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A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification
(Taylor & Francis Ltd, 2021) Kaya, Yilmaz; Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Ertunc, H. Metin
Recently, precise and deterministic feature extraction is one of the current research topics for bearing fault diagnosis. For this aim, an experimental bearing test setup was created in this study. In this setup, vibration signals were obtained from the bearings on which artificial faults were generated in specific sizes. A new feature extraction method based on co-occurrence matrices for bearing vibration signals was proposed instead of the conventional feature extraction methods, as in the literature. The One (1) Dimensional-Local Binary Patterns (1D-LBP) method was first applied to bearing vibration signals, and a new signal whose values ranged between 0-255 was obtained. Then, co-occurrence matrices were obtained from these signals. The correlation, energy, homogeneity, and contrast features were extracted from these matrices. Different machine learning methods were employed with these features to carry out the classification process. Three different data sets were used to test the proposed approach. As a result of analysing the signals with the proposed model, the success rate is 87.50% for dataset1 (different speed), 96.5% for dataset2 (fault size (mm)) and 99.30% for dataset3 (fault type - inner ring, outer ring, ball) was found, respectively.
A novel feature extraction method for bearing fault classification with one dimensional ternary patterns
(Elsevier Science Inc, 2020) Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Kaya, Yilmaz; Ertunc, H. Metin
Bearing is one of the most critical parts used in rotary machines. Bearing faults break down the mechanism where it is located. Moreover, the faults may cause to malfunction by spreading to the entire system. Thus this may result in catastrophic failure eventually. Precise and decisive feature extraction from the raw vibration signal maintains to be one of the current topics explored for fault diagnosis in bearings. In this study, vibration signals are obtained from bearings which are formed with artificial faults of specific dimensions from a bearing test setup. Instead of employing traditional feature extraction methods found in the literature, a novel feature extraction method for bearing faults called one-dimensional ternary pattern (1D-TP) is applied. The proposed approach is a statistical method that uses patterns obtained from comparisons between neighbors of each value on vibration signals. The study aims to identify the size (mm) of the fault by determining the bearing part (inner ring, outer ring, ball) from which the faults in the bearings are caused. Several classification techniques were performed by using ternary patterns with RF (Random Forest), k-NN (1<-nearest neighbor), SVM (Support Vector Machine), BayesNet, ANN (Artificial Neural Networks) models. As a result of analyzing the signals obtained from the experimental setup with the proposed model, 91.25% for dataset_1 (different speed), 100% for dataset_2 (fault type - inner ring, outer ring, ball) and 100% for dataset_3 (fault size (mm)) success rates are determined. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.
An Effective Method for Detection of Demagnetization Fault in Axial Flux Coreless PMSG With Texture-Based Analysis
(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Minaz, Mehmet Recep; Akcan, Eyyup
Due to its high power densities and compact dimensions, the axial flux coreless permanent magnet synchronous generator (PMSG) is used in a wide range of areas such as wind turbines and electric vehicles. It is extremely important to detect magnetization faults that occur in these generators. The occurrence of such faults in these machines with a wide range of areas of use affects their operation negatively. In this study, an effective method has been proposed to detect the demagnetization fault occurring in axial flux coreless PMSGs. The relevant method proposes an effective texture analysis-based feature extraction method, which is an original method in contrast to conventional methods used in the literature. It has been revealed that it is a method that can be used instead of conventional methods such as time-frequency analysis, frequency spectrum analysis, and motor current signature analysis (MCSA) methods. Using the finite element method, current and voltage signals were taken from the healthy and axial flux coreless PMSG with 3% and 6% demagnetization fault. Besides, these signals were retaken at different speeds and loads. After the signals were converted into images, using the features obtained from the images with LBP, fault diagnosis processes were carried out with Knn. It was tested both at different fault rates and under different load and speed conditions to test whether the proposed method worked properly. The success rate of this method was observed as 97.16% and 100%. With the proposed method, it has been revealed that the demagnetization fault can be detected in axial flux coreless PMSGs.
An effective method for detection of stator fault in PMSM with 1D-LBP
(Elsevier Science Inc, 2020) Minaz, Mehmet Recep
Permanent Magnet Synchronous Motors (PMSMs) have recently been used commonly in all areas of the industry due to their position control as well as precise speed. The success of these motors in applications of precise speed and position control depends on their whole operation. Even if the fault is at a highly-low-level, this negatively affects the precision of the motor. In this study, the one dimensional local binary patterns (1D-LBP) method, which is compelling and distinctive, has been used for feature extraction instead of frequency spectrum analysis or time-frequency analysis, which are among conventional feature extraction techniques in the literature, to detect short-circuit fault that occurs in PMSM stators. Thus, to test the proposed method, an experiment setup has been prepared to record current and voltage signals detected through 15 kHz sampling from healthy and faulty PMSM. 1D-LBP was applied to these current and voltage signals and the histograms of newly formed current and voltage signals were obtained. Histograms of newly formed signals are used as feature vectors. Healthy and faulty motors could be classified at high success rates applying one of the machine learning techniques, Knn algorithm, to histograms. It was found that the methods had a success rate over 90% when it was tested over-current and voltage data obtained from PMSM that ran at different speeds and loads and had different fault rates to test whether the methods ran properly. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.
An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis
(Elsevier, 2020) Kaplan, Kaplan; Kaya, Yilmaz; Kuncan, Melih; Minaz, Mehmet Recep; Ertunc, H. Metin
Bearings are one of the most widespread components used for energy transformation in machines. Mechanical wear and faulty bearings reduce the efficiency of rotating machines and thus increase energy consumption. The feature extraction process is an essential part of fault diagnosis in bearings. In order to diagnose the fault caused by the bearing correctly, it is necessary to determine an effective feature extraction method that best describes the fault. In this study, a new approach based on texture analysis is proposed for diagnosing bearing vibration signals. Bearing vibration signals were first converted to gray scale images. It can be understood from the images that the signals of different bearing failures form different textures. Then, using these images, LBP (Local Binary Pattern) and texture features were obtained. Using these features, different machine learning models and bearing vibration signals are classified. Three different data sets were created to test the proposed approach. For the first data set, the signals composed of very close velocities were classified. 95.9% success rate was observed for the first data set. The second data set consists of faulty signals at different parts of the bearing (inner ring, outer ring and ball) measured in the same RPM. The type of fault has been determined, and a 100% success rate was obtained for this data set. The final data set is composed of the fault size dimensions (mm) of different ratios. With the proposed approach, a 100% success rate was obtained in the classification of these signals. As a result, it was observed that the obtained feature had promising results for three different data types and was more successful than the traditional methods. (C) 2019 Elsevier B.V. All rights reserved.
Analysis of efficiency and torque effect of 1,1 kw induction motor gear by 2-d finite elements method, 1,1 kw'lık indüksiyon motorun oluk sayısının verime ve torka etkisinin sonlu elemanlar yöntemiyle analizi
(IETS'18 International Engineering and Technology Symposium, 03-05 May, 2018, 3-5 Mayıs 2018) Yavuz, İzzet; Minaz, Mehmet Recep; Kuncan, Melih
In this study, the effect of varying the design parameters of the asynchronous motor was investigated. An asynchronous motor design parameter of 1.1 kW was taken as reference. This design has been analyzed with computer aided 2-D finite element method (SEY) and the results are compared. The number of stator gutters, number of rotor gutters and other motor parameters have been investigated in terms of reduction and increase in torque and torque. An asynchronous motor with design parameters has been shown to reduce the slot length and reduce the number of stator gutters and rotor gutters, resulting in an increase in efficiency of about 2%. Optimum efficiency and torque are obtained when the rotor slot length of the asynchronous motor is 2mm, the number of rotor gutter is 30 and the number of stator gutter is 24.
Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters
(Springer, 2020) Kaya, Yilmaz; Kuncan, Melih; Kaplan, Kaplan; Minaz, Mehmet Recep; Ertunc, H. Metin
Bearings are the most commonly used machine element in order to reduce rotational friction in machines and to compensate radial and axial loads. It is very important to determine the faults in the bearings in terms of the machine health. In order to accurately diagnose bearing-related faults with traditional machine learning methods, it is necessary to identify the features that characterize bearing fault most accurately. Therefore, a new feature extraction procedure has been proposed to determine the vibration signal velocities of different fault sizes and types in this study. The new approach has been employed to obtain features from the vibration signals for different scenarios. After different filtering based on 1D-LBP method, the F-1D-LBP method was used to construct feature vectors. The filters reduce the noise in the signals and provide different feature groups. In other words, it is aimed to generate filters in order to extract different patterns that can separate signals. For each filter applied, different patterns can be obtained for the same local point on signals. Thus, the signals can be represented by different feature vectors. Then, by using these feature groups with various machine learning methods, vibration velocities were separated from each other. As a result, it was observed that the obtained feature had promising results for classification of bearing vibrations.
Classification of CNC Vibration Speeds by Heralick Features
(Graz Univ Technolgoy, Inst Information Systems Computer Media-Iicm, 2024) Kuncan, Melih; Kaplan, Kaplan; Kaya, Yilmaz; Minaz, Mehmet Recep; Ertunc, H. Metin
In the contemporary landscape of industrial manufacturing, the concept of computer numerical control (CNC) has emerged due to the optimization of conventional machinery, distinguished by its remarkable precision and expeditious processing capabilities. These inherent advantages have seamlessly paved the way for the pervasive integration of CNC machines across a myriad of industrial manufacturing sectors. The present study embarks upon a comprehensive inquiry, delving into the intricate analysis of a specialized prototype CNC molding machine, encompassing a meticulous assessment of its structural rigidity, robustness, and propensity for vibrational occurrences. Moreover, an insightful exploration is undertaken to discern the intricate interplay between vibrational signals and intricate machining processes, particularly under diverse conditions such as the presence or absence of the cutting tool, and at varying rotational speeds denoted in revolutions per minute (RPM). The trajectory of this research voyage encompasses an extensive array of empirical experiments meticulously conducted on the prototype CNC machine, with synchronous real-time acquisition of vibrational data. This empirical journey starts by generating two distinct datasets, each meticulously designed to encompass an assemblage of seven distinct rotational speeds, spanning the spectrum from 18000 to 30000 RPM, thereby facilitating enhanced diversity within the dataset. In parallel, a secondary dataset is meticulously derived from the CNC machine operating in the absence of the cutting tool, thereby encapsulating an exhaustive range of 20 discrete RPM values. The extraction of pivotal features aimed at discerning between the vibrational signals arising from distinct conditions (i.e., those emanating from situations involving the presence or absence of the cutting tool) and the associated variance in CNC machine speeds is facilitated through an innovative framework grounded in co -occurrence matrices. The culmination of this methodological framework is the identification of discernible co -occurrence matrices, thereby facilitating the subsequent computation of Heralick features. The classification effort was performed systematically using 10 -fold cross -validation analysis, covering a number of different machine learning models. The outcomes emanating from this intricate sequence of systematic methodologies underscore remarkable achievements. Specifically, the classification of vibrational signals corresponding to varying CNC machine speeds, contingent upon the presence or absence of the cutting tool, yields commendable accuracy rates of 94.27% and 94.16%, respectively. Notably, an exemplary accuracy rate of 100% is attained when classifying differing conditions (i.e., situations involving the presence or absence of the cutting tool) across specific RPM settings, prominently at 22000 24000 26000 28000 and 30000 RPM.
Design and analysis of a new axial flux coreless PMSG with three rotors and double stators
(2017-01-01) Minaz, Mehmet Recep; Çelebi, Mehmet
In this study, axial flux coreless permanent magnet synchronous generator (PMSG) is designed as double stators and three rotors and its electromagnetic and structural characteristics are analyzed. Designing aimed the axial flux generator is placed into the single end of the side rotor in the machine and permanent magnets are placed into the double ends of the middle rotor. One more rotor than the number of stators here is used. Core is not used in the stator of the machine intended to be designed. Aim of this study is to provide both reduction of iron loss and making the machine become lighter by reducing the number of the rotors to be used. Moreover, easiness in the production stage of the machine is provided. Three-dimensioned electromagnetic analysis of the designed machine has been done through the finite element method and transient solutions are suggested based on this. Within this study, arrangements have been made depending on certain standards in order that permanent magnets and coils obtain direct alternating current. The designed new axial flux generator move as permanent speed of 500 rpm and so maximum voltage of approximately 120 V per phase is obtained. Furthermore, this PMSG does not need a gear system due to its design structure.
Efficiency Improvement for a DC-DC Quadratic Power Boost Converter by Applying a Switch Turn-off Lossless Snubber Structure Based on Zero Voltage Switching
(Kaunas Univ Technology, 2018) Ghaderi, Davood; Celebi, Mehmet; Minaz, Mehmet Recep; Toren, Murat
So as to keep the converter in small size, high switching frequencies are normally used. As a result, in higher frequencies, switching losses seriously affect the efficiency. Current and voltage stresses on power switch can be serious problems particularly in high amount of powers where MOSFET switches are generally applied. A snubber circuit can reduce or eliminate spike voltage and currents, decrease the di/dt or dv/dt values on power switch and transfer the power losses on switch to load and increases the lifelong of the switch. This study presents a method for improving the power transmission efficiency for DC-DC Cascaded Boost Converter and uses a passive snubber sub-circuit, which consists of an inductor, a capacitor, and two diodes for reducing the switching loss. The role of resonant capacitor of this structure is discharging directly through the load and is parallel with the power switch. Thus, it is effective in lossless switching and increasing the DC voltage gain of the boost converter. Soft switching is achieved through the use of a LC resonant tank circuit. The tank circuit is responsible for zero voltage switching (ZVS) and zero current switching (ZCS), eliminating the power loss in the switches appreciably. The proposed structure, done by MATLAB SIMULINK based on simulations, has shown more efficiency toward the same structure without snubber circuit. Besides, an application has been conducted in laboratory scales, and results confirm theoretical findings.
Further Qualitative Results for Second-Order Dynamical Systems Based on Circuit Theory Approach
(Springer Birkhauser, 2022) Ates, Muhammet; Minaz, Mehmet Recep
In this study, our goal is to construct mathematical models of some physical systems and then determine their qualitative behaviors by Lyapunov's direct method. The constructed systems are governed by second-order vector ordinary differential equations. Our main system involves three nonlinear elements that generalize the previously studied systems. For each system, we construct the associated energy or storage or Lyapunov function from the physical implication of the system which is based on basic circuit theory. We use the power-energy relationship to construct the Lyapunov function for each system. The directional derivative of each function gives the negative value of the dissipative power in the system. These two technics may not be clear in the literature. Thus, the proposed approach simplifies the derivative of Lyapunov functions which are written in integration and improves some well-known results. For a physical system, we insist that the Lyapunov function and its directional derivate are unique. Our discussion includes four new results associated with the stability theory of dynamical systems. We also give a new passivity result for our main system. Finally, we give an example with simulations to elucidate the theoretical results.
Fırçasız DC Motorunun Eksen Kaçıklığı ve Kırık Mıknatıs Arızalarının Tespitinin Bilgisayar Benzetimi ile Yapılması
(2020) Minaz, Mehmet Recep
Bu çalışma fırçasız DC motorlarda (BLDC) oluşabilecek arızalar önceden belirlenerek motor çalışmasınındevamlılığının sağlanması ve oluşabilecek olumsuzlukları önlemek açısından önem taşımaktadır. Hem arıza tespitive arıza şiddetinin belirlenmesi hem de sabit mıknatıslı motorunun tasarımı sonlu elemanlar yöntemi kullanılarakgerçekleştirildi. Sonlu elemanlar yöntemi kullanılarak motor analizleri yapıldı. Sonlu elemanlar yöntemiylesağlıklı motor, arızalı motor ve bu arızaların farklı şiddetlerinde simülasyonlar gerçekleştirildi. Endüksiyon motoruiçin Hızlı Fourier Dönüşümü (FFT) uygun görülürken BLDC motoru için trapezoidal sinyal çıkışından dolayıDalgacık dönüşüm (WT) yöntemi kullanılarak analiz gerçekleştirilmiştir. Bu çalışmada daha az belirgin olmayandurum analiz edilmiştir. FFT ve WT ölçülenler ile iyi bir uyum içinde olduğunu göstermiştir. Önerilen yöntemikullanarak stator akımı ve stator geriliminin sabit mıknatıs arıza tespiti için yararlı olduğunu göstermiştir. Ayrıca,farklı sınıflandırıcılar kullanarak karşılaştırma yapılmıştır. İncelenen k-NN, MLP ve RF algoritması sınıflandırmada doğruluğunun oldukça kayda değer olduğu bulunmuştur.
İndiksiyon Motorun Mekanik Arıza Teşhisinde Makine Öğrenme Yöntemlerinin Kullanılması
(2019) Minaz, Mehmet Recep; Yıldız, Kadriye
Elektrik makinalarında erken arıza tespiti, arızanın büyüyüp hasarı yaymadan önüne geçilmesi açısından oldukça önemlidir. Arızalarınbüyümeden öngörülüsü, motorun ömrünü artırabildiğinden araştırmacıların ilgi odağı haline gelmiştir. Bu yönde çalışan araştırmacılarendüstriyel düzeyde hızlı, yorumlaması kolay ve işletme açısından uygulanabilirlik olan teknikler üzerine odaklanmıştır. Bu çalışmadaindüksiyon motorlarda oluşan kırık rotor çubuğu ve eksenden kaçıklık arızalarının sonuçlarını sunmaktadır. Sağlıklı ve hatalı koşullariçin bir indüksiyon motorun sonlu elemanlar modeli (FEM) geliştirilmiş ve analiz edilmiştir. Arızalı bir makinenin modeli, sağlıklımotorun fiziksel durum ve mekanik pozisyonları değiştirilip farklı arıza şiddetleri oluşturularak akım, gerilim, akı ve tork sinyalleriincelenmiştir. Bu farklı arıza şiddetlerine ait elektriksel sinyallerin verdiği tepkiler karşılaştırılmıştır. Elde edilen akım sinyaline aitham verilere hızlı fourier yöntemi (FFT) uygulanarak işlenmiş veriler elde edilmiştir. Öznitelik çıkarımı olarak kNN, MLP, RT gibifarklı sınıflandırma metotları ile arıza teşhisinde eğitim amaçlı kullanılmıştır. Kırık rotor çubuğuna ait farklı arıza şiddetleri ileilgilenirken, eksantriklik arızasında ise statik eksantriklik, dinamik eksantriklik ve karışık eksantriklik arızaları üzerinde durulmuştur.Ayrıca, farklı sınıflandırmalar kullanarak karşılaştırma yapılmıştır. k-NN, MLP ve RF algoritması sınıflandırma da doğruluğununoldukça belirgin olduğu tespit edilmiştir.
Influence of a Proposed Switching Method on Reliability and Total Harmonic Distortion of the Quasi Z-Source Inverters
(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Hang, Liu; Subramaniam, Umashankar; Bayrak, Gokay; Moayedi, Hossein; Ghaderi, Davood; Minaz, Mehmet Recep
An Improved Sinusoidal Pulse Width Modulation (ISPWM) technique carried out to obtain pure sine waves for voltage and current signals in Quasi Z-Sourc Inverters (QZSIs) in the load side is given in this study. This switching method can be examined to two and multi-phase approaches simply through the addition of the same controller structure to per phase. This is the main advantage of the proposed converter to obtain higher voltage gains at the output ends of this inverter. The idea is to generate a positive rectified voltage at the output point of the QZSI and positive and negative rectified voltages at the output terminals of the QZSI in two-phase approaches to improve the quality of the output voltage of the F-Bridge Inverter (FBI). These rectified voltages are applied to the Full-Bridge Inverter (FBI) block and pure sine waves to obtain the load current and voltages. 1.34% of the Total Harmonic Distortion (THD) for the output voltage has been reported in the one-phase system while 0.88% of THD has been obtained in the two-phase approach. Besides, the reliability of the QZSI was tested through the Mean Time to Failure (MTTF) analysis with the values of the proposed components. The calculations show a very good result for the long-life of the converter. All experimental and simulations steps have been obtained for the same values of the components to support and confirm the accuracy and correctness of the proposed IMSPW. For the states of single-phase and two-phase converters, a 50 Hz sine-wave with 220 V and 440 V peak to peak amplitude has been acquired. Evaluations of the quality of the voltage and current waveforms related to different active (Resistive, P) and reactive (combination of Resistance and Inductance, QL) loads have been carried out. Experimental results show confirmation for all simulation and mathematical results.
PVsyst Yazılımı ile 30 kW Şebekeye Bağlı Fotovoltaik Sistemin Modellenmesi ve Simülasyonu
(2020) Akcan, Eyüp; Kuncan, Melih; Minaz, Mehmet Recep
Elektrik enerjisi, son yüzyılda, insanlığın günlük yaşam standartlarında temel bir gereklilik haline gelmiştir. Dünyada elektrik enerjisiihtiyacı her geçen gün artmaktadır. Bu yüksek elektrik enerjisi ihtiyacının tedarik edilmesinde şu anda ağırlıklı olarak termal veyahidroelektrik enerji üretim santrallerinden faydalanılmaktadır. Elektrik enerjisi üretiminin olumsuz etkileri olan sera gazı emisyonu vediğer çevresel olumsuzluklarla ilgili artan endişe, elektrik üretimi için PV (fotovoltaik) sistemler gibi yenilenebilir enerjiteknolojilerinin giderek daha fazla farkındalık, önem ve talep görmesine sebep olmaktadır.Çevre dostu elektrik üretim sistemlerine olan talep, her geçen gün daha fazla artış göstermektedir. Bu artışa karşılık verebilmek için,yenilenebilir enerji tabanlı üretimde, güneş fotovoltaik tabanlı enerji üretim sistemlerinin en değerli katkı payına sahip olmasıdolayısıyla, küresel anlamda bu sistemlere büyük bir odak oluşmuş durumdadır. Elektrik enerjisi üretiminde güneş enerjisindenfaydalanmak için genel olarak güneş fotovoltaik teknolojisi kullanılmaktadır. Bu açıdan bakıldığında güneş enerji potansiyeli yüksekolan Türkiye için, PV sistemler çok büyük önem taşımaktadır. PV enerji sistemlerinin performansına, coğrafi konumun ve güneşgörme potansiyelinin yanı sıra güneş modülü tipleri de etki etmektedir.Bu makalede; Türkiye'nin güneydoğusundaki Batman ilinde birbirine bağlı 30 kW güneş fotovoltaik şebekenin tam bir modellemesive simülasyonu gösterilmiştir. Performans oranını ve sistemde meydana gelen farklı kayıpları analiz etmek için PVsyst yazılımprogramı kullanılmıştır.
Sonlu Elemanlar Yöntemi ile Fırçasız Doğru AkımMotorunun (BLDC) Kısa Devre Arıza Analizi
(2021) Minaz, Mehmet Recep; Yıldız, Kadriye
Üç fazlı sürekli mıknatıslı fırçasız makineler, güvenilirlik ve hata toleransının önemli olduğu birçokuygulamada kullanılır. Bu çalışmada, dış rotorlu sürekli mıknatıslı bir fırçasız doğru akım (BLDC) motorunun arızasının benzetimini yapmak için, stator faz devresine ek paralel bir empedans eklenereksargıda oluşan yalıtım bozulması modellenmiştir. Arıza empedansı, manyetik bozulmaya neden olan dolaşımakımını hesaplamak için gereklidir. Motor akım imza analizi (MCSA) arızalı motorun akım işaretleri üzerindeki değişimi göz önünde bulundurularak farklı şiddette arıza tespiti için analizler yapılır. Kısa devrearıza modellemesi sonlu elemanlar yöntemi (SEY) ile gerçekleştirilip kullanılan program ile stator çıkışişaretlerine Hızlı Fourier Dönüşümü (FFT) uygulanarak öznitelik çıkarımı elde edilmiştir. Sınıflandırıcıolarak k en yakın komşu (k-NN), çok katmanlı algılayıcı yapay sinir ağları (MLP), karar ağaçları (RT) gibifarklı sınıflandırma metotları uygulanarak kısa devre arızaların oluşumunu ve şiddetini tahmin etmedekullanılmıştır. İki farkı öz nitelik çıkarımı ile arıza tespitinin doğruluğu karşılaştırıldıktan sonra uygun öznitelik veri seti seçilip, sunulan kriterin kısa devre hatası tespit edilmiştir. Arıza tespitinde, MLPsınıflandırma metodu %80 başarı oranına sahip olmasına karşın k-NN ve RT metotlarında %100 başarı eldeedilmiştir. Bu durumda arıza tespiti için uygulanan k-NN ve RT metotlarının oldukça başarılı olduğugörülmektedir.

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