Robust Estimation and Fault Diagnostics for Aircraft Engines with Uncertain Model Data

Yedavalli, Rama K.

doi:10.1109/acc.2007.4283156

Cited by 5 publications

(4 citation statements)

References 115 publications

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“…At the end of the last decade three important works about prognostic activity in the industry have appeared [7], [9] and [10]. In the aviation industry, in 2011, [11] prepared a study on operational management in the industry which refers to concerns about prognostic activities.…”

Section: Aircraft Engine Maintenance Problemmentioning

confidence: 99%

“…The followings works shows different approaches to aircraft engines: [8] in 1993 refers to the advantages of the application of neural networks in monitoring an aircraft engine's condition; [9] in 2005 describes a model which would be able as to predict the residual life of an aircraft engine, using the Principal Components Analyses (PCA) technique in oil data; in 2007 two works distinguished themselvesone about the diagnosis of engine failures [10], the other about advanced estimation in engines [11]; in 2009 more studies appeared about the parameters of aircraft engines and their extreme values [12] as well as forms of classifying their faults from the perspective of diagnosis [13]; the following year [14] studied one important aircraft engine parameter, Exhaust Gas Temperature (EGT). They used three different tools: Self Organizing Map, Vector Machine Experts and Genetic Algorithm.…”

Section: Aircraft Engine Maintenance Problemmentioning

confidence: 99%

See 1 more Smart Citation

Selection of instances in Condition Based Monitoring: the case of aircraft engines

Fernandes¹,

Henriques²,

Lobo³

2014

Proceedings of Maintenance Performance Measurement and Management (MPMM) Conference 2014

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show abstract

Section: Aircraft Engine Maintenance Problemmentioning

confidence: 99%

Section: Aircraft Engine Maintenance Problemmentioning

confidence: 99%

Selection of instances in Condition Based Monitoring: the case of aircraft engines

Fernandes¹,

Henriques²,

Lobo³

2014

Proceedings of Maintenance Performance Measurement and Management (MPMM) Conference 2014

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show abstract

“…Aero-engine in the process of operation, affected by high temperature, high speed working environment,internal gravity larger, cause attenuation of the capabilities of pneumatic components, the engine performance decline. Machine performance parameters compared with a series of thresholds, it is not enough to understand the engine health state internal development, can't effectively achieve the engine performance parameters of the gradient analysis [3][4][5] . Areo-engine is a complex nonlinear system, its complex internal flow field, influenced each other in various parts of the system, which difficult to monitor and calculate, and the actual measurement of feature information or characteristic parameters is also changes with time, some even changes randomly [6] .Some scholars have been in accord with the sequence based on the joint entropy done a certain amount of exploration and research, on this basis, the entropy and many goals optimization are introduced into the aircraft engine health monitoring, to make up for deficiencies by the joint entropy of the aircraft engine to obtain more accurate overall health trend.…”

mentioning

confidence: 99%

Kolmogorov Entropy Optimization in the Aeroengine Health Monitoring

Shi

Song

2012

AMR

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Kolmogorov entropy theory was introduced into this paper for time series of one-parameter maximum likelihood estimation of the entropy K, combined with practical applications to aeroengine health monitoring. K entropy value was used to describe the disorder degree of aeroengine parts performance parameters, analyzing the health status and evolutive trend of aeroengine components. Optimization base on K entropy was used to analyze muli-parameter time series trend of K entropy, according to preferred status indicator changes analyzes the overall health of aeroengine. Experimental demonstration on the actual flight data can be more accurately reflected the health of aeroengine,have some significance to fault diagnosis and repair of aeroengine

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“…State space models of the engine with fixed threshold detection logic 10,11 or artificial neural networks (ANN) classifiers applied to linear model residuals 12 have been used for engine actuator and sensor fault detection. ANN have also been used [13][14][15] to model the plant for detection purposes. Fuzzy logic was proposed 16 to enhance a set of fault specific non-linear engine model for health monitoring.…”

mentioning

confidence: 99%

Development of a Detection Scheme for Aircraft Engine Failures Based on the Artificial Immune System Paradigm

Perhinschi

Porter

Moncayo

et al. 2010

AIAA Guidance, Navigation, and Control Conference

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A detection scheme based on the artificial immune system (AIS) paradigm was developed for specific classes of aircraft jet engine 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. The Modular Aero-Propulsion System Simulation model developed by NASA has been linearized and interfaced with a supersonic fighter aircraft model and a motion-based flight simulator to provide the adequate framework for the development and testing of the detection scheme. A variety of aircraft engine actuator and sensor failures were modeled and implemented into this simulation environment. A 5-dimensional feature hyper-space was determined to build the "self" within the AIS paradigm for abnormal condition detection purposes. The AIS interactive design environment based on evolutionary algorithms developed at West Virginia University (WVU) was used for data processing, detector generation, and optimization. Flight simulation data for system development and testing were 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. Results show that the AIS-based approach has excellent potential for the detection of all the classes of engine failures considered. I.

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Robust Estimation and Fault Diagnostics for Aircraft Engines with Uncertain Model Data

Cited by 5 publications

References 115 publications

Selection of instances in Condition Based Monitoring: the case of aircraft engines

Selection of instances in Condition Based Monitoring: the case of aircraft engines

Kolmogorov Entropy Optimization in the Aeroengine Health Monitoring

Development of a Detection Scheme for Aircraft Engine Failures Based on the Artificial Immune System Paradigm

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