Advanced diagnostics and prognostics for engine health monitoring

Babbar, Ashish; Syrmos, V.L.; Ortiz, Estefan; Arita, Michael M.

doi:10.1109/aero.2009.4839657

Cited by 31 publications

(16 citation statements)

References 9 publications

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“…Figure 5: Architecture of the proposed neural model. [26]. By differential analysis, it is aimed to compare EGT value obtained from ANN with real EGT value coming from the sensor of the aircraft and to easily notice when there is any problem.…”

Section: Modeling Of the Engine Health Monitoring Program In Matlab Smentioning

confidence: 99%

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Yildirim

Bulent

2018

International Journal of Aerospace Engineering

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Modern condition monitoring-based methods are used to reduce maintenance costs, increase aircraft safety, and reduce fuel consumption. In the literature, parameters such as engine fan speeds, vibration, oil pressure, oil temperature, exhaust gas temperature (EGT), and fuel flow are used to determine performance deterioration in gas turbine engines. In this study, a new model was developed to get information about the gas turbine engine's condition. For this model, multiple regression analysis was carried out to determine the effect of the flight parameters on the EGT parameter and the artificial neural network (ANN) method was used in the identification of EGT parameter. At the end of the study, a network that predicts the EGT parameter with the smallest margin of error has been developed.

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Section: Modeling Of the Engine Health Monitoring Program In Matlab Smentioning

confidence: 99%

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Yildirim

Bulent

2018

International Journal of Aerospace Engineering

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“…For this purpose, a priori probability distributions and actual measurements are used to assess health state and predict impending failures of on-board equipment. The literature on PHM solutions for aeronautical components comprises a wide range of applications, such as the monitoring of valves [2], pumps [3], engines [4] and electronic devices [5].…”

Section: Introductionmentioning

confidence: 99%

PHM-based Multi-UAV task assignment

Medeiros

Rodrigues

Santos

et al. 2014

2014 IEEE International Systems Conference Proceedings

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This paper is relating to the application of Integrated Vehicle Health Management (IVHM) concepts based on Prognostics and Health Monitoring (PHM) techniques to Multi-UAV systems. Considering UAV as a mission critical system, it is expected and required to accomplish its operational objectives with minimal unscheduled interruptions. So that, it does make sense for UAV to take advantage of those techniques as enablers for the readiness of multi-UAV. The main goal of this paper is to apply information from a PHM system to support decision making through an IVHM framework. PHM system information, in this case, comprises UAV remaining useful life (RUL) estimations. UAV RUL is computed by means of a fault tree analysis that it is fed by a distribution function from a probability density function relating time and failure probability for each UAV critical components. The IVHM framework, in this case, it is the task assignment based on UAV health condition (RUL information) using the Receding Horizon Task Assignment (RHTA) algorithm. The study case was developed considering a team of electrical small UAVs and pitch control system was chosen as the critical system.

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“…Modern commercial aircraft require advanced prognostic and diagnostic schemes to determine engine performance in an effort to reduce operational and maintenance costs as well as limit aircrafts' down time [1]. Commercial airliners are increasingly practicing Condition Based Maintenance (CBM) where decisions on maintenance actions are based on the aircraft's actual conditions reflected in sensor readings.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have shown that a sequence consisting of the average value per flight for a parameter can be used to diagnose engine faults. Abnormal values within the sequence coincide with fault instances occurring in engine components associated with that parameter [1], [3]. Learning the behavior of the statistics and the ability to forecast their future values will help the maintainer to more effectively plan their maintenance schedules, resulting in reduction in costs.…”

Section: Introductionmentioning

confidence: 99%

Forecasting gas turbine Exhaust Gas Temperatures using Support Vector Machine Experts and Genetic Algorithm

Yukitomo

Syrmos

2010

18th Mediterranean Conference on Control and Automation, MED'10

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One aspect of modern commercial aircraft engine maintenance involves monitoring recorded engine parameters. When these parameters exceed their respective threshold tolerances, appropriate maintenance actions are taken. Reducing these unscheduled maintenance actions would allow maintainers to more effectively plan their maintenance schedules which help in the reduction of costs. One way of accomplishing this is to learn the behavior of the statistics of parameters and the ability to reliably forecast their future values. The parameter studied in this paper is the Exhaust Gas Temperature. A hybrid algorithm called Support Vector Machine Experts and Genetic Algorithm is applied to the task of forecasting a statistic of EGT. The algorithm involves first partitioning the input space according to similar training examples through a procedure that uses a Self Organizing Map. A SVM is regressed onto each partition and the optimal parameters for SVM are found using Genetic Algorithm. This algorithm is shown to have an improved mean absolute error when forecasting EGT values over a single support vector regression machine. The study was conducted for both engines of two Boeing 737 commercial aircrafts.978-1-4244-8092-0/10/$26.00 ©2010 IEEE

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Advanced diagnostics and prognostics for engine health monitoring

Cited by 31 publications

References 9 publications

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

Aircraft Gas Turbine Engine Health Monitoring System by Real Flight Data

PHM-based Multi-UAV task assignment

Forecasting gas turbine Exhaust Gas Temperatures using Support Vector Machine Experts and Genetic Algorithm

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