An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis

Journal Pre-proof An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis Kaplan Kaplan, Yılmaz Kaya, Melih Kuncan, Mehmet Recep Minaz, H. Metin Ertunç

PII: DOI: Reference:

S1568-4946(19)30801-4 https://doi.org/10.1016/j.asoc.2019.106019 ASOC 106019

To appear in:

Applied Soft Computing Journal

Received date : 15 May 2019 Revised date : 28 November 2019 Accepted date : 10 December 2019 Please cite this article as: K. Kaplan, Y. Kaya, M. Kuncan et al., An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis, Applied Soft Computing Journal (2019), doi: https://doi.org/10.1016/j.asoc.2019.106019. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Elsevier B.V. All rights reserved.

*Highlights (for review)

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> In this study, a novel approach based on Texture Analysis with LBP is proposed to extraction of quantitative features from bearing vibration signals > One advantage is that this method uses all data points for feature extraction > It is fast and can be use in real-time application

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> Original data (Experimental setup of authors)

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> High accuracies achieved for bearing fault classification

*Manuscript Click here to view linked References

Journal Pre-proof An Improved Feature Extraction Method Using Texture Analysis with LBP for Bearing Fault Diagnosis

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Kaplan KAPLAN1, Yılmaz KAYA2, Melih KUNCAN3, Mehmet Recep MİNAZ3, H. Metin ERTUNÇ1 1 Kocaeli University, Mechatronics Engineering, 413800, Turkey 2 Siirt University, Computer Engineering, 56100, Turkey 3 Siirt University, Electrical and Electronics Engineering, 56100, Turkey

[email protected]

Abstract

Bearings are one of the most widespread components used for energy transformation in

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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.

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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

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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

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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.

Keywords: Feature Extraction, Texture Analysis, Vibration Signals, Local Binary Pattern

Journal Pre-proof 1. Introduction In the industrial automation systems of recent years, machine motion is generally provided by rotational force. Bearings are a mechanical component commonly used in motor systems that perform this rotational motion and are used to reduce friction. Early detection and diagnosis of rotating machines, deteriorating condition, low efficiency and avoidance of unexpected failures are becoming increasingly important in these systems. The main reasons for the

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failure of rotating machines are generally due to bearing faults. For example, metal bearing failures in induction motors constitute 40% of the faults in the system [1]. Therefore, several techniques have been developed for the health monitoring of bearings to prevent such failures early. Apart from these techniques, vibration-based fault analysis has proved to be more advantageous in revealing bearing failure. Furthermore, it is impossible to prevent wear due to the constant friction of the mechanical components. For this reason, a condition monitoring based on bearing diagnostics should be applied to rotating machines in automation systems

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[2]. When the current literature is examined, the methods based on vibration analysis and current analysis can be seen to be the most applied fault monitoring methods. The data obtained in these studies are analyzed by methods such as time-space [3-5], frequency space

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[6-7], time-frequency space [8-10] and then supported by methods such as artificial intelligence techniques [11-15]. Nowadays, in order to define bearing failures better, more studies on improving the method of feature extraction are started. Van and Kang developed a new feature extraction technique based on non-local means de-noising of non-local means and

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empirical mode decomposition in order to obtain more accurate error information in the feature extraction step. Later, during the feature selection phase, the hybrid distance evaluation technique (DET) was combined with utilizing the advantages of particle swarm optimization models. As a result, a comparison was made between three popular classifier types: K-nearest neighbors, probabilistic neural network, and support vector machine classifiers are employed as the classifier. They obtained the classification with the success of 98.58% with DET-PSO-KNN model [16]. Zhang et al. proposed a new intelligent diagnostic

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method that can automatically learn the bearing fault characteristics. To perform automatic feature extraction, they developed a subset based deep auto-encoder (SBTDA.) In order to obtain the appropriate configuration, they optimized several key parameters with the particle swarm optimization (PSO) algorithm. They used three publicly available data sets with the proposed method and stated that they achieved superior success compared to other studies with 99.65%, 99.66% and 99.60% mean test accuracy, respectively [17]. Han et al. have proposed a new method for fault diagnosis using the Complementary Ensemble Empirical

Journal Pre-proof Mode Decomposition (CEEMD) method as feature extraction and Teager energy operator for signal enhancement. They achieved better characteristic profile by using the Teager energy operator and the CEEMD combination. The authors stated that the application of the model with simulation data and experimental data was successful in distinguishing weak error characteristics from noise [18]. Li et al. proposed a new signal processing scheme for the fault detection of the train axle bearings based on the multi-scale morphological filter (MMF)

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feature selection. They stated that more than 30 vibration signals were calculated for the axle bearings with different conditions and the features which can reflect the fault characteristics more effectively and representative were selected by using the maximum relevance and minimum redundancy principle. In the experimental results, they showed that the method they proposed had superior performance in extracting fault characteristics of faulty train axle bearings. Also, they compared MMF with MMF based on kurtosis criteria and MMF based on spectral kurtosis criteria. Based on the proposed feature selection, the MMF method observed

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that these two methods left behind in the detection of train axle bearing failures [19]. Zarei et al. propose an intelligent method based on artificial neural networks (ANN) to detect bearing failures of induction motors. In this method, the vibration signal has first passed the removing

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non-bearing fault component (RNFC) filter to eliminate the fault components that are not caused by the bearing, and then they performed fault classification using a second neural network using pattern recognition techniques. They suggested that the proposed method in the three-phase asynchronous motor results in a confirmation of its ability despite the low quality

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(noisy) of the vibration signal measured in fault detection [20]. Ahmed et al. stated that they submitted an original article based on a compressive sampling (CS) using the multiple measurement vector (MMV) and feature order for bearing fault classification. The MMVbased CS used a large amount of bearings to reduce the vibration signal by generating sampled signals from the raw vibration signals. They tested the model for the classification of bearing failures by three classification algorithms (LRC, ANN and SVM). They stated that they achieved a high degree of performance [21].

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The feature extraction step is the most crucial 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 recent trends, deep learning methods have been preferred intensively for feature extraction besides traditional methods [22-24]. This study aims to propose a new feature extraction approach in the classification of bearing vibration signals. Therefore, in this study, an approach based on texture analysis for bearing vibration signals is presented. Firstly, bearing vibration signals were transformed to gray scale

Journal Pre-proof images. Then, LBP (Local Binary Pattern) is used to obtain texture features. In the final stage, different machine learning models and actual bearing vibration signals are classified to test the success of the system validation. The most important advantages of the proposed method are; the first is that it uses all the values on the vibration signal, and the second one is that it is simple and real-time applications. According to the results, it was observed that the proposed method provides stable features for classification of vibration signals.

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In this study, in the first part, literature information about the study subject and general information about bearing vibration signals classification are presented. In the second part, information about the data set used in the study is given. In the third part, proposed feature extraction approach is explained. In the fourth section, the results obtained are given in detail. In the fifth section, the results are discussed.

2. Dataset and Experimental Setup

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The data used in this study were collected from the bearing-shaft assembly connected to an AC servo motor in Figure 1. This type of servo motors is generally two-phase squirrel cage asynchronous motors. Two-phase asynchronous motors are made of large-power but mostly

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used in automatic control systems as small servo motors. Because they are not brushes and collectors, they are less likely to malfunction and maintenance is easy [25]. The test setup consists of an AC servo motor, LVD drive, two-axis vibration sensor, radial, and axial pressure valves, NIDAQ 6211 data acquisition card, and signal conditioners. The

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data collection was performed using the National Instruments 6211 data acquisition card (DAQ), which was compatible with MATLAB environment. A vibration sensor (352C65) with piezoelectric material is used to convert the vibration to electrical signals. The vibration sensor operates with a sensitivity of 10.2 mV / (m/s2), a non-linearity less than 1% at a resonance frequency of less than 35 kHz and a measuring range of ± 491 m/s2 pk. PCB 484B06 signal conditioners in connection with the sensor were used to regulate and amplify the vibration signals. The signal conditioner is used to upgrade the data from the vibration

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sensor by converting it to an electrical signal. The condenser with one channel output can raise up to 200 kHz signal. It can also output a 24-volt sensor from 2 mA to 20 mA [26-28].

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Figure 1. The experimental test setup

Figure 2. Vibration data collection scheme

In order to classify the error size in the roll, artificial defects of specific diameters have been formed. Artificial defects must be carefully opened in order to prevent any extra vibration except for artificial faults in bearing [29-30]. For this purpose, 0.15 mm, 0.5 mm and 0.9 mm diameter holes were drilled on the inner ring with micron precision by laser drilling method.

Journal Pre-proof In Figure 3, the laser-generated artificial errors and Figure 4 show the microscopic view of the inner ring 0.15 mm faulty bearing as an example. Vibration data were collected at 24 kHz

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sampling frequency and at different rpm speeds, divided into 1 second and 20 data packets.

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Figure 3. Artificial fault in the inner and outer ring

Figure 4. A microscopic view of a laser-generated sample fault At the first stage, the data obtained from an error-free bearing was recorded. The data

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obtained from the artificial defects (mm) in the inner ring, outer ring and balls of the bearings were recorded. The tests were repeated for five different speeds determined at the data collection stage. Thus, different speed, different fault size (mm) and different fault types (inner ring fault, outer ring fault and ball fault) were obtained from the bearings. The following tables show the different bearing fault details tested in the test setup for operation.

Journal Pre-proof Table 1. Dataset1 - data set created for different speeds Dataset1

Artificial Fault Type (Speed)

Experiment Number: 1 2 3 4 5

IR (mm) 0.15 0.15 0.15 0.15 0.15

OR (mm) 0.15 0.15 0.15 0.15 0.15

BS (mm) 0 0 0 0 0

Speed (rpm) 1500 1740 1800 1860 2100

Dataset2

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Table 2. Dataset2 – data set generated for bearing fault types Artificial Fault Type (variety)

Experiment Number 1 2 3 4

IR (mm) 0.00 0.9 0.0 0.0

OR (mm) 0.00 0.0 0.9 0.0

BS (mm) 0.00 0.0 0.0 0.9

Speed (rpm) 1500 1500 1500 1500

Table 3. Dataset3 - data set for bearing error sizes Artificial Fault Type (size) IR (mm) 0.00 0.15 0.5 0.9

OR (mm) 0.00 0.15 0.5 0.9

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Experiment Number 1 2 3 4

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Dataset3

BS (mm) 0.00 0.15 0.5 0.9

Speed (rpm) 1500 1500 1500 1500

In this study, three different data sets were used. In the first data set, there are signals obtained in different RPMs. The sample vibration signals of the signals consisting of 1500, 1740, 1800,

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1860, and 2100 RPM are given in Figure 5.

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Figure 5. Sample signals obtained in different RPMs. (A) 1500 RPM, (B) 1740 RPM, (C) 1800 RPM, (D) 1860 RPM, (E) 2100 RPM. In the second data set, there are indications of different faults taken at 1500 RPM. It contains

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signals on the error-free, inner ring, outer ring and ball faulty. The example signals of these

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fault conditions are given in Figure 6.

Figure 6. Signals of different fault types (A) faultless, (B) Inner ring fault, (C) Outer Ring Fault (D) Ball Fault

Journal Pre-proof In the last data set, there are sample signals at different fault sizes taken at 1500 rpm. Faults consist of vibration signals with inner ring, outer ring, ball in the size of 0.00mm, 0.15 mm,

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0.5mm, 0.9mm. Samples of these faulty signals are given in Figure 7.

Figure 7. Sample signals of different fault sizes (A) 0.15mm Fault, (B) 0.9mm Fault, (C)

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0.5mm Fault (D) Fault-free signals

In Table 4, some statistical information about the signals obtained from the experiment set is given. Mean, standard deviation, confidence intervals (95% confidence) and standard error

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statistics of the measured signals were given for a total of 11 experiments.

Table 4: Statistical properties of signals

Dataset 2

Experiment Number 1

-0.00288

Standard deviation 0.05874

2

-0.00277

0.08343

3

-0.00280

0.08491

4

-0.00257

0.08860

5

-0.00308

0.09979

1

-0.00733

0.07917

2

-0.00498

0.08316

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Dataset 1

Dataset

Mean

Confidence Interval -0.00304, -0.00271 -0.00300, -0.00253 -0.00303, -0.00255 -0.00282, -0.00231 -0.00336, -0.00280 -0.00756, -0.00711 -0.00521, -0.00474

Standard Error 1.223e-07 1.737e-07 1.768e-07 1.844e-07 2.078e-07 1.648e-07 1.731e-07

3

-0.03182

0.04642

4

-0.00673

0.01428

1

-0.00288

0.05874

2

-0.00712

0.09243

3

-0.00498

0.08316

4

-0.00673

0.01428

-0.03195, -0.03169 -0.00677, -0.00669 -0.00304, -0.00271 -0.00738, -0.00685 -0.00521, -0.00474 -0.00677, -0.00669

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Dataset 3

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3. Method

9.668e-08 2.974e-08 1.223e-07 1.924e-07 1.731e-07 2.974e-08

In this section LBP texture analysis method and the proposed approach were described. 3.1. Local binary pattern method

Local binary pattern (LBP), texture analysis operator, is a gray-level independent texture

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measurement method. The original LBP operator generates a label for each pixel of the image. This label is a binary number which is obtained by comparing the pixel to the pixels in the 3x3 neighborhood. Each pixel obtained from the image is obtained by binarizing the

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difference between itself and its neighbors with the step function. The LBP operator characterizes the relationship between the pixels in the image (xp, xc). This operator is

Here, the

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expressed by the following equation [31-34].

represents the center pixel generated by the LBP label, the

(1)

represents the

neighbors of the central pixel, the R is the distance of the neighbors from the center pixel, and the P specifies the number of neighbors processed. This structure shows that various circular neighborhoods can be used. Thus, it is possible to perform the analysis of textures of different

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sizes with LBP. Figure 8 shows an example of different LBP operators [35].

(P,R)=(8,1)

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(P,R)=(12,2.5)

(P,R)=(16,4)

Figure 8. LBP operators for different P, R values

The LBP value obtained by combining neighbor labels is used as a unique identifier for the center pixel. Figure 9 shows an example of labeling pixels with the LBP operator.

1th bit

91

92

94

95

94

77

0

0

0 1

0

1

1

(00010110)2

22

0

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90 8th bit

88

Decimal representation

2nd bit 3th bit

4th bit

75

8 bit binary representation

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Thresholding according comparison with the center

Sub-image

th

7 bit

th

6 bit

th

5 bit

Figure 9. Example of LBP operator calculation

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All of the LBP values obtained are not used in texture analysis. The uniform patterns used in the definition are those in the binary LBP code 0-1 or 1-0, and the number of passes 2 or less. For example, since the 000000 and 111111 patterns have 0 transition and 011000 and 110011 patterns have two transition, these values are uniform. However, the 4- transition 010100 and 5- transition 010101 patterns are not uniform. In the studies, it is seen that most of the surface of the investigated surfaces consist of uniform patterns [36].

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Since uniform patterns are used when extracting the LBP histogram, all non-uniform patterns are used as a feature, while a histogram was specifying a feature for each uniform pattern. When all patterns are analyzed, 256 different codes are formed for eight neighborhoods, and 58 of them are uniform. In this case, the LBP histogram has 59 compartments. For a picture, I(x,y), eight neighboring LBP histograms at 1-pixel distance are obtained as follows [37-40].

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Because of the uniform patterns in the histogram generation, n = 58, U(i) is the sequence that holds 58 of the possible 256 different patterns produced in the neighborhood of 8. This histogram contains information about micro-patterns such as edges, speckles, and flat areas on

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the image [41].

3.2. Proposed Method

In this study, a completely different approach was proposed from previous studies for the classification of bearing vibration signals. The block diagram of the proposed approach is

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given in Figure 10.

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Figure 10. Block diagram of the proposed method.

Block 1: Raw Bearing indicates vibration signals. Specifies bearing signals measured at different speeds, different fault types and fault sizes. Block 2: The pixel values of the two-dimensional gray images vary between 0-255. To

this range.

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transform the signals to images, the bearing signal values must be transformed to values in uses the following equation (Equation 3) to convert the signals to values

between 0-255 to indicate values on the signal. Equation 3 was used to convert signals to a specific range. Because the gray images consist of values in the range of 0-255, the bearing vibration signals were converted to the values in this range. There is no change in the appearance of the signal after transformation. An example transformation is given in Figure

Here,

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11.

,

and

largest value, respectively.

(3)

indicate the values on the signal, the smallest value and the

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Figure 11. Transformation of bearing signals to values in the range 0-255

Block 3: At this stage, bearing signals are converted to images. Depending on the length of the bearing signals, these marks are converted to N×M images. The transformation process is

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shown in Figure 12.

Figure 12. Transformation bearing the signals to images. (A) Bearing signals, (B) Image of signals

Journal Pre-proof The transformation of a sample signal to a gray image is shown in Figure 13. As seen from

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the figure, a textural image is obtained.

Figure 13. Transformation a bearing signal to a 80x80 gray image

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Block 4: The LBP method is the application phase to the gray images obtained. The application of the LBP method to the images is given in section 4.1. Block 5: After the LBP method is applied to gray images, LBP All, LBPU2 and LBPRI features

classification methods.

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are obtained from newly formed images. These feature vectors are used as input features for

Block 6: This phase is the classification stage. In this study, different machine learning methods have been used for classification. Machine learning methods such as K-nn (K nearest

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neighbor), Random Forest (RF), NaiveBayes, BayesNet (BayesNet) and artificial neural networks (ANN) were used as classifier. The accuracies were calculated according to 10-folds cross-validation scheme.

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(4)

Block 7: This phase is the decision and fault detection stage.

4.Results

4.1. Texture Analysis In this study, a new approach for classification of bearing vibration signals is proposed. First, the bearing vibration signals were transformed into two-dimensional gray scale images. Time-

Journal Pre-proof domain signals can be converted to images in the desired M×N dimensions. The size of the image can be adjusted to the desired length according to the length of the signals. However, the dimensions of the image should be within the dimensions of meaningful expressions. The images of bearing vibration signals obtained at speeds of 1500, 1740, 1800, 1860 and 2100 RPM are shown in Figure 14. The images formed by the signals of different fault types are given in Figure 15. Finally, textural images formed by bearing signals of different fault types

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are given in Figure 16. In the following example transformation, the signals were converted to

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100×100 sized gray scale images.

Figure 14. Vibration signals at different speeds and textural images formed (IR=0.15, OR=0.15, BS=0). (A) signals for 1500 RPM, (B) 1740 RPM, (C) 1800 RPM, (D) 1860 RPM

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and (E) 2100 RPM

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Figure 15. Signals of different fault types and textural images (for 1500 RPM). (A) IR=0,

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OR=0, BS=0; (B) IR=0.9, OR=0, BS=0; (C) IR=0, OR=0.9, BS=0; (D) IR=0, OR=0, BS=0.9

Figure 16. Signals of different fault dimensions and textural images (for 1500 RPM). (A) IR=0, OR=0, BS=0; (B) IR=0.15, OR=0.15, BS=0.15; (C) IR=0.5, OR=0.5, BS=0.5; (D) IR=0.9, OR=0.9, BS=0.9

Journal Pre-proof When Figures 14, 15, and 16 are analyzed, different RPM velocities of the images appear to be separated from each other. In the same way, it is understood that the textures of different fault types and fault dimensions are differentiated from each other. The texture is a property of the surface of an image. It can be expressed as a regular repetition of a texture or pattern on the image surface. The texture is one of the important features used to identify the relevant areas or regions of an image and is expressed as a collection of similar structures. Hence,

4.2. Classification of Bearing Vibration Signals

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texture can be considered as a similarity group in an image.

In order to classify bearing vibration signals, it is necessary to obtain the features from the generated images. LBP method was used to extract features from images. LBP is one of the simplest statistical approaches used for texture classification. LBP specifies the statistical intensity of an image or a local structure in an image. The texture of the images refers to the

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appearance, structure, and order of the parts of an object in the image. Texture analysis is the method that enables us to understand, classify and interpret the image. The LBP approach is based on the assumption that the local differences of the central pixel and its neighbors are

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independent of the central pixel itself. The relationships between pixels are expressed in binary patterns. The main advantages of LBP are: (1) a strong separator, (2) less computational cost than other methods, (3) being simple to apply. After the LBP method was applied to the images, the obtained features were classified by using 10-fold cross validation

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test with different machine learning methods. WEKA (Witten and Frank, 2005) was used as an open-source software for the classification process. In the classification process, RF, Knn, NaiveBayes, BayesNet and ANN machine learning methods were used. The machine learning methods used in the study have different parameters. The success rates may vary according to these parameters. For example, in the Knn method, K specifies the number of closest neighbors, and distance function specifies the distance method between samples. Learning rate ratio, hidden layer count, momentum value and transfer function type are the parameters

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used for ANN method. The success rate may vary according to these parameters. The values in Table 5 were decided by trial and error rate. High success rates were observed in all trials. The success rates observed with the use of 100×100-sized images are given in Table 6.

Table 5: Parameters for Machine Learning Methods

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Parameter(s) numIterations=100 K=3; distanceFunction=Euclidean Default parameter values Estimator=SimpleEstimator; searchAlgorithm=K2 learningRate=0.3, hiddenLayer=1; momentum=0.2; Tfunction=sigmoid

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Table 6. Success rates of different machine learning methods for 100x100 images Dataset

RF

Knn

NaiveBayes

BayesNet

ANN

Dataset1

91.7

80.8

95

90

95.8

Dataset2

100

100

100

100

100

Dataset3

100

100

100

100

100

Table 6 shows the success rates of RF, Knn, NaiveBayes, BayesNet, and ANN machine

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learning methods for three different data sets (Dataset1, Dataset2 and Dataset3). 100% success was observed in the classification Dataset2 and Dataset3; in other words, the signals of fault types and different fault sized signals. However, lower success rates were observed in

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the classification of signals in different RPMs. The highest success rate was 95.8% in the classification of different RPM signals. The highest success rate was determined with ANN in the classification procedures performed with different machine learning. Experiments were performed by transforming the bearing vibration signals to different sized

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gray scale images. LBP texture analysis method was applied to 50×50, 60×60, 70×70, 80×80, 90×90, and 100×100 images. The success rates of the features obtained from different sized images using ANN are given in Table 7.

Table 7. Success rates observed with ANN for images of different sizes

Dataset1 Dataset2 Dataset3

100×100

90×90

80×80

70×70

60×60

50×50

95.8

95.9

95.5

89.3

84.7

80.4

100

100

100

100

100

99.74

100

100

100

100

99.6

99.6

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Dataset

Looking at the results in Table 7, the success rate decreases when the image size decreases. Because the smaller the size of the image, the loss of information occurs and thus separating images from each other becomes difficult.

Journal Pre-proof All of the LBP features obtained are not used in texture analysis. Uniform patterns used in the definition, the number of 0 to 1 or 1 to 0 transition in the binary LBP code is 2 or less. For example, since the patterns 000000 and 111111 have 0 transitions, and 011000 and 110011 patterns have two transitions, they are uniform pattern. However, the 4-pass 010100 and 5pass 010101 patterns are not uniform. In the studies conducted, it is seen that most of the surfaces of the investigated surfaces consist of uniform patterns. When all patterns are

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examined, 256 different codes are formed for eight neighborhoods, and 58 of them are uniform. In this case, the LBP histogram has 59 compartments. Patterns with a maximum of two-bit changes in circular binary notation are defined as a uniform. Independent patterns from rotation angle are obtained with transforming minimum value by rotating each bit circularly, with the largest value being the last element. Some of the LBP patterns are uniform, and some are rotationally independent. The success rates observed with all LBPall,

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uniform LBPU2, and rotationally independent LBPRI patterns are given in Table 8.

Table 8. The success rates observed by using different feature groups # of Features

Dataset1

Dataset2

Dataset3

LBPAll

59

95.8

100

100

LBPU2

58

95.4

100

100

LBPRI

10

82.5

99.5

100

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Features

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Table 8 shows high success rates with Dataset2 and Dataset3 for all feature groups. It is clear that fault sizes and fault types are easily differentiated for all feature groups. However, there was no high success for all feature groups to separate the signals at different speeds. Acceptable success rates were observed with the use of uniform or all feature groups. One of the important parameters of the LBP method is P, which indicates the number of neighbor to be taken around a pixel. A different number of features are obtained according to

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the different values of P. Experiments were performed for different values of P, such as P = {4,6,8 and 10}. The success rates are given in Table 9.

Table 9. The success rates observed according to different neighbor numbers #Neighbors (P Value) P=4

#Features

Dataset1

Dataset2

Dataset3

15

93.8

100

100

P=6

33

91.3

100

99.5

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59

95.9

100

100

P=10

93

93.3

100

100

The results show that P has no significant effect on success. Dataset2 and Dataset3 images are easily differentiated. However, the highest success rate for dataset1 was 95.93%.

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4.3. Comparison of the Obtained Results with Literature

In Table 10, the studies related to the classification of bearing vibration signals are given. In this study, tests related to three different scenarios were performed. In the first scenario, the classification of bearing signals at different speeds was realized. In the second scenario, the error detection was performed from the signals obtained from the bearings with different fault types. In the last scenario, the error size was determined from the signals with different error

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sizes. A high success rate was observed in all scenarios. As can be seen from the table, the high success rates obtained with the proposed approach were found to be more successful than the studies in the literature.

Pan et al. [43]

Li et al. [44]

Zhang et al. [45]

- Symplectic Geometry Matrix Machine (SGMM)

- Deep Stacking Least Squares Support Vector Machine (DSLS-SVM) - Deep Residual Learning (DRL) - Gated Recurrent Unit Based Non-Linear Predictive Autoencoders (GRUNP-DAEs) - Synchro squeezing Transform (SST) and Deep Convolutional Neural Network (DCNN) - Rotation Forest

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Liu et al. [46]

Model - Convolutional Neural Network (CNN)

Zhao et al. [47]

Kavathekar et al. [48] Seera et al. [6]

Dataset - Case Western Reserve University Bearing Fault Database (CWRU)

Fault Type - Normal State (NS), Inner Ring Fault (IRF), Outer Ring Fault (ORF), and Ball Fault (BF)

Accuracy - 97.74%

- CWRU - The experimental roller bearing data of Hunan University - CWRU

- NS, IRF ORF, and BF, - Normal, IRF ORF, and BF - NS, ORF, IRF and BF

- 100 %

- CWRU

- NS, ORF, IRF and BF - NS, ORF, IRF and BF

- 99.99%

- Experimental setup of authors

- NS, IR, OR

- 98.3%

- CWRU

- NS, ORF, IRF and BF - NS, ORF, IRF and BF

- 75%

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Author(s) Hoang &Kang [42]

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Table 10: The Reported Studies on the Bearing Fault

- Fuzzy Min-Max Neural Network Random Forest FMM-

- CWRU

- CWRU - Experimental setup of authors

- 99.83% - 99.90%

- 99.75%

- 99.9% - 99.8%

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He & He [50]

Vakharia et al. [51] Konar and Chattopadhyay [52]

RF - Compressive Sampling- Laplacian Score Logistic Regression Classifier (CS-LS-LRC) - Discrete Fourier Transform -Inverse Discrete Fourier Transform Auto encoder (DFT-IDFT auto encoders) - Fisher Score, ReliefF, Wilcoxon Rank - Continuous Wavelet Transform (CWT)

- CWRU

- NS, ORF, IRF and BF

- 99.9%

- Experimental setup of authors

- NS, ORF, IRF, BF, Cage

- 99.92%

- Experimental setup of authors - Experimental setup of authors

- NS, IRF ORF, and BF - Normal State (NS), Fault State (FS)

- 100 %

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Ahmed et al. [49]

- 73.33 % - 90 % - 93.33 % - 96.67 % - 100 %

- Continuous Wavelet Transform (CWT)

- Experimental setup of authors

- NS, ORF, IRF and BF

- 76 % - 94.66 % - 98.66 %

Dou and Zhou [54]

- K-Nearest Neighbor algorithm (KNN), Probabilistic Neural Network (PNN), ParticleSwarm Optimization optimized Support Vector Machine (PSO-SVM), Rule-Based Method (RBM) - Fuzzy Lattice Classifier (FLC)

- CWRU

- Normal State (NS), Fault State (FS)

- 92.81 % - 94.38 % - 99.06 % - 99.69 %

- CWRU

- NS, ORF, IRF and BF

- Signal2Image+LBP

- Experimental setup of authors

- NS, ORF, IRF, BF,

- 92% - 96% - 100% - 100%

5. Discussion

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Li et al. [55]

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Kankar et al. [53]

- Fault size,

- 100%

- Motor speed

- 95.9%

As a result of the rapid development of technology, the industrial importance of rotary machines has increased. Bearings are one of the most critical parts of rotating machines.

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Bearings are precision parts that allow rotary machines to move at high speeds with low friction and at the same time support effective loads on the shaft. The bearings are one of the most important elements of the motor-shaft mechanism, including the inner and outer rings, the various types of ball bearings rolling between two rings, and also the bearings allow rotation with the least friction. Even if the costs of the bearings are too low compared to the machines, the malfunctions on the bearing can cause malfunctions in the whole mechanism and disruptions in the system.

Journal Pre-proof When the literature is examined, there are many research studies conducted using different feature extraction schemes and open source data sets for fault classification techniques. In this study, the models that can predict the type and the magnitude of the fault and also the rotational speed of the motor to which connected to bearing are determined. A new feature extraction method based on texture analysis is proposed for bearing vibration

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signals. Bearing vibration signals were first converted to gray scale images. It is understood from the images that the signals of different bearing failures form different textures. Then, texture features are obtained from these images with LBP. These features are classified using different machine learning methods by bearing vibration signals. Three different data sets were used to test the proposed approach.

Different approaches were used in the classification of bearing failure types (Table 10). Success rates range from 75% to 100%. It is seen that there is not enough work in the

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classification of vibration speeds and determination of bearing error dimensions from vibration signals.

In this study, for the first data set, the classification of the signals consisting of very close to

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each other was carried out. A success rate of 95.9% was observed for this data set. The second data set consists of faulty signals at different points of the bearing (inner ring, outer ring and ball) measured in the same RPM. The type of error has been determined. A 100% success rate was obtained for this data set. The final data set is composed of the fault dimensions (mm) of

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different ratios. With the proposed approach, a 100% success rate was obtained in the classification of these signals. As a result, according to the results obtained, high success rates were observed in the classification of vibration signals by the proposed method. It is

areas.

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considered that the proposed approach can be used in the classification of signals in different

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*Declaration of Interest Statement

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There is no ‘Conflict of Interest’ in the publication of the manuscript “An Improved Feature Extraction Method Using Texture Analysis with LBP”.

*Author Contributions Section

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Kaplan KAPLAN and H. Metin ERTUNÇ conceived of the presented idea. Yılmaz KAYA and Melih KUNCAN developed the theory and performed the computations. Mehmet Recep MİNAZ verified the analytical methods. All authors discussed the results and contributed to the final manuscript.