scholarly journals Automated detection of hot-gas path defects by Support Vector Machine based analysis of exhaust density fields

2021 ◽  
pp. 1-16
Author(s):  
Marcel Oettinger ◽  
Lars Wein ◽  
Dajan Mimic ◽  
Philipp Gilge ◽  
Ulrich Hartmann ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Gas Turbines ◽  
Automated Detection ◽  
Support Vector ◽  
Acceptance Test ◽  
Hot Gas ◽  
Monitoring Applications ◽  
Aero Engines ◽  
The Individual ◽  
Exhaust Jet

Defects in the hot-gas path of aero engines have been shown to leave typical signatures in the density distribution of the exhaust jet. These signatures are superposed when several defects are present. For improved maintenance and monitoring applications, it is important to not only detect that there are defects present but to also identify the individual classes of defects. This diagnostic approach benefits both, the analysis of prototype or acceptance test and the preparation of Maintenance, Repair, and Overhaul. Recent advances in the analysis of tomographic Background-Oriented Schlieren (BOS) data have enabled the technique to be automated such that typical defects in the hot-gas path of gas turbines can be detected and distinguished automatically. This automation is achieved by using Support Vector Machine (SVM) algorithms. Choosing suitable identification parameters is critical and can enable SVM algorithms to distinguish between different defect types. The results show that the SVM can be trained such that almost no defects are missed and that false attributions of defect classes can be minimized.

scholarly journals Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Jianwei Cui ◽  
Mengxiao Shan ◽  
Ruqiang Yan ◽  
Yahui Wu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Energy Difference ◽  
Test System ◽  
Support Vector ◽  
Svm Classifier ◽  
Aero Engine ◽  
Aero Engines ◽  
Optimal Set

This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB) with support vector machine (SVM). The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT-) based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

scholarly journals Pembangkitan Formulir Web Berdasarkan Metadata SQL dan Spesifikasi W3C

2018 ◽  
Vol 3 (2) ◽  
pp. 63
Author(s):  
Muhammad Fikry ◽  
Yusra Yusra ◽  
Taufik Hidayat
Keyword(s):  
Support Vector Machine ◽  
User Interface ◽  
User Acceptance ◽  
Black Box ◽  
Support Vector ◽  
Acceptance Test

Dalam membangun sistem informasi berbasis web, terdapat di dalamnya sebuah tahapan membangun formulir data isian sebagai representasi basis data didalam sistem informasi. Formulir menjadi jalan utama untuk memasukkan data kedalam basis data melalui sistem informasi. Dalam membangun formulir sistem informasi berbasis web, seorang user interface programmer akan merancang formulir dengan elemen-elemen HTML yang sesuai dengan struktur basis data. Penelitian ini membahas tentang bagaimana membangun aturan-aturan pembangkitan formulir dan mengimplementasikan aturan-aturan tersebut kedalam aplikasi pembangkit formulir. penelitian dilakukan terhadap standar bahasa SQL dan standar penulisan tag HTML, kemudian dilakukan pemetaan elemen SQL menjadi elemen formulir HTML sebagai acuan dalam membangun aturan-aturan membangkitkan formulir. Setelah itu dilakukan analisa terhadap model hubungan data pada RDBMS serta menganalisa dampaknya terhadap formulir. Hasil analisa aturan-aturan pembangkitan formulir akan diimplementasikan kedalam aplikasi pembangkit formulir berbasis web berdasarkan metadata SQL. Berdasarkan hasil pengujian yang dilakukan menggunakan Black Box dan User Acceptance Test, aplikasi pembangkit dapat dibangun dan berjalan dengan baik dalam membangkitkan formulir HTML.Kata kunci - Basis Data, HTML, Pembangkit Formulir Web, SQL, iraise, keluhan, klasifikasi, rapidminer, support vector machine 

The potential of support vector machine as the diagnostic tool for schizophrenia: A systematic literature review of neuroimaging studies

2011 ◽  
Vol 26 (S2) ◽  
pp. 1363-1363 ◽  
Author(s):  
M.P. Collins ◽  
S.E. Pape
Keyword(s):  
Support Vector Machine ◽  
Diagnostic Tool ◽  
Literature Search ◽  
Structural Mri ◽  
Support Vector ◽  
Systematic Literature Search ◽  
Resonance Imaging ◽  
Pubmed Database ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

IntroductionSchizophrenia is a relatively common chronic psychotic mental illness, which usually continues throughout life. Current diagnosis is based on a set of psychiatrist-applied diagnostic criteria. There can be considerable differences between diagnostic classification based upon either the set of criteria used, or the individual who applies the criteria. For this reason, the development of an objective test to inform the diagnosis could be highly beneficial.ObjectivesTo assess the use of Support Vector Machine (SVM) as a potential diagnostic tool for schizophrenia, with a particular focus on the application of SVM to Magnetic Resonance Imaging (MRI) data.AimsTo show the use of SVM on MRI data to be a potentially viable diagnostic test.MethodA systematic literature search was carried out using the PubMed database, Web of Knowledge as well as Google Scholar. This search was conducted using the terms ‘Schizophrenia’, ‘SVM’/‘Support Vector Machine’ and ‘MRI/fMRI’. This was followed by the application of criteria relating to relevance to the desired search topic (as assesed by the author). Ten publications were identified as relevant.ResultsResults showed strong evidence that the application of SVM to MRI data can reliably differentiate between patients with schizophrenia and healthy controls.ConclusionsThe results indicate that using SVM to analyse MRI data can be reliably used to identify schizophrenia, although there is some variability between the results produced. The potential of SVM in application to fMRI (as opposed to structural MRI) data is yet to be fully explored.

scholarly journals A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7653 ◽  
Author(s):  
Mahyat Shafapour Tehrany ◽  
Lalit Kumar ◽  
Farzin Shabani
Keyword(s):  
Support Vector Machine ◽  
Statistical Analysis ◽  
Belief Function ◽  
Susceptibility Mapping ◽  
Support Vector ◽  
Ensemble Technique ◽  
Evidential Belief Function ◽  
Flood Susceptibility ◽  
Flood Susceptibility Mapping ◽  
The Individual

In this study, we propose and test a novel ensemble method for improving the accuracy of each method in flood susceptibility mapping using evidential belief function (EBF) and support vector machine (SVM). The outcome of the proposed method was compared with the results of each method. The proposed method was implemented four times using different SVM kernels. Hence, the efficiency of each SVM kernel was also assessed. First, a bivariate statistical analysis using EBF was performed to assess the correlations among the classes of each flood conditioning factor with flooding. Subsequently, the outcome of the first stage was used in a multivariate statistical analysis performed by SVM. A highest prediction accuracy of 92.11% was achieved by an ensemble EBF-SVM—radial basis function method; the achieved accuracy was 7% and 3% higher than that offered by the individual EBF method and the individual SVM method, respectively. Among all the applied methods, both the individual EBF and SVM methods achieved the lowest accuracies. The reason for the improved accuracy offered by the ensemble methods is that by integrating the methods, a more detailed assessment of the flooding and conditioning factors can be performed, thereby increasing the accuracy of the final map.

scholarly journals Support Vector Machine for Behavior-Based Driver Identification System

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Huihuan Qian ◽  
Yongsheng Ou ◽  
Xinyu Wu ◽  
Xiaoning Meng ◽  
Yangsheng Xu
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identification System ◽  
Dynamic Features ◽  
Experimental Platform ◽  
Driving Behaviors ◽  
The Individual ◽  
Driver Identification

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.

scholarly journals Support vector machine for the prediction of heating energy use

2018 ◽  
Vol 22 (Suppl. 4) ◽  
pp. 1171-1181 ◽  
Author(s):  
Aleksandra Sretenovic ◽  
Radisa Jovanovic ◽  
Vojislav Novakovic ◽  
Natasa Nord ◽  
Branislav Zivkovic
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Energy Use ◽  
Support Vector Machine Model ◽  
District Heating ◽  
Support Vector ◽  
University Campus ◽  
Machine Model ◽  
Proposed Model ◽  
The Individual

Prediction of a building energy use for heating is very important for adequate energy planning. In this paper the daily district heating use of one university campus was predicted using the support vector machine model. Support vector machine is the artificial intelligence method that has recently proved that it can achieve comparable, or even better prediction results than the much more used artificial neural networks. The proposed model was trained and tested on the real, measured data. The model accuracy was compared with the results of the previously published models (various neural networks and their ensembles) on the same database. The results showed that the support vector machine model can achieve better results than the individual neural networks, but also better than the conventional and multistage ensembles. It is expected that this theoretically well-known methodology finds wider application, especially in prediction tasks.

Automated Condition Evaluation of Hot-Gas Path Components of Jet Engines Through Exhaust Jet Analysis

2018 ◽  
Author(s):  
Ulrich Hartmann ◽  
Joerg R. Seume
Keyword(s):  
Density Distribution ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Automated Detection ◽  
Reference State ◽  
Jet Engines ◽  
Density Distributions ◽  
Hot Gas ◽  
Svm Algorithm ◽  
Exhaust Jet

This paper determines the influence of different defective components in the hot-gas path (HGP) of a civil aircraft engine on the density distribution in the exhaust. The intention is to automate the identification of defective components inside the HGP through an analysis of the density distribution in the exhaust jet. The defects include an increased radial gap of the blades in the high-pressure turbine (HPT), and a reduction of the film cooling air mass flow in the first stage of the HPT. In addition, several combinations of both defects are simulated. In the present paper the exhaust density distributions are generated numerically using CFD simulations of the HGP. The density distribution in the exhaust jet is reconstructed with synthetic Background-Oriented Schlieren (BOS) measurements and automatically analyzed. The methodology for the automated defect detection consists of two algorithms, a Support Vector Machine (SVM) algorithm to automatically classify each measurement into a corresponding defect or reference class and an outlier detection algorithm to detect variations from the reference state — without assignment. It is shown that BOS provides a sufficient reconstruction quality to automatically detect defective HGP components with a SVM algorithm. It is possible to automatically detect both defects, even when they occur at the same time. For this purpose, different features were calculated to isolate the influence of each defect on the density distribution. The outlier detection algorithm allows for an automated detection of variations in the density distribution compared to the reference state without any previous knowledge of the influence of the defects on the density distributions during the training procedure. With this algorithm it is possible to detect unknown or new defects which have not been observed or regarded yet. These results strengthen the hypothesis, that an automated detection of defects in jet engines prior to the disassembly is possible.

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