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AUTOMATIC CLASSIFYING METHOD FOR WEDGE
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TIGHTNESS BY SUPPORT VECTOR MACHINE AND
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ARTIFICIAL NEURAL NETWORK
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Running head: Automatic wedge tightness by Support Vector Machine and
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Artificial Neural Network Technique
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Thanachai Poombansao1*, Waree Kongprawechnon1, Somsak
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Kittipiyakul1, Chonlada Theerawon2, Muntita Charoenlarp1 and It
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Chunsangnate1
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1
School of Information, Computer, and Communication Technology (ICT),
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Sirindhorn International Institute of Technology, Thammasat University, Khlong Luang,
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Pathum Thani, 12121, Thailand. Tel. 0-2986-9009; Fax. 0-2986-9112;
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E-mail: t.poombansao@gmail.com
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2
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National Electronics and Computer Technology Center (NECTEC), Khlong Luang, Pathum
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Thani, 12121, Thailand. Tel. 0-2564-6900; Fax. 0-2564-6901-3;
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*Corresponding author
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Abstract
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This study proposed an automatic classifying system for wedge tightness of a
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generator’s wedge. It consists of 4 processes called data collection,
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preprocessing, feature extraction, and classification. The aim of this study is to
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classify and verify the tightness of a generator’s wedge for the Electricity
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Generating Authority of Thailand (EGAT) by using Machine Learning
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algorithms, called Support Vector Machine (SVM) and Artificial Neural
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Network (ANN). The linear and radial basis function (RBF) are selected for
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SVM classifier. The evaluation is completed by using a 10-fold cross validation
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technique to give high accuracy and a low number of False Negatives (FN).
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From the simulation results, the signals extracted in the frequency domain has
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less FN than the time domain, and ANN gives the best performance among
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classifiers.
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Keywords: Wedge tightness signal, Support Vector Machine, Artificial Neural
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Network, classification
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Introduction
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Nowadays, the use of electricity is increasing rapidly around the world due to the
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exponential growth of new technology and population. Electricity is involved in all
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daily activities such as industries, hospitals, and houses. Therefore, more usage of
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power plants is needed to support the demand of users, which cause the deterioration
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of generators. The information of a generator such as the tightness of a generator’s
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wedge is very important to evaluate the safety of the generator. Normally, human
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judgment has been used to determine the wedge performance inside the generator by
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inspection and evaluation. A human can check the wedge tightness by tapping with a
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hammer, feeling for vibration, listening to the sound by trained ears, and recording
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the degree of looseness on a chart. A tight wedge has very little vibration or hard
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ringing sound. On the other hand, a loose wedge has noticeably vibration and a very
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hollow sound (Geoff Klempner and Isidor Kerszenbaum, 2004). This method is very
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subjective because it depends on individual judgment from operators by hearing the
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sound from a slot wedge (Summer project pal, 2011). However, it consumes both
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time and cost because the rotor in the generator has to be removed and the generator
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needs to shut down during operation. Hence, a robot has been used instead of a
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human to determine the wedge performance, which is cost efficient because the rotor
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does not need to be removed during operation. George introduced the testing of stator
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wedge tightness in an electrical generator that contains a vibration sensor attached to
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the stator core lamination and a mounting system for mounting the base assembly to
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impact assembly (George, F.D. et al, 1996). At first, robots used for generators in
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Thailand were imported robots. However, the robots are very expensive and too big
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for some generators in Thailand. Therefore, the Electricity Generating Authority of
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Thailand (EGAT) plans to develop its own robot that is smaller with lower cost. The
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robot also is required to automatically classify the wedge performance of a generator.
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The task of a robot can be divided into two main functions including wedge
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tapping control and automatic analyze wedge tightness system. Wedge tapping
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control is used to adjust the tapping pattern of the robot to be a single, periodic, or
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repeating pattern. The wedge tightness signals are analyzed afterward. There are 4
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steps to analyze wedge tightness signals including data collection, preprocessing,
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feature extraction, and classification.
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Data collection is the raw data received by the robot to illustrate the wedge
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inside the generator. Preprocessing refers to improving the raw data quality including
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filtering, and noise cancellation. Feature extraction reduces the pattern vector to a
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lower dimension and keeps the useful information from the original vector, called the
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feature vector. Classification takes the feature vector as an input to define the
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tightness of the wedge by placing the feature vector into the most appropriate class
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(Tohka J., 2013). Many classification methods have been applied to the system such
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as nearest neighbor classifier, wavelet transform, probabilistic classification, and
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multiclass classification.
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SVM classification is an alternative way for pattern recognition by using the
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separation on the hyperplane (decision boundary) to distinguish data diversity. ANN
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classification is the machine learning algorithm to teach to recognize and remember
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different traits. ANN also has low computation loads, and flexibility for pattern
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recognition. A computational neuron can produce either a linear or non-linear input
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which allows the network to learning efficiently. ANN is configured for a specific
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application such as pattern recognition or data classification through a learning
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process. There are many research works with SVM and ANN for classification such
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as Cardiac Auscultation Analysis, a screening system to diagnose disease from heart
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sounds in digital format (Phatiwuttipat, P. et al, 2011).
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classification methods also are used for analyzing construction materials with
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ultrasonic waves to find the number of defects, and the result is very accurate
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(Saechai, S. et al, 2012). The objective of this work is to develop an automatic
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classification system for determining the tightness of a generator’s wedge.
The SVM and ANN
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This study is beneficial for identifying the tightness of a generator’s wedge
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in a power plant. A brief description of both SVM and ANN classifiers, the
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methodology, and the comparison between both classification results is discussed in
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the following sections.
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Material and methods
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System Configuration
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In the experiment, Figure 1 illustrates the developed robot by the National
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Electronic and Computer Technology Center (NECTEC) with size 180-300
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millimeters wide, 300 millimeters long and 20 millimeters thick. The developed
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robot is cheaper than the cost of an imported robot (about 250,000 U.S. dollars).
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The overall robot system is shown in Figure 2. The control box acts like a
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brain and nervous system which is responsibility for controlling and linking all
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functions into the entire system, including movement of a camera, movement of
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robot, and wedge tapping. The display monitor is used for monitoring each activity in
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the system including video camera, wedge’s result, position of the robot and the
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graphical user interface (GUI). The joystick is used for controlling the direction of the
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camera, the movement of robot, and tapping pulse. In addition, the robot connects to
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the control box via cable. This study focuses on wedge tightness signal analysis.
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The raw data of a wedge tightness signal obtained from the robot goes
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through the process called “automatic analyze wedge tightness system” in order to
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classify the data. The block diagram in Figure 3 illustrates the signal processing part
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of the wedge tightness signal.
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Data collection
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Data collection comes from the robot tapping on the generator and receiving a
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reacted signal back. “Signal conditioning” is used in order to focus and make the
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signals even clearer. A Data Acquisition System (DAQ) is used to convert analog
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data samples from the robot into digital numeric values to plot a graph (Figure 4).
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Figure 4 a) and Figure 5 a) show the tight wedge data signal and the loose wedge
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signal, respectively. The real raw data is obtained from the board calibration, which
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has 20,000 sampling points. The robot taps every 100 ms and each sampling point has
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a time interval of 50 µs.
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In this experiment, there are 240 inputs for each wedge signal (tight and loose). The
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inputs have been reduced into 2,000 sampling points to extract only significant parts.
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The 2,000 sampling points of tight wedge signal and loose wedge signal are shown in
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Figure 4 and Figure 5, respectively.
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Preprocessing
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Preprocessing is used for the preparation of signal. Preprocessing refers to
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operations performed on the raw data to improve its quality such as filtering, and
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noise cancellation. The wedge tightness signal response is reduced by using a
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threshold to capture the significant part. The threshold is set to 2,000 sampling points
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to remove the unwanted part of the signal.
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Feature extraction
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Feature extraction extracts the significant features from the raw data. Fast
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Fourier Transform (FFT) is used for feature extraction in the frequency domain. FFT
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is a very useful tool for signal analysis to obtain the features for classification (Kim et
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al., Unpublished). FFT computes the same as the Discrete Fourier Transform (DFT)
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by letting
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as follows:
be complex numbers. The DFT can be defined by the formula
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where
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By contrast, FFT takes approximately
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which takes approximately
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experiment to obtain the input features of classifiers as follows.
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operations which is faster than DFT,
operations. There are 2 methods used in this
i) Time domain classifier: 2,000 data points in the time domain are used in
this experiment, as shown in Figure 6.
ii) Frequency domain classifier: 2,000 data points are transformed to the
frequency domain by using Fast Fourier Transform (FFT), as shown in Figure 7.
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Classification
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The input sample is separated into 2 classes including 120 tight wedge data
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signals and 120 loose wedge data signals. The classification methods used in this
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experiment are Support Vector Machine (SVM) and Artificial Neural Network
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(ANN).
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Support Vector Machine (SVM)
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Support Vector Machine (SVM) is one of the important models for pattern
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recognition and classification. The main concept of SVM is to transform an input
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signal into feature space and divide the clear gap between classes with an optimal
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hyperplane (decision boundary) and maximum margin (support vector) (Avci, E.,
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2009), as shown in Figure 8 (Chatunapalak, I. et al., 2012). Since most of the data are
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non-linear and non-separable, a kernel function is used for mapping a non-linear
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input space into a linear space (higher dimensional feature space) to perform the
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separation (Ahmad, A.R. et al., 2009). Figure 9 illustrates how to transform a non-
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linear data point to a higher dimensional feature space by using SVM with a kernel
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function.
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Many kernel functions are used for SVM classifiers such as linear, polynomial, radial
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basis unction (RBF), Fourier, etc. The kernel parameters in a kernel function must be
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set properly to maximize the accuracy of SVM classification. This study selected
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linear and RBF kernels for SVM classification.
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SVM classification trained and evaluated input samples by a data splitting
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method. The training and testing samples generally split into 90% and 10% of the
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input samples for each class, respectively. The input, training, and testing samples are
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provided in Table 1. The binary classifications of SVM are performed for
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classification. Each data input is prepared in the form of row vector of size
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where
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columns of features (2,000 features), as shown in the formula as follows:
represents the number of input samples. Each input sample contains
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The output of a wedge tightness signal depends on the data class. The
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numbers 1 and 0 are set as a target for class 1 and class 2 (tight and loose wedge),
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respectively. The output vector for binary classification respectively is presented as
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follows:
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The proper kernel function is selected to train the non-linear SVM classifier. The
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linear and RBF kernels are used to train classifiers and test results because the RBF
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kernel has good classification according to many studies. The RBF kernel gives high
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classification performance in this study. Ten-fold cross validation is applied for
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classification, as shown in Figure 10. The datasheets are randomly divided into 10
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subsets. Each subset is constructed and trained by using 9 out of 10 (90%) folds and
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tested 1 out of 10 (10%) to obtain a cross validation estimate of its error rate. The
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process is repeated in the same steps for 10 subsets and averaged for classification
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accuracy from all data (Delen et al., 2005). The proposed SVM classification used
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MATLAB for simulations.
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Artificial Neural Network (ANN)
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An Artificial Neural Network (ANN), usually called Neural Network (NN), is
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a good technique for determining pattern recognition. The main concept of ANN is
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inspired by animal nervous systems (brain), which is composed of the large number
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of highly interconnected processing elements (neurons) working to solve specific
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problems through a learning process. The learning process of ANN includes adaptive
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learning, self-organization, real-time operation, and fault tolerance. ANN uses the
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back-propagate model to solve non-linear signals.
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The back-propagated model is a feed-forward multi layered ANN that allows
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a signal to travel one way from input to output with no feedback (loops). Figure 11
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illustrates the back-propagated model of ANN. According to Figure 11, the ANN
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network consists of 3 layers including input layer, hidden layer, and output layer. The
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input layer represents the raw data fed into the network as an input, which forwards to
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the hidden layer. The hidden layer consists of neurons to solving complex problem by
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calculation, and forwards the result into the output layer. The output layer takes the
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results from the hidden layer, performs a calculation, and gives the final result.
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The input samples and classes are the same as SVM. MATLAB is used to
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train, validate, and test for Neural Network Pattern Recognition Protocol (nprtool) by
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selected input and target. Training, validation, and testing is set to 85%, 5%, and
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10%, respectively, and the number of hidden neurons is set to 10.
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Results and discussion
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Each raw data of wedge tightness signal performs preprocessing and feature
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extraction to obtain the input features for the classifier. The number of total input
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samples, training samples, and testing samples are mentioned in a previous section.
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The trained classifier is evaluated by test samples. The classification accuracies are
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analyzed by using two different types of input features from the data that were
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obtained in the time domain and frequency domain. The efficiency of each single
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classification is obtained by a 2×2 confusion matrix. The confusion matrix is a table
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that shows the performance of the algorithm. An example of a confusion matrix is
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shown in Figure 12. The first and second rows represent the data with target tight
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wedge and loose wedge, respectively. The first and second columns represent the
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predictor of a tight wedge and loose wedge, respectively. The true positives (TP) are
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the number of tight wedges which are correctly classified. False positives (FP) are the
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number of loose wedges which are misclassified from tight wedges. On the other
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hand, true negatives (TN) are the number of loose wedges which are correctly
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classified and false negatives (FN) shows the number of tight wedges which are
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misclassified from loose wedges. Positive predictive value (PPV) and negative
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predictive value (NPV) are the rate that the predictor can give a correct classification
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for the tight wedge and loose wedge, respectively. Sensitivity and specificity are the
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correctness prediction for tight wedge and loose wedge, respectively. Accuracy
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(ACC) value computes the average accuracy in the classification tables. All Values in
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the tables are the average classification accuracies of 10 confusion matrices.
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Time Domain Analysis
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The result of classification algorithms for wedge tightness signal in the time
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domain is shown in Table 1 including SVM (linear and RBF) with kernel functions
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and ANN classifier.
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From Table 1, the value of specification in the time domain between 2 classes
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(tight and loose wedge) is classified. The sensitivity of ANN (98.24%) has better
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performance than VSM in both linear (96.08%) and RBF (96.1%) functions. The
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specification of ANN (90.52%) also has better efficiency than the others (linear
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88.59% and RBF 88.67%). In comparison, the SVM with RBF kernel function has an
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accuracy of about 91.38% which is worse than that of SVM with linear function
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(92.34%), but has twice faster average training time than that of the linear function.
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From the result, ANN classifier provides the best efficiency, compared to both SVM
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(linear and RBF) classifiers in the time domain. However, a false negative is very
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important for wedge tightness because the misclassification of a loose wedge can
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cause uncertainly in a generator. Since false negatives still exist in the time domain
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(1.25% for ANN, 1.97% for SVM with linear function, and 1.98% for SVM with
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RBF function), frequency domain classification is applied to decrease the number of
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false negatives.
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Frequency Domain Analysis
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The classification result in frequency domain is shown in Table 2 including
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SVM (linear and RBF) with kernel functions and ANN classifiers. From Table 2, the
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number of false negatives for SVM with RBF function is 0.08%, which is the worst
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classification, compared to the other methods. The numbers of false negatives in
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ANN and SVM with linear function are reduced to 0%, which is a perfect
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classification for wedge tightness. The training time decreases when using SVM with
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linear function (11.182s), SVM with RBF function (7.095s), and ANN (2s) methods.
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Conclusion
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The automatic classification method for wedge tightness by support vector machine
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and artificial neural network provides good recognition and classification results in
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the frequency domain. The classification result shows that the ANN classification
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method has the best performance, which has the highest accuracy and shortest
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training time. The result shows that the frequency domain signal has more accuracy
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than the time domain signal. By comparison between 2 SVM methods, the linear
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function provides a better solution in terms of accuracy, but consumes more time than
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the RBF function does.
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For further study, binary-classification (tight or loose wedge) from this
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experiment may not be sufficient to identify a suspected loose wedge generator in a
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real environment. The multi-classification technique can be developed in the future to
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predict how many degrees of looseness in a suspected loose wedge for a generator.
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For instance, multi-class support vector machine has been used by an automatic
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screening method to detect glaucoma for early treatment and preserve eye sight
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(Vejjanugraha, P. et al, 2013).
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wedge tightness needs Graphic User Interface (GUI) for ease of use and
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understanding.
Moreover, the automatic classifying method for
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Acknowledgements
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This research is financially supported by School of Information, Computer, and
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Communication Technology (ICT) and the National Research University Project,
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Thailand office of Higher Education Commission (NRU). The author would like to
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thank the National Electronics and Computer Technology Center (NECTEC) and
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Electricity Generating Authority of Thailand (EGAT) for advice and data in this
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research.
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References
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Geoff Klempner and Isidor Kerszenbaum (2004). Stator winding mechanical tests. In:
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Operation and maintenance of large turbo-generators. John Wiley & Sons,
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Inc, Hoboken, New Jersey, p. 485-487.
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Summer project pal (2011). A new method for Stator Slot Wedge Testing of Large
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…… Generators. Available from: http://topicideas.net/how-to-a-new-method-for-
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…… stator-slot-wedge-testing-of-large-generators. Access date: Jan, 2001.
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George, F.D., Mark, W.F., and Harry, L.S., inventors; Westinghouse Electric
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...…….Corporation, assignee. February 27, 1996. System and method for impact
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…… testing wedge tightness in an electrical generator. U.S. patent no. 5,493,894.
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Tohka, J. (2013). Introduction to Pattern Recognition. Department of Signal
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……….Processing, Tempere University of Technology.
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Phatiwuttipat, P., Kongprawechnon, W., and Tungpimolrut, K. (2012). Cardiac
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…........Auscultation Analysis System with Neural Network and SVM technique.
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Saechai, S., Kongprawechnon, W., and Sahamitmongkol, R. (2012). Test System for
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………Vector Machine and Neural Network, SCIS-ISIS 2012, Kobe, Japan.
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Kim, J.S., Kim, I.B., and Yoo, K.S. An experimental comparison of ultrasonic pulse
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Avci, E. (2009). A new intelligent diagnosis system for the heart valve disease by
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Ahmad, A.R., Khalid, M., Gaudin, C.V., and Poisson, E. (2004). Comparison of
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………(2012). Childhood musical murmur classification with support vector machine
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Delen, D., Walker, G., and Kadam, A. (2005). Predicting breast cancer survivability:
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Vejjanugraha, P., Kongprawechnon, W., Kondo, T., Tungpimolrut, K., Kaneko, H.,
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………and Sintuwong, S. (2013). An Automatic Screening Method For Detecting
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Using
Multi-class
Support
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Vector
Machine,
Thammasat
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Table 1. Classification results in the time domain
Methods
Kernel
TP
FP
FN
TN
PPV
NPV
Sensitivity
Specification
ACC
Training
Function
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
time (S)
ANN
-
46.25
5
1.25
47.51
90.55
97
98.24
90.52
93.75
2.000
SVM
Linear
48.04
5.71
1.97
44.31
89.4
95.79
96.08
88.59
92.34
7.460
RBF
48.03
6.66
1.98
43.34
87.84
95.69
96.1
86.67
91.38
3.857
364
365
366
367
368
369
370
371
20
21
372
Methods
373
Kernel
TP
FP
FN
TN
PPV
NPV
Sensitivity
Specification
ACC
Training
Function
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
time (S)
ANN
-
56.67
0
0
43.33
100
100
100
100
100
2.000
SVM
Linear
50
0
0
50
100
100
100
100
100
11.182
RBF
49.92
0.46
0.08
49.55
99.12
99.84
99.84
99.09
99.47
7.095
Table 2. Classification results in the frequency domain
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377
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Figure 1. Imported robot for testing.
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384
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23
385
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Control box
Control
Movement of
camera
PLC
+
Joystick
Automatic
Analysis of wedge
tightness system
Display Monitor
+
Manual
Camera
Control
Movement of
robot
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Figure 2. Overall robot system
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DATA COLLECTION
PREPROCESSING
FEATURE EXTRACTION
CLASSIFICATION
TIME ANALYSIS
Wedge tightness
signal response
Noise Removal
Time Domain
FREQUENCY ANALYSIS
FFT Transform
Frequency
Domain
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Figure 3. Block diagram of signal processing part
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405
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407
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SVM or ANN
CLASSIFICATION
RESULTS
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408
409
(a)
(b)
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Figure 4. a) The original sampling points of tight wedge signal b) The 2,000 sampling
points of tight wedge signal
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(a)
(b)
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Figure 5. a) The original sampling points of loose wedge signal b) The 2,000 sampling
points of loose wedge signal
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27
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(a)
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(b)
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Figure 6. a) Tight wedge signal in the time domain b) Loose wedge signal in the time
domain
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(a)
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(b)
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Figure 7. a) Tight wedge signal in the frequency domain b) Loose wedge signal in the
frequency domain
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Figure 8. Basic concept of support vector machine
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Figure 9. Transformation non-linear data points to higher dimension linear space
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471
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Have not been tested sets
Test sets
Tested sets
10%
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10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
10%
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10%
10%
10%
Round 1
10%
10%
10%
Round 2
Figure 10. Ten-fold cross-validation procedure
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10%
…
10%
10%
10%
10%
10%
10%
10%
10%
10%
Round 10
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Figure 11. Back-Propagation model of artificial neural network
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493
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Condition positive Condition negative
Test outcome
positive
True Positive
(TP)
False Positive
(FP)
Positive Predictive
Value (PPV)
TP
TP+FP
Test outcome
negative
False Negative
(FN)
True Negative
(TN)
Negative Predictive
Value (NPV)
TN
FN+TN
Sensitivity
TP
TP+FN
Specification
TN
FP+TN
Accuracy
TP+TN
TP+FP+FN+TN
495
496
Figure 12. Example of 2×2 confusion matrix
33