A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

2016 ◽  
Vol 33 (3) ◽  
Author(s):  
Feng Lu ◽  
Yafan Wang ◽  
Jinquan Huang ◽  
Qihang Wang
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filter ◽  
Fault Diagnosis ◽  
Fitness Function ◽  
Performance Degradation ◽  
Estimation Accuracy ◽  
Adaptive Genetic Algorithm ◽  
Turbofan Engine ◽  
Gas Path Fault ◽  
Fault Mode

AbstractA hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Gas Path Fault Diagnosis of a Turbofan Engine From Transient Data Using Artificial Neural Networks

2003 ◽  
Author(s):  
S. O. T. Ogaji ◽  
Y. G. Li ◽  
S. Sampath ◽  
R. Singh
Keyword(s):  
Neural Networks ◽  
High Pressure ◽  
Artificial Neural Networks ◽  
Steady State ◽  
Fault Diagnosis ◽  
Turbofan Engine ◽  
Transient Data ◽  
Gas Path Fault ◽  
Pressure Compressor ◽  
Trained Network

Transient and steady state data may contain the same essential fault information but some faults have been shown to be more easily detectable from transient data because the transient records provide significant diagnostic content especially as the fault effects are magnified under transient. Various traditional and conventional techniques such as fault trees, fault matrixes, gas path analysis and its variants have been applied to gas path fault diagnosis of gas turbines. Recently, artificial intelligence techniques such as artificial neural networks (ANN) as well as optimization techniques such as genetic algorithm (GA) are being explored for fault diagnosis activities. In this paper, a novel approach to gas path fault diagnosis is proposed. The method involves the use of ANN with engine transient data. A set of nested neural networks designed to estimate independent parameter (efficiencies and flow capacities) changes due to faults within single or multiple components of a turbofan engine are presented. The approach involves classification and approximation type networks. Measurements from the engine are first assessed by a trained network and if a fault is diagnosed, are then classified into two groups — those originating from sensor faults and those from component faults, by another trained network. Other trained networks continue the fault isolation process and finally the magnitude of the fault(s) is quantified. A computer simulation of the process shows that results from a batched process of these networks can be obtained in less than three seconds. Four of the gas path components — intermediate pressure compressor (IPC), high pressure compressor (HPC), high pressure turbine (HPT) and low pressure turbine (LPT) — and measurements from eight sensors are considered. Sensor noise and bias are also considered in this analysis. The comparison of fault signatures from a steady state and transient process show that diagnosis with transient data can improve the accuracy of gas turbine fault diagnosis.

scholarly journals 5D Parameter Estimation of Near-Field Sources Using Hybrid Evolutionary Computational Techniques

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Fawad Zaman ◽  
Ijaz Mansoor Qureshi
Keyword(s):  
Genetic Algorithm ◽  
Interior Point ◽  
Fitness Function ◽  
Near Field ◽  
Pattern Search ◽  
Estimation Accuracy ◽  
Computational Techniques ◽  
Computational Technique ◽  
Interior Point Algorithm ◽  
Validity And Reliability

Hybrid evolutionary computational technique is developed to jointly estimate the amplitude, frequency, range, and 2D direction of arrival (elevation and azimuth angles) of near-field sources impinging on centrosymmetric cross array. Specifically, genetic algorithm is used as a global optimizer, whereas pattern search and interior point algorithms are employed as rapid local search optimizers. For this, a new multiobjective fitness function is constructed, which is the combination of mean square error and correlation between the normalized desired and estimated vectors. The performance of the proposed hybrid scheme is compared not only with the individual responses of genetic algorithm, interior point algorithm, and pattern search, but also with the existing traditional techniques. The proposed schemes produced fairly good results in terms of estimation accuracy, convergence rate, and robustness against noise. A large number of Monte-Carlo simulations are carried out to test out the validity and reliability of each scheme.

Information-Applied Technology in the Model of RBF Neural Network Based on Adaptive Genetic Algorithm for Analog Circuit Fault Diagnosis

2014 ◽  
Vol 540 ◽  
pp. 456-459
Author(s):  
Hu Cheng Zhao ◽  
Hao Lin Cui ◽  
Zhi Bin Chen
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Analog Circuit ◽  
Rbf Neural Network ◽  
Adaptive Genetic Algorithm ◽  
Connection Weight ◽  
Proposed Model ◽  
Applied Technology ◽  
Circuit Fault Diagnosis ◽  
Crossover And Mutation

To obtain the improvement of analog circuit fault diagnosis, a RBF diagnosis model based on an Adaptive Genetic Algorithm (AGA) is proposed. First an adaptive mechanism about crossover and mutation probability is introduced into the traditional genetic algorithm, and then AGA algorithm is used to optimize the network parameters such as center, width and connection weight. The experiment simulation indicates that the proposed model has exact diagnosis characteristic.

A Genetic Algorithm for the Production Policy in a Supply Chain under Discrete Demand and Time-Vary Cost

2013 ◽  
Vol 321-324 ◽  
pp. 2137-2140 ◽  
Author(s):  
Bing Chang Ouyang
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Fitness Function ◽  
Optimal Solution ◽  
Just In Time ◽  
Adaptive Genetic Algorithm ◽  
Production Policy ◽  
Production Scheme ◽  
Distribution Requirement ◽  
Local Optimal Solution

Considering discrete demand and time-vary unit production cost under a foreseeable time horizon, this study presents an adaptive genetic algorithm to determine the production policy for one manufacturer supplying single item to multiple warehouses in a supply chain environment. Based on Distribution Requirement Planning (DRP) and Just in Time (JIT) delivery policy, we assume each gene in chromosome represents a period. Standard GA operators are used to generate new populations. These populations are evaluated by a fitness function using the total cost of production scheme. An explicit procedure for obtaining the local optimal solution is provided.

Fault Diagnosis of a Two Spool Turbo-Fan Engine Using Transient Data: A Genetic Algorithm Approach

2003 ◽  
Author(s):  
Suresh Sampath ◽  
Y. G. Li ◽  
S. O. T. Ogaji ◽  
Riti Singh
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Simulated Data ◽  
Operating Conditions ◽  
Turbofan Engine ◽  
Transient Model ◽  
Turbine Performance ◽  
Genetic Algorithm Approach ◽  
Transient Data ◽  
Cumulative Deviation

Traditionally engine fault diagnosis has been performed at steady state conditions. There are several problems which can only be detected by transient data analysis like bearing fault, some control problems etc.. In addition, gas turbine performance deviation due to a component fault is more likely to be magnified during transients, when compared with the same parameter deviations at steady states. The specific approach used in this paper is to compare model-based information with measured data obtained from the engine during a slam acceleration. The measured transient data (from actual engine) is compared with a set of simulated data from the engine transient model, under similar operating conditions and known faults through a Cumulative Deviation. The Cumulative Deviations obtained from the comparisons are minimized for the best match using Genetic Algorithm. The Genetic Algorithm has been tailored to use real coding [1] method and to meet the requirements of the new procedure. The paper describes the application of the approach to a 2-spool turbofan engine and discusses the preliminary studies conducted.

Application of Intelligent Algorithm to Transformer Optimal Design

2011 ◽  
Vol 219-220 ◽  
pp. 1578-1583
Author(s):  
Shuang Zhang ◽  
Qing He Hu ◽  
Xing Wei Wang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Penalty Function ◽  
Fitness Function ◽  
Mutation Rates ◽  
Adaptive Genetic Algorithm ◽  
Speed Of Convergence ◽  
Intelligent Algorithm ◽  
Transformer Model ◽  
Crossover And Mutation

The paper studies transformer optimal design, establishes optimal transformer model based on total owning cost. It adopts penalty function to process objective function with weighted coefficients. For prematurity and low speed of convergence of Simple Genetic Algorithm, improved adaptive genetic algorithm is adopted. It increases crossover and mutation rates, and improves fitness function. It is adopted to search for minimum total owning cost of transformer. The result shows that the algorithm performs well, increases converging speed and betters solution.

scholarly journals Application of modified culture Kalman filter in bearing fault diagnosis

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 757-765
Author(s):  
Wang Hailun ◽  
Alexander Martinez
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Nonlinear Model ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Sensor Data ◽  
Estimation Accuracy ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Abstract Rolling bearings are an important part of rotary machines. They are used most widely in various mechanical sectors, which are among the most vulnerable components in machines. This paper uses CKF algorithm to compile a signal analysis system, analyses the vibration signal of the rolling bearing, extracts fault features, and realizes fault diagnosis. In order to improve the estimation accuracy of bearing fault diagnosis under nonlinear model, a nonlinear model of bearing fault diagnosis based on quaternion and low-accuracy high-noise sensors is established, and the attitude estimation has performed using the culture Kalman filter (CKF) algorithm. The sensor data comparison shows that the use of the volumetric Kalman filter algorithm can effectively improve the estimation accuracy of bearing fault diagnosis and stability. In this paper, the measured vibration signals of several groups of rolling bearings are analysed, and the signal characteristic frequency has extracted. The results show that using the analysis software designed in this paper, several typical faults of rolling bearings can be correctly identified.

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