and its different derivatives have been developed for more than thirty years and used widely and successfully by many gas turbine manufacturers and operators. In gas turbine gas path component diagnosis, it has been recognized for a long time that GPA would be more successful if degraded components could be located. Unfortunately, only the deviation of measurable parameters is monitored in operation and information about the degraded components is normally not available. In this research, a two-step diagnostic approach is introduced, where a pattern matching method is used first and further developed to isolate degraded components; then Gas Path Analysis is applied to assess the quantity of degradation. A gas turbine performance simulation program, Cranfield University TURBOMATCH, has been modified to simulate the diagnostic process. A model gas turbine engine similar to Rolls-Royce aero AVON is used to test the effectiveness of the approach. It is found that the developed fault isolation method can isolate degraded components accurately and enhance the effectiveness of the quantitative assessment of the degradation with Gas Path Analysis (GPA) in gas turbine diagnostics.

Comparison of Linear and Nonlinear Gas Turbine Performance Diagnostics

2005 ◽  
Vol 127 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Ph. Kamboukos ◽  
K. Mathioudakis
Keyword(s):  
Gas Turbine ◽  
Theoretical Background ◽  
Diagnostic Methods ◽  
Nonlinear Methods ◽  
Turbine Performance ◽  
Different Types ◽  
Linear And Nonlinear ◽  
Computational Procedures ◽  
Monitoring And Diagnostics ◽  
Performance Diagnostics

The features of linear performance diagnostic methods are discussed, in comparison to methods based on full nonlinear calculation of performance deviations, for the purpose of condition monitoring and diagnostics. First, the theoretical background of linear methods is reviewed to establish a relationship to the principles used by nonlinear methods. Then computational procedures are discussed and compared. The effectiveness of determining component performance deviations by the two types of approaches is examined, on different types of diagnostic situations. A way of establishing criteria to define whether nonlinear methods have to be employed is presented. An overall assessment of merits or weaknesses of the two types of methods is attempted, based on the results presented in the paper.

Uncertainty Reduction in Gas Turbine Performance Diagnostics by Accounting for Humidity Effects

2001 ◽  
Author(s):  
K. Mathioudakis ◽  
A. Tsalavoutas
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Ambient Conditions ◽  
Ambient Temperatures ◽  
Turbine Performance ◽  
Industrial Gas Turbines ◽  
Industrial Gas Turbine ◽  
The Impact ◽  
Performance Diagnostics ◽  
Health Parameters

The paper presents an analysis of the effect of ambient humidity on the performance of industrial gas turbines and examines the impact of humidity on methods used for engine condition assessment and fault diagnostics. First, the way of incorporating the effect of humidity into a computer model of gas turbine performance is described. The model is then used to derive parameters indicative of the “health” of a gas turbine and thus diagnose the presence of deterioration or faults. The impact of humidity magnitude on the values of these health parameters is studied and the uncertainty introduced, if humidity is not taken into account, is assessed. It is shown that the magnitude of the effect of humidity depends on ambient conditions and is more severe for higher ambient temperatures. Data from an industrial gas turbine are presented to demonstrate these effects and to show that if humidity is appropriately taken into account, the uncertainty in the estimation of health parameters is reduced

Gas turbine performance monitoring based on extended information fusion filter

2018 ◽  
Vol 233 (2) ◽  
pp. 483-497 ◽  
Author(s):  
Feng Lu ◽  
Yihuan Huang ◽  
Jinquan Huang ◽  
Xiaojie Qiu
Keyword(s):  
State Estimation ◽  
Gas Turbine ◽  
Information Fusion ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Maintenance Cost ◽  
Global State ◽  
State Estimate ◽  
Turbine Performance ◽  
Information Filters

Performance monitoring is a critical issue for gas turbine engine for improving the operation safety and reducing the maintenance cost. With regard to this, variants of Kalman-filters-based state estimation have been employed to detect gas turbine performance, but the classical centralized Kalman filters are subject to heavy computational effort and poor fault tolerance. A novel nonlinear fusion filter algorithm using information description with distributed architecture is proposed and applied to gas turbine performance monitoring. This methodology is developed from federated Kalman filter, and a bank of local extended information filters and one information mixer are combined with extended information fusion filter. The local state estimates and covariance calculated in parallel by the local extended information filters are integrated in the information mixer to yield a global state estimate. The global state estimate of nonlinear system is fed back to the local filters with weighted factor for next iteration. The aim of the proposed methodology is to reduce the computational efforts of state estimation and improve robustness to sensor faults in cases of gas turbine performance monitoring. The simulation results on a turbofan engine confirm the extended information fusion filter's effective capabilities in comparison to the general central ones.

Fusing unscented Kalman filter for performance monitoring and fault accommodation in gas turbine

2016 ◽  
Vol 232 (3) ◽  
pp. 556-570 ◽  
Author(s):  
Feng Lu ◽  
Yafan Wang ◽  
Jinquan Huang ◽  
Yihuan Huang ◽  
Xiaojie Qiu
Keyword(s):  
Kalman Filter ◽  
Gas Turbine ◽  
Extended Kalman Filter ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Unscented Kalman Filter ◽  
Estimation Accuracy ◽  
Health State ◽  
Sensor Failure ◽  
Turbine Performance

The Kalman filter is widely utilized for gas turbine health monitoring due to its simplicity, robustness, and suitability for real-time implementations. The most common Kalman filter for linear systems is linearized Kalman filter, and for nonlinear systems are extended Kalman filter and unscented Kalman filter. These algorithms have proven their capabilities to estimate gas turbine performance variations with a good accuracy, and the studies are done provided that all sensor measurements are available. In this paper, a nonlinear fusion approach with consistent diagnostic mechanism based on unscented Kalman filter is proposed, especially for gas turbine performance monitoring in the case of sensor failure. The architecture of fusion method comprises a set of local unscented Kalman filters and an information mixer. The local unscented Kalman filters are utilized to estimate health parameters of various component combinations, and the results are then transferred to the mixer for the integrated estimation of global health state in fusion structure. The consistent fault diagnosis and isolation logic is designed based on the fusion architecture and combined with the fusing unscented Kalman filter, called an improved fusing unscented Kalman filter. A systematic comparison of the generic linearized Kalman filter, extended Kalman filter, and unscented Kalman filter to their fusion filter kinds is presented for engine health estimation of gradual deterioration and abrupt fault. The studies show that the fusing unscented Kalman filter evidently outperforms the fusing linearized Kalman filter and fusing extended Kalman filter, while the fusing Kalman filters have slightly better estimation accuracy than the basic Kalman filters. In addition, the proposed methodology can reach the reliable performance monitoring with measurement uncertainty while the conventional Kalman filters collapse.

Uncertainty Reduction in Gas Turbine Performance Diagnostics by Accounting for Humidity Effects

2002 ◽  
Vol 124 (4) ◽  
pp. 801-808 ◽  
Author(s):  
K. Mathioudakis ◽  
T. Tsalavoutas
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Ambient Conditions ◽  
Ambient Temperatures ◽  
Turbine Performance ◽  
Industrial Gas Turbines ◽  
Industrial Gas Turbine ◽  
The Impact ◽  
Performance Diagnostics ◽  
Health Parameters

The paper presents an analysis of the effect of ambient humidity on the performance of industrial gas turbines and examines the impact of humidity on methods used for engine condition assessment and fault diagnostics. First, the way of incorporating the effect of humidity into a computer model of gas turbine performance is described. The model is then used to derive parameters indicative of the “health” of a gas turbine and thus diagnose the presence of deterioration or faults. The impact of humidity magnitude on the values of these health parameters is studied and the uncertainty introduced, if humidity is not taken into account, is assessed. It is shown that the magnitude of the effect of humidity depends on ambient conditions and is more severe for higher ambient temperatures. Data from an industrial gas turbine are presented to demonstrate these effects and to show that if humidity is appropriately taken into account, the uncertainty in the estimation of health parameters is reduced.

scholarly journals A sequential model-based approach for gas turbine performance diagnostics

Energy ◽  
2020 ◽  
pp. 119657
Author(s):  
Yu-Zhi Chen ◽  
Xu-Dong Zhao ◽  
Heng-Chao Xiang ◽  
Elias Tsoutsanis
Keyword(s):  
Gas Turbine ◽  
Sequential Model ◽  
Model Based ◽  
Turbine Performance ◽  
Performance Diagnostics

Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

2020 ◽  
pp. 1-11
Author(s):  
Wenjuan Ma ◽  
Xuesi Zhao ◽  
Yuxiu Guo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Education Reform ◽  
Traditional Education ◽  
Machine Learning Algorithms ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Fish School ◽  
Tlbo Algorithm ◽  
Neural Network System

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.

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