Multiple-model based sensor fault diagnosis using hybrid kalman filter approach for nonlinear gas turbine engines

2013 ◽  
Author(s):  
Bahareh Pourbabaee ◽  
Nader Meskin ◽  
Khashayar Khorasani
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Multiple Model ◽  
Sensor Fault ◽  
Model Based ◽  
Filter Approach

Sensor fault diagnosis of gas turbine engines using an integrated scheme based on improved least squares support vector regression

2019 ◽  
Vol 234 (3) ◽  
pp. 607-623
Author(s):  
Yu Hu ◽  
Jietang Zhu ◽  
Zhensheng Sun ◽  
Lijia Gao
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Support Vector Regression ◽  
Gas Turbine ◽  
Turbine Engines ◽  
Support Vector ◽  
Gas Turbine Engines ◽  
Sensor Fault ◽  
Diagnosis System ◽  
Fault Diagnosis System

As the flight envelope is widening continuously and operational capability is improving sequentially, gas turbine engines are faced with new challenges of increased operation and maintenance requirements for efficiency, reliability, and safety. The measures for security and safety and the need for reducing the life cycle cost make it necessary to develop more accurate and efficient monitoring and diagnostic schemes for the health management of gas turbine components. Sensors along the gas path are one of the components in gas turbines that play a crucial role in turbofan engines owing to their safety criticality. Failures in sensor measurements often result in serious problems affecting flight safety and performance. Therefore, this study aims to develop an online diagnosis system for gas path sensor faults in a turbofan engine. The fault diagnosis system is designed and implemented using a genetic algorithm optimized recursive reduced least squares support vector regression algorithm. This method uses a reduction technique and recursion strategy to obtain a better generalization performance and sparseness, and exploits an improved genetic algorithm to choose the optimal model parameters for improving the training precision. The effectiveness of the sensor fault diagnosis system is then validated through typical fault modes of single and dual sensors.

Development of Fault Diagnosis and Failure Prediction Techniques for Small Gas Turbine Engines

2001 ◽  
Author(s):  
Craig R. Davison ◽  
A. M. Birk
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Operating Time ◽  
Turbine Engines ◽  
Time To Failure ◽  
Gas Turbine Engines ◽  
Ambient Conditions ◽  
Engine Operation ◽  
Maintenance Cycle ◽  
Prediction Techniques

A computer model of a gas turbine auxiliary power unit was produced to develop techniques for fault diagnosis and prediction of remaining life in small gas turbine engines. Due to the relatively low capital cost of small engines it is important that the techniques have both low capital and operating costs. Failing engine components were identified with fault maps, and an algorithm was developed for predicting the time to failure, based on the engine’s past operation. Simulating daily engine operation over a maintenance cycle tested the techniques for identification and prediction. The simulation included daily variations in ambient conditions, operating time, load, engine speed and operating environment, to determine the amount of degradation per day. The algorithm successfully adapted to the daily changes and corrected the operating point back to standard conditions to predict the time to failure.

Fault Diagnosis of Gas Turbine Engines From Transient Data

1989 ◽  
Vol 111 (2) ◽  
pp. 237-243 ◽  
Author(s):  
G. L. Merrington
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Sampling Rate ◽  
Diagnostic Information ◽  
Measurement Noise ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Input Output ◽  
Fault Parameters ◽  
Transient Data

The desirability of being able to extract relevant fault diagnostic information from transient gas turbine data records is discussed. A method is outlined for estimating the effects of unmeasured fault parameters from input/output measurements. The resultant sensitivity of the technique depends on the sampling rate and the measurement noise.

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