CHAPTER 1 Introduction and Literature Review
1.15 Machine Learning Techniques
1.15.4 Support Vector Machine Classifier
Support vector machine (SVM) is one of the latest machine learning techniques based on statistical learning theory (Vapnik, 1995; Vapnik, 1998). An excellent review of applications of SVMs for machine fault diagnosis was given by Widodo et al. (2007). SVMs are the non- probabilistic, binary, and linear classifiers. They revolve around the concept of construction of a linear hyperplane to separate two classes of data as shown in Figure 1.19. The hyperplane’s location and orientation are found out by solving an optimization problem to maximize the margin.
x
1x
2Margin Neg
ative C lass
Positive C lass
Hyperplane
Figure 1.19: SVM classifier
SVM works well for the linearly separable data, however, when the data is not linearly classifiable, a kernel transformation to a greater dimensional space is carried out. In such a space the data can be linearly classifiable. The advantages of SVMs are that, (1) they require very fewer data to construct a reliable classifier (2) their training time is less and (3) because of the convex function optimization they find the global minimum. However, the disadvantage
is that classifier’s performance majorly depends upon the values of SVM parameters and kernel parameters.
Many optimizations and cross-validation techniques have been used by researchers to construct an optimum SVM classifier. Various CP fault classification algorithms developed are given in refs. (Yuan and Chu, 2006; Zhao et al., 2010; Saberi et al., 2011; Sakthivel et al., 2012a; Yunlong and Peng, 2012; Azadeh et al., 2013; Muralidharan et al., 2014; Kumar and Kumar, 2017; Shervani-Tabar et al., 2017).
Yuan et al. (2006) used the multi-class SVM algorithm to classify various pump-rotor faults, such as the gear damage, structure resonance, vane rupture, shaft crack, bearing damage, bearing looseness, rotor part looseness, rotor eccentricity, rotor radial touch friction, body join looseness, pressure pulse, cavitation, and shaft bend. They used the feature dimensionality reduction technique the PCA. They compared various multiclass SVM strategies and found that ‘one to others’ gave the maximum precession ratio and took the least training time.
Zhao et al. (2010) used neighborhood rough set models to select the features for the slurry CP fault classifications. Where the faults considered were leading and trailing edge damages of the CP impeller. The authors illustrated that using a shared neighborhood size for every feature may miscalculate a feature’s degree of dependency. Therefore, the neighborhood rough set model was revised by setting different neighborhood sizes for various features. To check the effectiveness of feature selection, probabilistic neural networks, kNN and SVM algorithms were used. It was found that the average classification accuracy improved from 84.06%
(without feature selection) to 99.56% (with the aforementioned feature selection). The SVM and the kNN performed on par with each other.
Saberi et al. (2011) compared the performance of ANN and SVM to classify CP faults with noisy data. It was found that the SVM outperformed ANN in the fault classification. To extract suitable features from the vibration data Yunlong and Peng (2012) used the empirical mode decomposition (EMD) and intrinsic mode functions (IMFs). They classified CP faults, such as the unbalance, misalignment and looseness. The classification algorithm adapted was the least square support vector machine (LSSVM). They obtained a classification accuracy of 92.6%. Sakthivel et al. (2012a) compared the classification accuracy of gene expression programming (GEP) technique with other pattern recognition techniques, like the SVM, wavelet-GEP, and PSVM in classifying the same faults. It has been observed that both SVM and GEP outdo the rest of the methods, giving a classification accuracy of 99.93%.
Muralidharan et al. (2014) used CWT analysis in conjugation with the SVM to diagnose the aforementioned CP faults. They compared the performances of various wavelet families at different levels. The best classification accuracy was found with db8 wavelet. The accuracy presented was 99.84%. Azadeh et al. (2013) proposed the SVM based flexible algorithm with a hyperparameter optimization (optimization techniques used were the genetic algorithm (GA) and particle swarm optimization (PSO)) and ANN. A binary CP fault classification was attempted in standard as well as noisy states of the signals. Classification accuracy of 93.3%
was obtained in both the considered states. The SVM with hyperparameter optimization gave better prediction performance than ANN.
Xue et al. (2014) diagnosed different severities of CP faults, such as the cavitation, unbalance and misalignment using an intelligent fault diagnosis technique. A statistic filter was employed to obtain the feature signals from the acquired vibration signals in an optimum frequency region. Also, NSPs were identified to represent the feature signals for differentiating CP fault types. The optimal classification hyperplane function obtained using SVM and NSPs was called the synthetic symptom parameter (SSP). Based on SSPs, the CP fault detection and the fault class identification was attempted based on the possibility theory and the Dempster-Shafer theory. The developed methodology identified high severity faults with good precession; however, the low and medium severity of faults could not be adequately identified.
Azizi et al. (2017) built an algorithm based on the generalized regression neural network (GRNN) estimated the severity of cavitation. Three states of the cavitation, namely, the developed cavitation, limited cavitation and no cavitation were considered. The EMD method was used to decompose original signals into IMFs. A hybrid feature selection algorithm based on artificial-bees algorithm was used to improve the accuracy of classification from 97.5 to 100%. Bordoloi and Tiwari (2017) attempted classification of different severities of suction blockages and cavitation faults in a CP at varying operating speeds of it. They found that the classification accuracy improved from 55.3 to 94.6% from the low to high speeds. The algorithm they adopted was SVM with the hyper-parameter optimization using the genetic algorithm (GA) and the artificial bee colony algorithm (ABCA).
Ebrahimi and Javidan (2017) presented an SVM based approach to classify CP faults including impeller defects and seal faults. The vibration signals acquired were decomposed
using discrete wavelet transform (DWT) to three levels using Daubechies wavelets.
Classification accuracy of 96.67% was obtained. Tabar et al.(2017) developed an algorithm based on SVMs to identify different intensities of cavitation in a CP. Features used for the classification were selected based on their sensitivity towards the fault using compensation distance evaluation approach. Kumar and Kumar (2017) used the GA optimized SVM algorithm for the fault detection in CPs. The faults considered were the clogged impeller, broken impeller, bearing inner race and outer race defects. The features that are sensitive to the faults are acquired from the raw vibration signals and scale marginal integration graph (from CWT). An accuracy of 96.66% was found in the identification of various CP faults.