scholarly journals Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines

Aerospace ◽  
2020 ◽  
Vol 7 (9) ◽  
pp. 132
Author(s):  
Phattara Khumprom ◽  
David Grewell ◽  
Nita Yodo
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Gas Turbine ◽  
Deep Neural Network ◽  
Data Driven ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Aircraft Engines ◽  
Selection Methods ◽  
Wrapper Feature Selection

Predicting Remaining Useful Life (RUL) of systems has played an important role in various fields of reliability engineering analysis, including in aircraft engines. RUL prediction is critically an important part of Prognostics and Health Management (PHM), which is the reliability science that is aimed at increasing the reliability of the system and, in turn, reducing the maintenance cost. The majority of the PHM models proposed during the past few years have shown a significant increase in the amount of data-driven deployments. While more complex data-driven models are often associated with higher accuracy, there is a corresponding need to reduce model complexity. One possible way to reduce the complexity of the model is to use the features (attributes or variables) selection and dimensionality reduction methods prior to the model training process. In this work, the effectiveness of multiple filter and wrapper feature selection methods (correlation analysis, relief forward/backward selection, and others), along with Principal Component Analysis (PCA) as a dimensionality reduction method, was investigated. A basis algorithm of deep learning, Feedforward Artificial Neural Network (FFNN), was used as a benchmark modeling algorithm. All those approaches can also be applied to the prognostics of an aircraft gas turbine engines. In this paper, the aircraft gas turbine engines data from NASA Ames prognostics data repository was used to test the effectiveness of the filter and wrapper feature selection methods not only for the vanilla FFNN model but also for Deep Neural Network (DNN) model. The findings show that applying feature selection methods helps to improve overall model accuracy and significantly reduced the complexity of the models.

scholarly journals Methods of clustering parameters in the creation of neural network multi-mode dynamic models of aircraft engines

2021 ◽  
pp. 71-78
Author(s):  
Александр Анатолиевич Тамаргазин ◽  
Людмила Борисовна Приймак ◽  
Валерий Владиславович Шостак
Keyword(s):  
Neural Network ◽  
Cluster Analysis ◽  
Gas Turbine ◽  
Technical Condition ◽  
Technical State ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Aircraft Engines ◽  
Clustering Methods ◽  
Aviation Engines

The presence on modern aviation gas-turbine engines of dozens and even hundreds of sensors for continuous registration of various parameters of their operation makes it possible to collect and process large amounts of information. This stimulates the development of monitoring and diagnostic systems. At the same time the presence of great volumes of information is not always a sufficient condition for making adequate managerial decisions, especially in the case of evaluation of the technical condition of aviation engines. Thus it is necessary to consider, that aviation engines it is objects which concern to individualized, i.e. to such which are in the sort unique. Therefore, the theory of creating systems to assess the technical state of aircraft engines is formed on the background of the development of modern neural network technology and requires the formation of specific methodological apparatus. From these positions in the article the methods which are used at carrying out clustering of the initial information received at work of modern systems of an estimation and forecasting of a technical condition of aviation gas-turbine engines are considered. This task is particularly relevant for creating neural network multimode models of aircraft engines used in technical state estimation systems for identification of possible failures and damages. Metric, optimization and recurrent methods of input data clustering are considered in the article. The main attention is given to comparison of clustering methods in order to choose the most effective of them for the aircraft engine condition evaluation systems and suitable for implementation of systems with meta-learning. The implementation of clustering methods of initial data allows us to breakdown diagnostic images of objects not by one parameter, but by a whole set of features. In addition, cluster analysis, unlike most mathematical-statistical methods do not impose any restrictions on the type of objects under consideration, and allows us to consider a set of raw data of almost arbitrary nature, which is very important when assessing the technical condition of aircraft engines. At the same time cluster analysis allows one to consider a sufficiently large volume of information and sharply reduce, compress large arrays of parametrical information, make them compact and visual.

Application of neural network expert systems for monitoring, diagnostics and forecasting of technical condition of aircraft engines

2020 ◽  
pp. 51-59
Author(s):  
Д.О. Пушкарёв
Keyword(s):  
Neural Network ◽  
Expert Systems ◽  
Gas Turbine ◽  
Technical Condition ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Monitoring And Diagnostics ◽  
Technical Operation

Рассматривается применение нейросетевых экспертных систем в области контроля, диагностики и прогнозирования технического состояния авиационных ГТД на основе нечеткой логики. Показана методика для решения таких задач в области технической эксплуатации авиационной техники совместно с использованием фаззи-интерференсной системы программы MATLAB. Используя статистические данные о работе двигателя формируется экспертная система на основе нейронной сети позволяющая осуществлять контроль и диагностику ГТД, а также прогнозировать дальнейшее техническое состояния анализируемого двигателя. The application of neural network expert systems in the field of monitoring, diagnostics and forecasting of the technical condition of aviation gas turbine engines based on fuzzy logic is considered. The technique for solving such problems in the field of technical operation of aircraft and using the fuzzy-interference system of the MATLAB program is shown. Using statistical data on the operation of the engine, an expert system is based on the fundamental of a neural network that provide monitoring and diagnostics of gas turbine engines, as well as predicting the further technical condition of the analyzed engine.

Determination of Cycle Configuration of Gas Turbines and Aircraft Engines by Optimization Procedure

1990 ◽  
Author(s):  
Yoshiharu Tsujikawa ◽  
Makoto Nagaoka
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Specific Impulse ◽  
Optimization Procedure ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Aircraft Engines ◽  
Scramjet Engine ◽  
Aero Engines

This paper is devoted to the analyses and optimization of simple and sophisticated cycles, particularly for various gas turbine engines and aero-engines (including scramjet engine) to achive the maximum performance. The optimization of such criteria as thermal efficiency, specific output and total performance for gas turbine engines, and overall efficiency, non-dimensional thrust and specific impulse for aero-engines have been performed by the optimization procedure with multiplier method. The comparisons of results with analytical solutions establishes the validity of the optimization procedure.

Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks

2015 ◽  
Author(s):  
A. Vatani ◽  
K. Khorasani ◽  
N. Meskin
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Health Monitoring ◽  
Network Architecture ◽  
Information Criterion ◽  
Turbine Engines ◽  
Prediction Errors ◽  
Gas Turbine Engines ◽  
Dynamic Neural Networks ◽  
Turbine Engine

In this paper two artificially intelligent methodologies are proposed and developed for degradation prognosis and health monitoring of gas turbine engines. Our objective is to predict the degradation trends by studying their effects on the engine measurable parameters, such as the temperature, at critical points of the gas turbine engine. The first prognostic scheme is based on a recurrent neural network (RNN) architecture. This architecture enables ONE to learn the engine degradations from the available measurable data. The second prognostic scheme is based on a nonlinear auto-regressive with exogenous input (NARX) neural network architecture. It is shown that this network can be trained with fewer data points and the prediction errors are lower as compared to the RNN architecture. To manage prognostic and prediction uncertainties upper and lower threshold bounds are defined and obtained. Various scenarios and case studies are presented to illustrate and demonstrate the effectiveness of our proposed neural network-based prognostic approaches. To evaluate and compare the prediction results between our two proposed neural network schemes, a metric known as the normalized Akaike information criterion (NAIC) is utilized. A smaller NAIC shows a better, a more accurate and a more effective prediction outcome. The NAIC values are obtained for each case and the networks are compared relatively with one another.

scholarly journals Some Aerodynamic Problems of Aircraft Engines: Fifty Years After -The 2007 IGTI Scholar Lecture-

2009 ◽  
Vol 131 (3) ◽  
Author(s):  
Edward M. Greitzer
Keyword(s):  
Gas Turbine ◽  
Collaborative Research ◽  
Research Process ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Aircraft Engines ◽  
Jet Engines ◽  
Organizational Boundaries ◽  
Turbine Performance ◽  
Single Discipline

Problems of high technological interest, for example the development of gas turbine engines, span disciplinary, and often organizational, boundaries. Although collaboration is critical in advancing the technology, it has been less a factor in gas turbine research. In this paper it is proposed that step changes in gas turbine performance can emerge from collaborative research endeavors that involve the development of integrated teams with the needed range of skills. Such teams are an important aspect in product development, but they are less familiar and less subscribed to in the research community. The case histories of two projects are given to illustrate the point: the development of the concept of “smart jet engines” and the Silent Aircraft Initiative. In addition to providing a capability to attack multidisciplinary problems, the way in which collaboration can enhance the research process within a single discipline is also discussed.

scholarly journals METHOD OF FORMULATING INPUT PARAMETERS OF NEURAL NETWORK FOR DIAGNOSING GAS-TURBINE ENGINES

Aviation ◽  
2013 ◽  
Vol 17 (2) ◽  
pp. 52-56 ◽  
Author(s):  
Mykola Kulyk ◽  
Sergiy Dmitriev ◽  
Oleksandr Yakushenko ◽  
Oleksandr Popov
Keyword(s):  
Neural Network ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Training Data ◽  
Turbine Engines ◽  
Data Sets ◽  
Gas Turbine Engines ◽  
Turbine Engine ◽  
Wide Range ◽  
And Training

A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. These data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. The method allows sets that can project some changes in the technical conditions of a gas-turbine engine to be received, taking into account errors that occur in the measurement of the gas-dynamic parameters of the air-gas path. The operation of the engine in a wide range of modes should also be taken into account.

scholarly journals Multi-Layer Perceptron Neural Network Model Development for Chili Pepper Disease Diagnosis Using Filter and Wrapper Feature Selection Methods

2021 ◽  
Vol 11 (5) ◽  
pp. 7714-7719
Author(s):  
S. Nuanmeesri ◽  
W. Sriurai
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Information Gain ◽  
Disease Diagnosis ◽  
Chili Pepper ◽  
Growth Stages ◽  
Multi Layer Perceptron ◽  
Selection Methods ◽  
Diagnosis Model ◽  
Wrapper Feature Selection

The goal of the current study is to develop a diagnosis model for chili pepper disease diagnosis by applying filter and wrapper feature selection methods as well as a Multi-Layer Perceptron Neural Network (MLPNN). The data used for developing the model include 1) types, 2) causative agents, 3) areas of infection, 4) growth stages of infection, 5) conditions, 6) symptoms, and 7) 14 types of chili pepper diseases. These datasets were applied to the 3 feature selection techniques, including information gain, gain ratio, and wrapper. After selecting the key features, the selected datasets were utilized to develop the diagnosis model towards the application of MLPNN. According to the model’s effectiveness evaluation results, estimated by 10-fold cross-validation, it can be seen that the diagnosis model developed by applying the wrapper method along with MLPNN provided the highest level of effectiveness, with an accuracy of 98.91%, precision of 98.92%, and recall of 98.89%. The findings showed that the developed model is applicable.

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