Self-tuning routine alarm analysis of vibration signals in steam turbine generators

This paper presents a self-tuning framework for knowledge-based diagnosis of routine alarms in steam turbine generators. The techniques provide a novel basis for initialising and updating time series feature extraction parameters used in the automated decision support of vibration events due to operational transients. The data-driven nature of the algorithms allows for machine specific characteristics of individual turbines to be learned and reasoned about. The paper provides a case study illustrating the routine alarm paradigm and the applicability of systems using such techniques

Aeronautical engineering: A continuing bibliography, supplement 122

This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980

AIRCRAFT JET ENGINE CONDITION MONITORING THROUGH SYSTEM IDENTIFICATION BY USING GENETIC PROGRAMMING

In this thesis a new approach for aircraft jet engine condition monitoring is proposed based on system identification and by using Genetic Programming (GP). This approach consists of two fault detection and isolation parts. In the detection part, the relationship between the engine Exhaust Gas Temperature (EGT), as a major indicator of the engine health condition, and other engine parameters and operating conditions corresponding to different phases of the flight is modelled using the GP technique. Towards this end, flight characteristics are divided into several phases such as the take-off and the cruise. The GP scheme is then used to discover the structure of the interrelations among engine variables. The constructed model is then used to detect abrupt faults in the engine performance. For the isolation purpose, a hierarchical approach is proposed which narrows down the number of possible faults toward the target fault. The GP algorithm is then exploited to extract a series of nonlinear functions of the engine variables called fault indices. These indices attempt to magnify the signature of a fault in the engine by combining the effects of a fault on the engine parameters. These indices subsequently provide the necessary residuals for classifying the faults. The approaches developed in this thesis provide an effective strategy for inspecting the aircraft jet engine health condition without requiring any specific information on the engine internal characteristics. The main advantage of the proposed approaches over other data driven methods such as neural networks is that our approaches provide a simple and tangible mathematical model of the engine rather than a black box model. The performance of the proposed algorithms are demonstrated and illustrated by implementing them on a double spool jet engine data that is generated by using the Gas turbine Simulation Program (GSP) software

Aeronautical engineering: A continuing bibliography with indexes (supplement 272)

This bibliography lists 719 reports, articles, and other documents introduced into the NASA scientific and technical information system in November, 1991. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment, and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Aeronautical engineering: A continuing bibliography with indexes (supplement 238)

This bibliography lists 458 reports, articles, and other documents introduced into the NASA scientific and technical information system in March, 1989. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

NARX models for simulation of the start-up operation of a single-shaft gas turbine

In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy. The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data. The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation

Aeronautical engineering: A continuing bibliography with indexes (supplement 247)

This bibliography lists 437 reports, articles, and other documents introduced into the NASA scientific and technical information system in December, 1989. Subject coverage includes: design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Aeronautical engineering: A cumulative index to a continuing bibliography (supplement 274)

This publication is a cumulative index to the abstracts contained in supplements 262 through 273 of Aeronautical Engineering: A Continuing Bibliography. The bibliographic series is compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). Seven indexes are included: subject, personal author, corporate source, foreign technology, contract number, report number, and accession number

Modeling of jet engine abnormal conditions and detection using the artificial immune system paradigm

Previous research at WVU has yielded promising results in the detection of aircraft sub-systems malfunctions using the artificial immune system (AIS) paradigm. However, one aircraft component that requires improvement is the aircraft propulsion system. In this research effort, MAPSS, a non-real time low-bypass turbofan engine model distributed by NASA, has been linearized and interfaced with the WVU F-15 model and the WVU 6 degrees-of-freedom flight simulator to provide a more complex engine model and create more options for engine failure modeling and engine failure detection. A variety of engine actuator and sensor failures were modeled and implemented into the simulation environment. A detection scheme based on the AIS approach was developed for specific classes of failures including throttle, burner fuel flow valve, variable nozzle area actuator, variable mixer area actuator, low-pressure spool speed sensor, low-pressure turbine exit static pressure sensor, and mixer pressure ratio sensor.;A 5-dimensional feature hyper-space is determined to build the self within the AIS paradigm for abnormal condition detection purposes. The WVU AIS interactive design environment based on evolutionary algorithms was used for data processing, detector generation, and limited optimization. Flight simulation data for system development and testing was acquired through experiments in the WVU 6 degrees-of-freedom flight simulator over extended areas of the flight envelope. The AIS-based detection scheme was tested using both nominal and engine failure conditions and its performance evaluated in terms of detection rates and false alarms. As compared to the previous failure detection results, significant improvement has been demonstrated as well as excellent potential for detection of the newly modeled engine failures