detection of stator, bearing and rotor faults in induction motors

Procedia Engineering 30 (2012) 1103 – 1109

1877-7058 © 2011 Published by Elsevier Ltd.doi:10.1016/j.proeng.2012.01.969

Available online at www.sciencedirect.com

Procedia

Engineering Procedia Engineering 00 (2011) 000–000

www.elsevier.com/locate/procedia

International Conference on Communication Technology and System Design 2011

Detection of Stator, Bearing and Rotor Faults

in Induction Motors

Semih Ergina, Arzu Uzuntasb, M. Bilginer Gulmezogluc ,a*

a,b,cEskisehir Osmangazi University, Department of Electrical and Electronics Engineering, Eskisehir, 26480, Turkey

Abstract

In this paper, faults in induction motors were diagnosed using the Common Vector Approach (CVA). In order to verify the performance of CVA, a database including stator current signals for normal and faulty cases with 1.5A loading condition was used. The current signals belong to six identical induction motors one of which is normal and remaining ones are faulty motors. The 2-step One-Dimensional Discrete Wavelet Transform (1D-DWT) is applied on the current signals in order to construct feature vectors of each class in the database. While performing CVA, the leave-30-out strategy was followed to test all feature vectors in the database. Substantially satisfactory recognition results were obtained for wavelet energy component-based features. © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of ICCTSD 2011 Keywords: fault diagnosis; induction motors; Common Vector Approach; Discrete Wavelet Transform; wavelet energy component

1. Introduction

The early diagnosis of the faulty conditions in electrical machines is becoming increasingly important. Some electrical machine faults develop slowly and early detection of those faults is crucial to prevent machine breakdown. Unless fault diagnosis is performed in time, the replacement of the machine will be necessary. For this reason, fault diagnosis has great importance in the field of highly reliable systems. Fault identification and diagnostics can improve reliability, allowing drive repair/replacement to happen in a timely and orderly fashion [1].

One of the most commonly used electrical motors in modern industrial installations is the induction

motor. Induction motors play an important role in the safe and efficient operations of industrial plants due

* Semih Ergin. Tel.: +90-222-239-3750 / Ext: 3265; fax: +90-222-229-0535.. E-mail address: [email protected]..

1104 Semih Ergin et al. / Procedia Engineering 30 (2012) 1103 – 1109 Semih Ergin et.,al/ Procedia Engineering 00 (2011) 000–000

to their many advantages, such as simplicity in construction as well as reliability and relatively low cost [1]. The fault detection of induction motors is analyzed using signals, which are sensed from one of their three major components: The stator, the rotor, and the bearings [1]. Among these components, much effort is spent on rotor bar failures of induction motors. If the number of broken bars increases, reaching unacceptable operating conditions; therefore, the importance of a correct diagnosis is inevitable [2]. Bearing faults are over 40% of all induction machine faults, and their detections are desired to prevent damages of industrial processes [3]. Though rotor faults appear less significant than bearing faults in terms of quantitative manner, most of the bearing failures are caused by shaft misalignment, rotor eccentricity, and other rotor related faults [4]. These faults produce some symptoms such as unbalanced air-gap voltages and line currents, increased torque pulsations, decreased average torque, increased losses in efficiency, excessive heating [5].

Automatic condition monitoring and diagnosis are needed to recognize system faults, such as motor

failures in industrial installations where an early warning could prevent escalation of the problem [1]. In recent years, considerable research has been carried out on the investigation of motor condition monitoring, fault detection, and diagnosis of electrical machines. There are various approaches to determine motor faults and diagnose them [6-9]. These algorithms can be classified into time domain, frequency domain, time-frequency domain, higher order spectral analysis, Neural Network (NN), and model-based techniques [6]. Many condition monitoring methods, such as current spectrum analysis, vibration analysis, and thermal analysis have been proposed for different types of motor faults [10]. The NN method is widely used for fault detection and diagnosis of induction motors using current signals and vibration signals [9, 11, 12]. The other methods proposed for fault detection and/or diagnosis are the Genetic Algorithm [13], Fuzzy Automata [14], and Time-Series Data Mining [15, 16]. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been utilized to detect induction motor faults using stator current [17]. Since the number of samples in the different types of signals gathered from induction motors is extremely high, the DWT-based (Discrete Wavelet Transform-based) features are generally extracted in order to cope with high-dimensionality problem [18].

In this paper, the Common Vector Approach (CVA) is used for the diagnosis of faults in induction

motors. CVA is a well-known subspace-based pattern recognition method that is commonly used in speech recognition [19, 20], speaker recognition [21], image recognition [22], and motor fault diagnosis [1] problems. In this method, a common vector for each class is calculated using the feature vectors in the training set of that class. This common vector is unique and represents the common properties of that class [19, 23]. In order to verify the performance of CVA, a database [24] including stator currents for normal and faulty cases with 1.5 A loading condition was used. Since the number of samples in the raw current signals is extremely high, the DWT-based features [18] were calculated in order to cope with high-dimensionality problem. While performing the CVA method, the leave-30-out strategy was followed so as to test all feature vectors in the database.

The remaining of the paper is organized as follows: A brief description of the CVA method will be

provided in the next section. The benchmarking database, the feature extraction procedure and the comparative results will be given in the third section while all conclusions are presented in the last section.

1105Semih Ergin et al. / Procedia Engineering 30 (2012) 1103 – 1109 Semih Ergin et.,al / Procedia Engineering 00 (2011) 000–000

2. Common Vector Approach

The Common Vector Approach (CVA) is a well-known subspace-based pattern recognition method that is widely used in speech recognition [19, 20], speaker recognition [21], image recognition [22], and motor fault diagnosis [1] problems. In the CVA method, a common vector that represents the common properties or invariant features of a class is calculated. CVA is applied to each class separately considering the within-class scatter of the data only [19, 23]. In CVA, if the number (m) of feature vectors of a class in training set is less than or equal to the dimension (n) of each feature vector, this case is named as insufficient data case (m ≤ n) which is considered in this paper. In this section, CVA is detailed in the following paragraphs.

In the training phase, let us define the covariance matrix ( ) of the feature vectors belonging to a

normal/faulty class as

1

a a a a

m

Ti ave i ave

i

(1)

where ai is the ith feature vector in a class and aave is the average feature vector of that class. The eigenvalues of the covariance matrix are non-negative and they can be written in decreasing

order: 1 2 n . Let 1 2, ,u u un be the orthonormal eigenvectors corresponding to these eigenvalues. The first (m-1) eigenvectors of the covariance matrix ( ) corresponding to the nonzero eigenvalues form an orthonormal basis vector set for the difference subspace B. The orthogonal complement (B┴) is spanned by all the eigenvectors corresponding to the zero eigenvalues. This subspace is called as the indifference subspace and has a dimension of (n-m+1). The direct sum of two subspaces B and B┴ is the whole space, and the intersection of them is the null space. The common vector can be represented as the linear combination of the eigenvectors corresponding to the zero eigenvalues of , that is,

1 1 , , , 1,2, ,

a a u u a u u a u ucom i m m i m m i n n

i m

(2)

where acom is the common vector of that class whose covariance matrix is calculated in Eq. (1). Therefore, the common vector, acom , is the projection of any feature vector in a class onto the indifference subspace (B┴) of that class [19].

During classification, the following decision criterion is used:

2

1

*n Tc c c

x ave j jc S j=m

C argmin

a - a u u (3)


where ax is an unknown test feature vector, acave is the average feature vector of the cth class, uc

j is the jth eigenvector corresponding to the jth eigenvalue of the cth class and S indicates the total number of classes. If the distance is minimum for any class c, the unknown feature vector, ax , is assigned to class c.

If the feature vectors in the training set are applied to the CVA, all of them are correctly classified, that

is, a 100% recognition rate is guaranteed for the training set.

3. Experimental Study

3.1. Database

For the experimental verification of the CVA method, a database [24] is utilized in this study. In the construction of the database, stator current and vibration signals have been collected under normal and faulty cases. The normal/faulty classes involving in the database are the followings:

Class 1: Healthy motor, Class 2: Motor with inner race bearing fault, Class 3: Motor with outer race bearing fault, Class 4: Motor with stator fault, Class 5: Motor with open circuited windings, Class 6: Motor with broken rotor bars. The database includes four different loading conditions of no-load, 1.25, 1.50, and 1.75 A. There are

three distinct recordings, each of which belongs to one phase of the motor for each loading condition. The number of raw data vectors and the dimensions of the raw data vectors are same for current signals in each phase. The vectors including 1.5 A loading condition have been only selected in order to exploit in the experimental stages. Therefore, a pattern recognition work with six classes has been employed in this study by using those vectors.

3.2. Feature Extraction

One of the most commonly preferred feature extraction techniques for the diagnosis of faults in the induction motors is wavelet transform analysis [18]. Since the number of samples in the stator current signals gathered from induction motor is extremely high, the DWT-based (Discrete Wavelet Transform-based) features have been extracted in order to cope with high-dimensionality problem. DWT is one/multi-step decomposition process. In this study, the data belonging to each class in the database consists of 300 vectors, each of which has 2048 raw data samples. A 2-step decomposition was implemented by carrying out One-Dimensional Discrete Wavelet Transform (1D-DWT) on these vectors of all classes. In 2-step decomposition, Low-Low (LL) subband coefficients were calculated. Therefore, 300 feature vectors with the size of (512×1) were obtained for each class in the database.

In order to distinctly represent the classes in the database, the wavelet energy component (C) were calculated. This component can be expressed as [25]:

0 5C LH HL HH. (4)

where LH, HL and HH are the detail coefficients that can be found from 2-step 1D-DWT. Thus, again


300 feature vectors with the size of (512×1) were obtained for each class in the database.

3.3. Results

After feature extraction process was completed, two different kinds of feature vectors with the same vector sizes were obtained, and these feature vectors were applied to CVA separately. In the classification process, the leave-30-out strategy was used in order to test all feature vectors in the database. According to this strategy, 270 feature vectors of a class were used for training purpose while the remaining 30 feature vectors in each class were left to construct the test set. Since there are 300 feature vectors for each class in the database, the training and testing procedure were iterated 10 times. Since two types of feature vectors and three different phases of current signals were used in the experiments, 60 (10×3×2) different experiments were conducted in this study. The number of zero eigenvalues for each feature vector type is 243 (512-270+1=243). The recognition rates obtained from two types (LL and C) of feature vectors for three different phases of current signals are given in Tables 1 and 2.

Table 1. Recognition rates obtained using LL coefficients (%)

Leave-thirty-out Test set intervals Phase-1 Phase-2 Phase-3

1-30 81.1 92.2 81.1 31-60 84.4 99.4 84.4 61-90 85.5 100.0 85.5 91-120 83.8 99.4 83.8 121-150 85.5 100.0 85.5 151-180 85.5 99.4 85.5 181-210 83.8 100.0 83.8 211-240 86.1 100.0 86.1 241-270 86.1 100.0 86.1 271-300 82.2 95.6 82.2 Average 84.4 98.6 84.4

Table 2. Recognition rates obtained using wavelet energy component (C) (%)

Leave-thirty-out Test set intervals Phase-1 Phase-2 Phase-3

1-30 98.8 95.0 98.8 31-60 100 99.4 100 61-90 100 100 100 91-120 100 100 100 121-150 100 100 100 151-180 100 100 100 181-210 100 100 100 211-240 100 100 100 241-270 100 100 100 271-300 100 96.1 100 Average 99.9 99.1 99.8

4. Conclusion

In this paper, faults in induction motors were diagnosed using the Common Vector Approach (CVA). The diagnosis problem of induction motor faults was examined as six-class pattern recognition problem in this study. All the experiments were conducted in the MATLAB medium and any online application test was performed. The current signals corresponding to 1.5 A loading condition have been chosen in order to use in all the experimental stages. Rather than the usage of raw current signal data, two different


feature extraction procedures are applied on the current signals by implementing a second-step 1D-DWT which is basically a decomposition process. Since the number of samples in the raw current signals is extremely high, the DWT-based features can be a good choice in order to cope with high-dimensionality problem which ultimately results in undesirable highly computational load and memory requirements. After the feature extraction process, Low-Low (LL) coefficient and the wavelet energy component (C) were obtained to construct feature vectors of each class in the database.

Furthermore, the best recognition rates were obtained by using the wavelet energy component (C). The

main reason is that the wavelet energy component includes more detailed (high-pass filtered) coefficients than approximate coefficients (LL) produced from a signal. Since the samples in the initial and final sections of current signals do not effectively represent each normal/faulty class, the recognition rates increase when the test set intervals (1-30) and (271-300) are not considered. In future studies, it is planned to implement different feature extraction algorithms and to process the vibration signals in the database.

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detection of stator, bearing and rotor faults in induction motors

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