Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

2011 ◽  
Vol 28 (4) ◽  
Author(s):  
Changduk Kong ◽  
Semyeong Lim
Keyword(s):  
Performance Model ◽  
Fault Diagnostics ◽  
Artificial Intelligent ◽  
Turboprop Engine ◽  
Intelligent Methods

Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

2011 ◽  
Author(s):  
Changduk Kong ◽  
Semyeong Lim
Keyword(s):  
Fuzzy Logic ◽  
Diagnostic System ◽  
Engine Performance ◽  
Performance Model ◽  
Performance Parameters ◽  
Artificial Intelligent ◽  
Turboprop Engine ◽  
Base Engine ◽  
Intelligent Methods ◽  
Learning Data

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Recently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

2012 ◽  
Author(s):  
Changduk Kong ◽  
Semyeong Lim ◽  
Keonwoo Kim
Keyword(s):  
Neural Networks ◽  
Diagnostic System ◽  
Engine Performance ◽  
Performance Model ◽  
Performance Data ◽  
Turboprop Engine ◽  
The Neural Networks ◽  
Fault Diagnostic ◽  
Intelligent Methods ◽  
Learning Data

Recently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks, Fuzzy Logic and Genetic Algorithms have been studied to improve the model based engine diagnostic methods. Among them the Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if only use of the Neural Networks. In addition, it has a very complex structure due to finding effectively faults of single type faults and multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP(Feed Forward Back Propagation) is used. In order to user’s friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB. The proposed program is verified by application of several case studies having the arbitrary implanted engine component faults as well as real engine performance data.

scholarly journals Classification of older adults undergoing two dual-task training protocols based on artificial intelligent methods

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Rebeca Sarai ◽  
Francis Trombini-Souza ◽  
Vitoria Thaysa Gomes De Moura ◽  
Rafael Caldas ◽  
Fernando Buarque
Keyword(s):  
Older Adults ◽  
Dual Task ◽  
Artificial Intelligent ◽  
Task Training ◽  
Dual Task Training ◽  
Intelligent Methods ◽  
Training Protocols

Potential of Micro Turbine Based Propulsion Systems for Civil UAVs: A Case Study

2016 ◽  
Author(s):  
A. Marcellan ◽  
W. P. J. Visser ◽  
P. Colonna
Keyword(s):  
Scale Effects ◽  
Pressure Ratio ◽  
Performance Model ◽  
Inlet Temperature ◽  
Propulsion Systems ◽  
University Of Technology ◽  
Turboprop Engine ◽  
Component Efficiency ◽  
Micro Turbine ◽  
Technological Improvements

There is a high potential for civil applications of Unmanned Aerial Vehicles (UAV) in areas such as goods transport, telecommunication, remote monitoring and sensing, surveillance, search and rescue, and disaster management. Developments in areas such as telecommunication, control and information technology offer opportunities for long range remotely or automatically piloted missions. This requires efficient and light-weight small propulsion systems. The potential of turboprop propulsion for civil UAVs using micro turbine technology has been explored and compared with existing concepts, such as piston engine driven propellers. Different propulsion concepts have been analyzed and the application areas where advanced turboprops would be superior to other systems such as reciprocating engines and electric motors, identified. However, turboprop engines of the small power capacity required for the aircraft concepts and missions considered are not currently available with competitive performance. A conceptual design study of a micro turboprop engine has been performed by downscaling an existing reference engine. Scale effects on efficiency have been taken into account, as well as effects of technological progress. Engine cycle optimization has been carried out and the effects of turbine inlet temperature, compressor pressure ratio, engine size, and component efficiency have been investigated. An aerodynamic and flight performance model of a baseline UAV has been developed in order to predict mission performance. This model has been coupled to a turboprop model to evaluate system performance with different engine configurations for the selected mission. The outcome of the study provides information about the technological improvements in terms of cycle efficiency required to make the micro-turboprop a competitive solution. The Propulsion and Power group of Delft University of Technology will pursue these R&D goals in an attempt to contribute to the development of civil UAV technology.

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