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JOURNAL OF CURRENT RESEARCH IN SCIENCE ISSN 2322-5009 CODEN (USA): JCRSDJ Available at www.jcrs010.com JCRS S (1), 2016: 771-780 A new diagnosis of severity broken rotor bar fault based modeling and image processing system Hassan Divdel1, Mohammad Hosseinzadeh Moghaddam2, Ghafour Alipour3 1. Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran 2. Department of Computer Engineering, Hashtrood Branch, Islamic Azad University, Hashtrood,Iran 3. Department of Computer Engineering, Hashtrood Branch, Islamic Azad University, Hashtrood,Iran Corresponding Author email: [email protected] K E Y W O R D S: fault based modeling, image processing, broken rotor bar, induction motor. ABSTRACT: This paper proposes a new diagnosis of severity broken rotor bar fault based modeling and image processing system approach. It is shown the rise of broken bars and load level increases harmonics of the stator currents in the fault condition. Therefore, fully automatic pattern recognition methods are required to identify induction motor severity broken rotor bar fault .this paper proposes a dynamic model to analyze broken rotor bar fault in induction machines. In order to evaluate the ability of the proposed method several experiments are performed and a sets of data are gathered before and after fault under noise condition. Simulations and experimental results were performed to confirm the validity of the model. Introduction Induction motors are critical components of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motors applications1. Although induction motors are reliable, they are subjected to some failures. Therefore, in the past two decades, there has been substantial amount of research to provide new condition monitoring techniques for induction motors mostly based on analyzing vibration signals, or other signals such as current, and hence a number of commercial tools are available in this area1-7. One of the most widely used techniques to obtain information on the health state of induction motors is based on the processing of the stator line current. Typically in the motor fault diagnosis process, sensors are used to collect time domain current signals2. In 1997, an adaptive statistical time-frequency method was used for the detection of bearing defects by stator current analysis. The key idea in this method is to transform motor current into a time-frequency spectrum to capture the time variation of the frequency components and to analyze the spectrum statistically to distinguish faulty conditions from the normal operating condition of the motor. This method was used for broken rotor bar and bearing fault detection in 1999. In recent years more advanced signal processing methods such as wavelet analysis and wavelet packet analysis have been used. These methods require more computational effort and are not very successful in detecting defect location using motor current signature analysis (MCSA). In order to overcome these problems, the idea of using park’s vector to precisely locate a fault and at the same time to reduce the computational complexity was proposed by Angelo et al.3. That paper presents a dynamic model suitable for computer simulation of induction machines in a healthy state and faulty state, Then based on the image processing system identify rotor broken bars fault, as well as their correspondent severity and Finally to evaluate the ability of the proposed method several experiments are performed, and a sets of data are gathered before and after fault under noise condition. Simulations and experimental results were performed to confirm the validity of the model. This paper is organized as follows: In Sec. II common faults of induction motors are listed. In the first part of Sec. II fault detection using stator phase current signature is discussed. In the second part the symmetric model is introduced and in the next two part stator winding fault and broken bars model are discussed. In the first part of Sec. III Concordia vector approach is introduced and in the next two parts Pattern recognition approach and Image processing based systems are discussed. Finally the simulation results are summarized in Sec. IV. Problem Description The common faults of induction motors can be classified as stator faults (inter-turn), rotor faults (broken rotor bar/end-rings) and mechanical faults (bearing failures, air gap eccentricity). Approximately 40-50% of faults of induction J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 motors are bearing related faults, 30-40% are stator faults, and 5-10% are rotor faults8. Fig. 1 shows the general classification of induction motors faults. Fault Detection Using Stator Phase Current Signature Stator phase current is measured directly using current transformer (CT) or current transducer, and then unwanted high frequencies and noise components are filtered. For a certain time, depending on the selected frequency resolution, a window of sampled points is recorded. The Fast Fourier transform (FFT) algorithm is then applied to obtain the stator current spectrum or the signature. For a healthy motor this signature contains the fundamental supply frequency component and other components are neglected. When a fault exists inside the motor some of frequency components magnitudes are increased with respect to the fundamental component depending on fault type and severity. Thus, by identifying frequencies and magnitudes of these components with respect to fundamental component, both fault type and severity can be addressed 8. This technique is simple and requires only the measurement of one phase current. This reduces the cost of hardware and memory size required. Fig. 2 and Fig. 3 show the effect of noise on the healthy and unhealthy motor respectively which the identification of noise from the harmonics is difficult. Symmetric Model The voltage equations which describe the induction machines are established5. Some of the machines inductance which are functions of the rotor speed, where upon the coefficients of the differential equations (voltage equations) which describe the behavior of these machines, are time-varying except when the rotor is stalled. A change of variables described by Eq. (5) is often used to reduce the complexity of differential equations. So the voltage , the flux and the current can be expressed in arbitrary reference frame. The equations for the stator and rotor flux are: , (1) , (2) , (3) . (4) The subscript indicates the variables, the parameters and transformation associated with rotor circuit. The machine electromagnetic torque ( , the load torque ( and the rotor velocity ( are related as, . (5) Where is the rotor inertia and is a damping coefficient associated to the mechanical rotational system of the machine and mechanical load. The differential equations derived above can be solved by the fourth-order Runge-kutta method. The stator and rotor currents can be obtained from: , (6) , (7) , (8) . (9) Where , , , , , . The parameters and are stator and rotor self-inductance, and are stator and rotor leakage inductances and is mutual inductance. The expression for the electromagnetic torque in terms of arbitrary reference frame for a p-pole machine may be expressed as: ( ). (10) Stator winding fault In this section, a model of induction motor which includes short circuit is applied, Leakage inductance of the winding which is shorted, is obtained by the following equations, in these equations is the ratio of short period2. ( , (11) , (12) ), , , , (13) (14) (15) (16) 772 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 . Where , , , (17) , , , and . Broken Bars Model Of Induction Motors Fig. 4 shows a broken rotor bar in an induction motor which in such cases With regard to fault machine models, many researchers have developed methods for the analysis of the steady state and dynamic behavior that are able to introduce a specific fault. The dynamic models give the solution as instantaneous values from which the signal components can be computed under quasi-steady state. With respect to simulate the defect of bar breaks, a defect resistance is added to the corresponding element of the rotor matrix , [ ] [ ] [ ]. (18) Solutions Concordia Vector Approach In three-phase induction motors, the connection to the main does not usually use the neutral, Therefore, the mains current has no homopolar component. A two-dimensional (2-D) representation is based on the current Concordia vector, sometimes erroneously called Park vector. The current Concordia vector components, ( are functions of mains phase variables which are: √ √ √ , (19) . (20) In ideal conditions, these-phase currents lead to a Concordia vector with the following components, √ √ √ , (21) √ . (22) Where is the supply phase current maximum value and is the supply frequency. Fig. 5 shows the overall structure of the stator currents acquisition that are transformed into equivalents two-phase using the Concordia conversion. Pattern Recognition Approach Most of the common methods used to identify and classify a faulty induction motor are based on the analysis of the stator currents. The proposed approach also uses the analysis of stator currents, however, in this methodology the problem is converted into a pattern recognition analysis. Thus, considering a three-phase induction motor without neutral connection, ideal conditions for the motor and an unbalanced voltage supply, the stator currents are given by Eq. (23), where , and denote the three stator currents, their maximum value, their frequency, their phase angle and denotes time, ( ) . (23) ( ) In the proposed approach the currents are recognized as typical patterns for each faulty mode. This is accomplished by analyzing them in a 2-D current state space by using Concordia vector approach. For a healthy motor the corresponding current pattern is a circle centered at the origin of the coordinates of expression (23) where R denotes its radius so, . (24) For an induction motor working in a faulty mode, the previous pattern is no longer valid. So, with some rotor broken bars the current pattern is no longer a circle but a donut pattern is obtained. { Image Processing Based System In order to implement the proposed pattern recognition based fault detection algorithm, a feature-based recognition procedure of the stator current pattern, independent of their shape, size and orientation must be obtained. The key point to solve this problem is to find efficient invariant features. Particular attention is paid to visual-based features obtained in the image processing system. The proposed image-processing algorithm is divided into three steps: the stator current Park transformation, the image composition, and the feature extraction. The inputs for the image processing based system are the three-phase stator currents and the output is the identification of the motor working condition. In the image composition step, the three-phase stator current vector is converted into a binary image contour. 773 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 The last step of image-processing algorithm is the features extraction. Once a digital image has been segmented, measurements can be performed to scrutinize the shape and size of the spatial pattern in the image. Several image analysis applications commonly employ geometric features10. The proposed methodology uses an effective algorithm, based on visual features, for induction motor fault detection. In the feature extraction step the key features used for fault diagnosis are: contour section of the object, region orientation, and severity index. Assume that the pixels in the digital image are piecewise constant and that the dimension of the bounded region image for each object is denoted by , where and express, respectively the number of rows and columns. In these conditions the contour section is obtained using the following algorithm: In the nearest ⁄ line of the digital image, start the search from the 1st column until the first point which belongs to the region contour is found. The coordinates of the first point are . The coordinates of the last point are . Continue search of the contour points until reaching the column ( . The last contour point will have the coordinates . The region contour section will then be given by Eq. (25). . (25) The severity index for the rotor broken bars are given by Eq. (26), where and denote the maximum and minimum values of the contour section respectively. Those severity indexes assume values between zero and one, being the absence of any fault reported by a zero severity index ( =0). (26) The and values are obtained computing the distances between the center of the closed contour and the two contour and ( points ( , ), so (27) . (28) When the contour section is a straight line, the two contour points are equal and a zero severity index is obtained for the rotor broken bars. Fig. 6 shows the flow chart of the proposed algorithm. Simulation results Defect resistance effect on the pattern of the stator current The simulation results of the induction motor with and without fault were obtained using the Matlab/Simulink environment. The broken bars model implement with considering the different levels severity fault. Fig. 7 shows the obtained 2-D stator current pattern for a healthy motor. After applying the image processing based system, the computed severity indexes are very close to zero, denoting a healthy motor. The obtained contour section was one and the contour region pattern a circle. Figs. (8-10) show the obtained 2-D stator current pattern for a faulty motor by adding a defect resistance with a different level of severity fault ( . In this condition, due to small rotor asymmetries which force a small unbalance in the line currents, after applying the image processing based, the computed severity indexes based on severity fault are: 0.02840, 0.0709, 0.1134. Stator Winding Fault Effect On The Pattern Of The Stator Current The simulation results of the induction motor in state of healthy and stator winding fault with open each phases has been done Computing software. Fault Severity Index In state without fault value 0.228 obtained. With the opening of each of the phases, of this index is obtained equal to 0.8005. However, this index does not provide information on the type of phase fault. Another index the orientation is used to detect the phase fault. The pattern of the stator current (dq) for this type of fault is ellipse of shape. Experimental Results The data on the three-phase stator currents measured in January 2006 in normal rotor speed of 1450 rpm to 1469 rpm and second time three-phase stator currents in the rotor pierced twice speed of 1460 rpm with a sampling frequency of 12kHz and velocity measured with light Telemetry .Due to an imbalance in network voltage and presence of noise a 2-D stator current (d-q) pattern for healthy motor is shown in Fig. (5). So for detect the curve of healthy and faulty motor, severity index analysis was used. The severity index analysis based image processing system According to Fig. (5) after image processing based 774 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 system, the severity index computed 0.1768, and a 2-D stator current (d-q) pattern for faulty motor is shown in Fig. (6) and the severity index computed 0.2114. Figure 1: Induction motor’s faults classification. Figure 2: The stator current spectrum of healthy motor. Figure 3: The stator current spectrum of faulty motor (broken bar rotor). 775 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 Figure 4: broken bar rotor induction motor. Figure 5: Overall structure of the stator currents acquisition. Figure 6: Flow chart of the proposed Image processing based algorithm. 776 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 Figure 7: Current d-q Vector pattern for the healthy motor ( =0.005) - simulation results. Figure 8: Current d-q Vector pattern for the faulty motor (broken rotor, =0.05) - simulation results. Figure 9: Current d-q Vector pattern for the faulty motor (broken rotor, =0.1) - simulation results. 777 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 Figur. 10: Current d-q Vector pattern for the faulty motor (broken rotor, =0.15) - simulation results. Figure 11: Current d-q Vector pattern for the faulty motor. Figure . 12: Current d-q Vector pattern for the faulty motor- short circuit phase A. 778 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 Figure 13: Current d-q Vector pattern for the faulty motor- short circuit phase B. Figure 14: Current d-q Vector pattern for the faulty motor- short circuit phase C. Figure 15: Current d-q Vector pattern for the healthy motor- experimental results. 779 J. Curr. Res. Sci. Vol., S (1), 771-780, 2016 Figure 16: Current d-q Vector pattern for the faulty motor (broken bar rotor)- experimental results. Conclusion In this paper an image processing based classifier for detection and diagnosis of induction motor rotor fault was presented. In order to implement the proposed pattern recognition based fault detection algorithm, a feature-based recognition procedure of the stator current pattern, independent of their shape, size and orientation must be obtained. The key to solve this problem is to find efficient in variants features. Particular attention is paid to visual-based features obtained in the image processing system. 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