A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

2014 ◽  
Author(s):  
Dengji Zhou ◽  
Jiayun Wang ◽  
Huisheng Zhang ◽  
Shilie Weng
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Small Sample ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Artificial Neural ◽  
Fault Diagnostic ◽  
Gas Path Fault ◽  
Turbine Model

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like non-linear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like artificial neural networks, need a large number of experimental data. Both are difficult to gain. Support vector machine (SVM), a novel computational learning method with excellent performance, seems to be a good choice for gas path fault diagnostic of gas turbine without engine model. In this paper, SVM is employed to diagnose a deteriorated gas turbine. And the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data, and can be employed to gas path fault diagnostic of gas turbine. Additionally, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnostic based on small sample.

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Shilie Weng
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Small Sample ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Support Vector ◽  
Artificial Neural ◽  
Gas Path Fault ◽  
Turbine Model

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like nonlinear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like expert system, need a knowledge database. Both are difficult to gain. Thus, data-driven approach for gas path diagnosis, like artificial neural network, is increasingly attractive. Support vector machine (SVM), a novel computational learning method, seems to be a good choice for data-driven gas path fault diagnosis of gas turbine. In this paper, SVM is employed to diagnose a deteriorated gas turbine. The effect of sample number, kernel function, and monitoring parameters on diagnostic accuracy are studied, respectively. Additionally, the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data and can be employed to gas path fault diagnosis of gas turbine. In addition, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnosis based on small sample.

Gas turbine gas-path fault identification using nested artificial neural networks

2018 ◽  
Vol 90 (6) ◽  
pp. 992-999 ◽  
Author(s):  
Amare D. Fentaye ◽  
Aklilu T. Baheta ◽  
Syed Ihtsham Ul-Haq Gilani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gas Turbine ◽  
Diagnostic Technique ◽  
Identification Accuracy ◽  
Fault Identification ◽  
Content Type ◽  
Artificial Neural ◽  
Fault Diagnostic

Purpose The purpose of this paper is to present a quantitative fault diagnostic technique for a two-shaft gas turbine engine applications. Design/methodology/approach Nested artificial neural networks (NANNs) were used to estimate the progressive deterioration of single and multiple gas-path components in terms of mass flow rate and isentropic efficiency indices. The data required to train and test this method are attained from a thermodynamic model of the engine under steady-state conditions. To evaluate the tolerance of the method against measurement uncertainties, Gaussian noise values were considered. Findings The test results revealed that this proposed method is capable of quantifying single, double and triple component faults with a sufficiently high degree of accuracy. Moreover, the authors confirmed that NANNs have derivable advantages over the single structure-based methods available in the public domain, particularly over those designed to perform single and multiple faults together. Practical implications This method can be used to assess engine’s health status to schedule its maintenance. Originality/value For complicated gas turbine diagnostic problems, the conventional single artificial neural network (ANN) structure-based fault diagnostic technique may not be enough to get robust and accurate results. The diagnostic task can rather be better done if it is divided and shared with multiple neural network structures. The authors thus used seven decentralized ANN structures to assess seven different component fault scenarios, which enhances the fault identification accuracy significantly.

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

2018 ◽  
Vol 184 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Gal Amit ◽  
Hanan Datz
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Method ◽  
Algorithm Performance ◽  
Artificial Neural ◽  
First Time ◽  
Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

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