scholarly journals Aircraft Engine Gas-Path Monitoring and Diagnostics Framework Based on a Hybrid Fault Recognition Approach

Aerospace ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 232
Author(s):  
Juan Luis Pérez-Ruiz ◽  
Yu Tang ◽  
Igor Loboda
Keyword(s):  
Aircraft Engine ◽  
Fault Classification ◽  
Test Case ◽  
Diagnostic Systems ◽  
Blind Test ◽  
Self Evaluation ◽  
Promising Tool ◽  
Fault Recognition ◽  
Sparse Representation Classification ◽  
Monitoring And Diagnostics

Considering the importance of continually improving the algorithms in aircraft engine diagnostic systems, the present paper proposes and benchmarks a gas-path monitoring and diagnostics framework through the Propulsion Diagnostic Methodology Evaluation Strategy (ProDiMES) software developed by NASA. The algorithm uses fleet-average and individual engine baseline models to compute feature vectors that form a fault classification with healthy and faulty engine classes. Using this classification, a hybrid fault-recognition technique based on regularized extreme learning machines and sparse representation classification was trained and validated to perform both fault detection and fault identification as a common process. The performance of the system was analyzed along with the results of other diagnostic frameworks through four stages of comparison based on different conditions, such as operating regimes, testing data, and metrics (detection, classification, and detection latency). The first three stages were devoted to the independent algorithm development and self-evaluation, while the final stage was related to a blind test case evaluated by NASA. The comparative analysis at all stages shows that the proposed algorithm outperforms all other diagnostic solutions published so far. Considering the advantages and the results obtained, the framework is a promising tool for aircraft engine monitoring and diagnostic systems.

scholarly journals Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results

2013 ◽  
Author(s):  
Donald L. Simon ◽  
Sébastien Borguet ◽  
Olivier Léonard ◽  
Xiaodong (Frank) Zhang
Keyword(s):  
Health Management ◽  
Software Tool ◽  
Aircraft Engine ◽  
Diagnostic Techniques ◽  
Lessons Learned ◽  
Diagnostic Methods ◽  
Test Case ◽  
Benchmark Problem ◽  
Blind Test ◽  
The Public

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by the aircraft EHM community. It provides a standard benchmark problem enabling users to develop, evaluate and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.

A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach

2018 ◽  
Author(s):  
Igor Loboda ◽  
Juan Luis Pérez-Ruiz ◽  
Sergiy Yepifanov
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Gas Turbine ◽  
Software Tool ◽  
Aircraft Engine ◽  
Data Driven ◽  
Fault Classification ◽  
Fault Identification ◽  
Diagnostic Process ◽  
Promising Tool

In an effort to better compare particular gas turbine diagnostic solutions and recommend the best solution, the software tool called Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) has been developed. This benchmarking platform includes a simulator of the aircraft engine fleet with healthy and faulty engines. The platform presents a public approach, at which different investigators can verify and compare their algorithms for the diagnostic stages of feature extraction, fault detection, and fault identification. Using ProDiMES, some different diagnostic solutions have been compared so far. This study presents a new attempt to enhance a gas turbine diagnostic process. A data-driven algorithm that embraces the mentioned three diagnostic stages is verified on the basis of ProDiMES. At the feature extraction stage, this algorithm uses a polynomial model of an engine baseline to compute deviations of actual gas path measurements from the corresponding values of a healthy engine. At the fault detection and fault identification stages, a common classification for fault detection and fault identification is firstly constructed using deviation vectors (patterns). One of the three chosen pattern recognition techniques then performs both fault detection and fault identification as a common process. Numerous numerical experiments have been conducted to select the best configurations of the baseline model, a pertinent structure of the fault classification, and the best recognition technique. The experiments were accompanied by a computational precision analysis for each component of the proposed algorithm. The comparison of the final diagnostic ProDiMES metrics obtained under the selected optimal conditions with the metrics of other diagnostic solutions shows that the proposed algorithm is a promising tool for gas turbine monitoring systems.

scholarly journals Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results

2013 ◽  
Vol 136 (4) ◽  
Author(s):  
Donald L. Simon ◽  
Sébastien Borguet ◽  
Olivier Léonard ◽  
Xiaodong (Frank) Zhang
Keyword(s):  
Health Management ◽  
Software Tool ◽  
Aircraft Engine ◽  
Diagnostic Techniques ◽  
Lessons Learned ◽  
Diagnostic Methods ◽  
Test Case ◽  
Benchmark Problem ◽  
Blind Test ◽  
The Public

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by the aircraft EHM community. It provides a standard benchmark problem enabling users to develop, evaluate, and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.

Fourier Neural Networks and Generalized Single Hidden Layer Networks in Aircraft Engine Fault Diagnostics

2005 ◽  
Vol 128 (4) ◽  
pp. 773-782 ◽  
Author(s):  
H. S. Tan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Fourier Transforms ◽  
Aircraft Engine ◽  
Fault Classification ◽  
Noise Robustness ◽  
Fault Diagnostics ◽  
Multiple Faults ◽  
Hidden Layer

The conventional approach to neural network-based aircraft engine fault diagnostics has been mainly via multilayer feed-forward systems with sigmoidal hidden neurons trained by back propagation as well as radial basis function networks. In this paper, we explore two novel approaches to the fault-classification problem using (i) Fourier neural networks, which synthesizes the approximation capability of multidimensional Fourier transforms and gradient-descent learning, and (ii) a class of generalized single hidden layer networks (GSLN), which self-structures via Gram-Schmidt orthonormalization. Using a simulation program for the F404 engine, we generate steady-state engine parameters corresponding to a set of combined two-module deficiencies and require various neural networks to classify the multiple faults. We show that, compared to the conventional network architecture, the Fourier neural network exhibits stronger noise robustness and the GSLNs converge at a much superior speed.

Machine Fault Detection Using Genetic Programming

2005 ◽  
Author(s):  
B. Samanta
Keyword(s):  
Fault Detection ◽  
Genetic Programming ◽  
Vibration Data ◽  
Rotating Machine ◽  
Fault Recognition ◽  
Machine Fault ◽  
Artificial Neural Network Ann ◽  
Monitoring And Diagnostics ◽  
The Time Domain ◽  
Selection Of

Applications of genetic programming (GP) include many areas. However applications of GP in the area of machine condition monitoring and diagnostics is very recent and yet to be fully exploited. In this paper, a study is presented to show the performance of machine fault detection using GP. The time domain vibration signals of a rotating machine with normal and defective gears are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to GP for two class (normal or fault) recognition. The number of features and the features are automatically selected in GP maximizing the classification success. The results of fault detection are compared with genetic algorithm (GA) based artificial neural network (ANN)- termed here as GA-ANN. The number of hidden nodes in the ANN and the selection of input features are optimized using GAs. Two different normalization schemes for the features have been used. For each trial, the GP and GA-ANN are trained with a subset of the experimental data for known machine conditions. The trained GP and GA-ANN are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a gearbox. The results compare the effectiveness of both types of classifiers with GP and GA based selection of features.

scholarly journals Blind test comparison on the wake behind a yawed wind turbine

2018 ◽  
Author(s):  
Franz Mühle ◽  
Jannik Schottler ◽  
Jan Bartl ◽  
Romain Futrzynski ◽  
Steve Evans ◽  
...  
Keyword(s):  
Wind Tunnel ◽  
Wind Turbine ◽  
Development Stage ◽  
Wake Flow ◽  
Complex Case ◽  
Test Case ◽  
Good Prediction ◽  
Detached Eddy Simulation ◽  
Blind Test ◽  
Numerical Predictions

Abstract. This article summarizes the results of a fifth Blind test workshop, which was held in Visby, Sweden, in May 2017. This study compares the numerical predictions of the wake flow behind a model wind turbine operated in yaw to experimental wind tunnel results. Prior to the work shop, research groups were invited to predict the turbines' performances and wake flow properties using computational fluid dynamics (CFD) methods. For this purpose, the power, thrust and yaw moments for a 30° yawed model turbine as well as the wake's mean and turbulent streamwise and vertical flow components were measured in the wind tunnel at the Norwegian University of Science and Technology (NTNU). In order to increase the complexity, a non-yawed downstream turbine was added in a second test case, while a third test case challenged the modelers with a new rotor and turbine geometry. Four participants submitted predictions using different flow solvers, three of which were based on Large Eddy Simulations (LES) while another one used an Improved Delayed Detached Eddy Simulation (IDDES) model. The performance of a single yawed turbine was fairly well predicted by all simulations, both in the first and third test case. The scatter in the downstream turbine's performance predictions in the second test case, however, was found to be significantly larger. The complex asymmetric shape of the mean streamwise and vertical velocity was generally well predicted by all the simulations for all test cases. The largest improvement with respect to previous Blind tests is the good prediction of the levels of turbulent kinetic energy in the wake, even for the complex case of yaw misalignment. These very promising results confirm the mature development stage of LES/DES simulations for wind turbine wake modeling, while competitive advantages might be obtained by faster computational methods.

Decision Support for Remote Monitoring and Diagnostics of Aircraft Engine Using Influence Diagrams

2007 ◽  
Author(s):  
Mark Osborn ◽  
LiJie Yu
Keyword(s):  
Remote Monitoring ◽  
Service Providers ◽  
Aircraft Engine ◽  
Influence Diagrams ◽  
Large Set ◽  
Quality Indices ◽  
Secondary Damage ◽  
Desirable Outcome ◽  
Parametric Data ◽  
Monitoring And Diagnostics

FAA regulations require the monitoring of all commercial aircraft engines to ensure airworthiness. In doing so, it provides economic advantages to engine owners to monitor engine performance and resolve identified issues in a timely manner to reduce operational costs or avoid secondary damage. Various remote monitoring and diagnostics service providers exist in the marketplace. However, a common understanding among most of them is that given limited time and information, it is an extremely difficult task to make quick and optimized decisions. Difficulties arise from the fact that an aircraft engine is a complex system and demands considerable expertise to diagnose, but also due to the uncertainty in estimating an engine’s true physical state because of measurement and process noise. Therefore, it is often difficult to decide what action to take in order to achieve the most desirable outcome. In this paper, a cost sensitive engine diagnostic and decision making methodology is described. Diagnostic tool performance at various decision thresholds is estimated over a large set of validated historical cases to evaluate sensitivity, specificity and other quality indices. These quality indices and a set of cost functions are utilized in influence diagrams to derive the optimized decision model in order to minimize costs given the uncertain engine condition and noisy parametric data.

Data-driven dictionary design–based sparse classification method for intelligent fault diagnosis of planet bearings

2021 ◽  
pp. 147592172110290
Author(s):  
Yun Kong ◽  
Zhaoye Qin ◽  
Tianyang Wang ◽  
Meng Rao ◽  
Zhipeng Feng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Data Driven ◽  
Health States ◽  
Segmentation Strategy ◽  
Fault Recognition ◽  
Planetary Transmission ◽  
Monitoring And Diagnostics ◽  
Kinematic Mechanisms ◽  
Sparse Classification

Planet bearings have remained as the challenging components for health monitoring and diagnostics in the planetary transmission systems of helicopters and wind turbines, due to their intricate kinematic mechanisms, strong modulations, and heavy interferences from gear vibrations. To address intelligent diagnostics of planet bearings, this article presents a data-driven dictionary design–based sparse classification (DDD-SC) approach. DDD-SC is free of detecting the weak frequency features and can achieve reliable fault recognition performances for planet bearings without establishing any explicit classifiers. In the first step, DDD-SC implements the data-driven dictionary design with an overlapping segmentation strategy, which leverages the self-similarity features of planet bearing data and constructs the category-specific dictionaries with strong representation power. In the second step, DDD-SC implements the sparsity-based intelligent diagnosis with the sparse representation–based classification criterion and differentiates various planet bearing health states based on minimal sparse reconstruction errors. The effectiveness and superiority of DDD-SC for intelligent planet bearing fault diagnosis have been demonstrated with an experimental planetary transmission system. The extensive diagnosis results show that DDD-SC can achieve the highest diagnosis accuracy, strongest anti-noise performance, and lowest computation costs in comparison with three classical sparse representation–based classification and two advanced deep learning methods.

Study on Fault Classification Method for Control System

2014 ◽  
Vol 945-949 ◽  
pp. 2754-2757
Author(s):  
Long Jia ◽  
Zheng Hong Liu
Keyword(s):  
Pattern Recognition ◽  
Control System ◽  
Fault Diagnosis ◽  
Recognition System ◽  
Classification Method ◽  
Fault Classification ◽  
Intelligent Diagnosis ◽  
Fault Recognition

The paper centers on the character of fault modes recognition for control system and introduces intelligent diagnosis based on signal computing that is called fault recognition system. The key point and direction in recent research about fault recognition is given out. Later classifier of the fault recognition system and its character occupy an important part in the paper. At last application prospect of pattern recognition in fault diagnosis is stated briefly.

scholarly journals Assessment of Three Turbulence Models in a Compressor Rotor

1996 ◽  
Author(s):  
A. Shabbir ◽  
J. Zhu ◽  
M. Celestina
Keyword(s):  
Turbulence Models ◽  
Model Complexity ◽  
Test Case ◽  
Numerical Accuracy ◽  
Blind Test ◽  
Compressor Rotor ◽  
Wall Boundary Conditions ◽  
Sensitivity Studies ◽  
Wide Scatter ◽  
Inlet Boundary Condition

A blind test case for a compressor rotor (ROTOR 37) was organized by the ASME/IGTI at its 1994 meeting in order to assess the predictive capabilities of the turbomachinery CFD tools. The results from the different CFD codes showed a wide scatter which in part is due to the differences in the turbulence models that were used. In order to systematically isolate the capabilities and limitations of the turbulence models, ROTOR 37 flow is computed from the same numerical platform with three different turbulence models. These include: the Baldwin-Lomax model, the standard k-ϵ model, and an improved version of this k-ϵ model. The results from the three models are compared with the experiment. We find that with increasing model complexity the results move closer to the experiment. Several sensitivity studies are carried out to bracket the uncertainty in the computations. These include the effect of: wall boundary conditions for the turbulence models; numerical accuracy of the turbulence solver; and the effect of the inlet boundary condition for turbulence.

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