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1 Swinburne Research Bank Tashakori, A., & Ektesabi, M. (2013). A simple fault tolerant control system for Hall Effect sensors failure of BLDC motor. Originally published in Proceedings of the 8th IEEE Conference on Industrial Electronics and Applications (ICIEA 2013), Melbourne, June 2013 (pp ). Piscataway, NJ: IEEE. Available from: Copyright 2013 IEEE. This is the author s version of the work, posted here with the permission of the publisher for your personal use. No further distribution is permitted. You may also be able to access the published version from your library. The definitive version is available at Swinburne University of Technology CRICOS Provider 00111D swinburne.edu.au

2 A Simple Fault Tolerant Control System for Hall Effect Sensors Failure of BLDC Motor A. Tashakori, IEEE Student Member and M. Ektesabi, IEEE Member Swinburne University of Technology, Melbourne, Australia Abstract This paper presents a novel fault tolerant control system for Hall Effect position sensors failure of permanent magnet brushless DC (BLDC) motor in a closed loop control scheme. The proposed system is capable to detect and identify the Hall Effect sensors breakdown based on sensors signals and Discrete Fourier Transform (DFT) analysis of the measured line voltages of motor. In this paper behavior of BLDC motor is studied for Hall Effect sensors breakdown through simulation model. BLDC motor simulation model were validated first by experimental data under no fault condition. Analyzing the simulation results of the various sensor breakdowns leads to develop an expert system for Hall Effect sensors failure diagnosis in BLDC motor. A simple method is presented to generate the commutation signal of faulty Hall Effect sensor to maintain the proper operation of BLDC motor after fault occurrence. The proposed fault tolerant control system does not need massive computational efforts and can be implemented as a subroutine in the main control program of the BLDC motor. The simulation results show correct performance of the proposed fault tolerant control system. I. INTRODUCTION BLDC Motors have been used widely in different industrial and commercial applications since 1970 s. It is a novel DC motor that is commutated electronically due to absence of brushes. High efficiency, high speed ranges, high dynamic response (due to permanent magnet rotor) and less maintenance requirements (due to absence of brushes) are immediate advantages of BLDC over brushed DC motor. Complex control algorithm because of electronic commutation and higher manufacturing price due to high cost of ferromagnetic materials are disadvantages of BLDC compare to the brushed DC motor [1]. However BLDC motors are suitable and of interests for the applications such as drive train of electric vehicle that efficiency and precise controllability of electric motor have critical effect on the performance of overall system. Electronic commutation of BLDC motor depends on accurate detection of permanent magnet rotor position. There are two main control strategies of BLDC motor based on rotor position detection methods. In the first method sensors are used to detect the rotor position and in the second method the position of rotor is detected through sensorless techniques. Optical encoders are used for applications with high resolution requirements and Hall Effect sensors for applications with low resolution requirements [2]. Hall Effect sensors are mounted inside the BLDC motor with 120 electrical degree phase difference to detect rotor position. Output of each sensor is high (logic 1 ) for 180 electrical degree and is low (logic 0 ) for the next 180 degree with respect to rotor position. Decoding Hall Effect signals result to choose the proper voltage space vector for switching of the three phase Voltage Source Inverter (VSI) drive of BLDC motor. In this paper position sensor failure of a three phase (star connection) permanent magnet BLDC motor with the inbuilt Hall Effect sensors is discussed. BLDC motor and three phase VSI drive are shown in Fig. 1. Fig. 1. BLDC motor drive system Failure of Hall Effect sensors effect directly on the applied voltages to the BLDC motor and degrade the performance of overall motor drive. Development of fault tolerant control systems increases reliability of electric motor drive. The fault tolerant control system mainly should accomplish the following tasks, 1) fault detection; 2) fault identification; 3) remedial strategies. Various possible faults may happen in a Hall Effect sensor such as flaws in the core (corrosion, cracks, residual magnetic fields and core breakage), changes in the bias current, change in the magnetic properties of the ferrite core due to temperature variations, changes in the orientation of the induced magnetic field in the sensor (due to mechanical shocks or other reasons) [3]. Any of these faults may result to breakdown of the Hall Effect sensor in BLDC motor. Unbalanced positioning of Hall

3 Effect sensors is another common fault in BLDC motor that increases the low-frequency harmonics in torque ripple and degrades the overall drive performance [4], however it does not result in sensor breakdown. There are a few research work on Hall Effect sensor failure of BLDC motor specifically for electric vehicle applications. Jeong et al. have presented a control strategy that provides fault tolerance to the major sensor faults which may occur in an interior-permanent-magnet-motor (IPMM)-based electric vehicle propulsion drive system [5]. Position sensors fault is detected through difference between the estimated rotor angle and the actual measured one through a sensorless algorithm based on extended EMF in rotating reference frame. In this approach reconfiguration to sensorless control scheme is introduced to rectify the fault and maintain the proper operation of the motor after fault occurrence is detected. Complexities of sensorless control scheme and transition algorithm to snesorless control are the main drawbacks of the proposed method. Simulation results of BLDC motor have been presented during Hall Effect sensors failure for a lunar rover wheel application [6]. Decoded switching signals of VSI and current waveforms of all phases during Hall Effect sensor breakdown are shown; however there is not a comprehensive discussion on the presented simulation results, the fault diagnosis technique or remedial strategies in the paper. Signal analysis, model based, and expert systems (knowledge based methods) are three basic classifications for fault detection and diagnosis algorithms for BLDC motor [7]. In this paper, an expert fault diagnosis system of Hall Effect sensor failure in BLDC motor is discussed. In the section two behavior of BLDC motor is studied during Hall Effect sensor failure. Fault diagnosis technique and remedial strategies are discussed in sections three and four respectively. Integral (PI) controller is designed to adjust the duty cycle of high frequency Pulse Width Modulation (PWM) signal based on speed error to control the speed of motor in a closed loop scheme. An embedded code is used in Simulink model to produce high frequency PWM signal. A duty cycle controlled PWM signal is applied to all switches of VSI. TABLE I EXPRIMENTAL BLDC MOTOR SPECIFICATION Description Value Unit DC voltage 24 V Rated speed 3000 RP M Rated Torque 0.28 N.m Phase resistance 2 ohm Phase inductance 4.60 mh Inertia 4.43e-6 kg.m 2 Torque constant N.m/A Poles 10 - Experimental BLDC motor and simulation model are tested under the same operating conditions for 2000 RPM reference speed and 0.1 N.m load torque. The line voltage and corresponding Hall Effect signal of phase A for experimental setup and simulation model of BLDC motor are shown in Fig. 3. II. HALL EFFECT POSITION SENSOR FAILURE A three phase low voltage BLDC motor is used as a practical test motor to validate simulation results. Experimental setup of BLDC motor is shown in Fig. 2. Fig. 2. Experimental setup BLDC motor is simulated in Simulink using SimPowerSystems library. The experimental test motor specification data which is given in Table I have been used in the simulation model. The three phase variable source inverter drive of BLDC motor is simulated using MOSFET switches. A Proportional Fig. 3. Line voltage and Hall Effect signal of phase A of BLDC motor It can be seen in figure that the simulation results are in

4 a good agreement with the test data of experimental BLDC motor. Subsequently Hall Effect sensor faults are applied to the validated simulation model of BLDC motor. Characteristics of BLDC motor such as speed, torque, line voltages and current of all phases are studied for failure of Hall Effect position sensors. Breakdown of the position sensor cause the output signal of sensor to be whether constant high (logic 1 ) or constant low (logic 0 ) and it does not change according to the rotor position. Therefore behaviour of BLDC motor is studied for the both conditions (H A = 0 and H A = 1) separately during failure of Hall Effect position sensor of phase A. A. Hall Effect signal is constant zero Hall Effect sensor fault of phase A (H A = 0) is applied to the validated BLDC motor model at t = 0.5s while the motor was running under 0.1 N.m torque load at 2000 RPM speed. Speed and torque characteristics of BLDC motor for H A = 0 fault condition are shown in Fig. 4. Fig. 5. Line voltage of BLDC motor for H A = 0 the BLDC motor. Effect of various Hall Effect sensors fault on the switching signals of VSI are given in Table II. Switching signals S 1 and S 6 are constant zero (switches S 1 and S 6 remain open circuit) for H A = 0 fault condition. Change of applied voltages cause variation of the stator phase currents of the BLDC motor that effects directly on electrical torque of motor and increases torque ripple. Phase currents of BLDC motor for H A = 0 fault condition are shown in Fig. 6. Fig. 4. Speed and torque characteristics of BLDC motor for H A = 0 It can be seen in Fig. 4 after fault occurrence that the speed of motor is oscillating and electrical torque ripple amplitude is increased remarkably. The line voltages of BLDC motor for H A = 0 fault condition are shown in Fig. 5. Voltage of neutral point of BLDC motor is not stable during high frequency PWM switching, therefore line voltages of BLDC motor are measured with respect to negative terminal of DC link of inverter. TABLE II SENSORS FAULT EFFECT ON THE SWITCHING SIGNALS Fault type Open swiches of VSI H A = 0 S 1, S 6 H A = 1 S 5, S 2 H B = 0 S 3, S 2 H B = 1 S 1, S 4 H C = 0 S 5, S 4 H C = 1 S 3, S 6 Hall Effect sensor failure causes change of the switching signals of VSI and effect directly on the applied line voltage of Fig. 6. Phase currents of BLDC motor for H A = 0 B. Hall Effect signal is constant one Hall Effect sensor failure of phase A (H A = 1) has been applied to the validated BLDC motor model at t = 0.5s while the motor was running under 0.1 N.m torque load at 2000 RPM speed. Speed and torque characteristics of BLDC motor for H A = 1 fault condition are shown in fig. 7. It can be seen that speed and torque responses of BLDC motor for H A = 1 are quite similar to the ones for H A = 0 fault condition. However by checking switching signals of VSI, it can be observed that switches S 2 and S 5 are constant zero (switches S 2 and S 5 remain open circuit) after fault occurrence which are not the same as previous fault condition. Therefore applied voltages to the motor are completely different for H A = 1 fault condition. The line voltages of BLDC motor for H A = 1 fault condition are shown in Fig. 8.

5 rotor position. Electronic commutation is done by decoding the position sensor signals. Decoding rules of Hall Effect signals to choose a proper switching vector of VSI are shown in Table III. As it can be seen in table there is no condition that all three hall signals being one or zero at a same time. TABLE III DECODING RULES OF HALL EFFECT SIGNALS Rotor angle Hall A Hall B Hall C Conducting (Electrical degree) switches S 1, S S 1, S 6 Fig. 7. Speed and torque characteristics of BLDC motor for H A = 1 Fig. 8. Line voltage of BLDC motor for H A = 1 Phase currents of BLDC motor for H A = 1 fault condition are shown in Fig. 9. It is depicted from figure that current of all phases are deteriorated after fault occurrence. Phase currents for H A = 1 are approximately symmetric projections of the phase current waveforms of H A = 1 fault condition with respect to the time axis S 3, S S 3, S S 5, S S 5, S 4 The addition of Hall signals introduced by (1) is a fault signature for Hall Effect sensors breakdown. Maximum possible value of H f is 2, where the minimum possible value is 1 (1 H f 2) for each specific electrical angle section. If H f value goes over of these limits Hall Effect sensor failure is detected. Hall Effect sensors Fault Flag (HFF) is introduced for sensor fault detection. HFF is set to 1 if H f value is more than 2 (it means that one of the position sensor signals is constant one), HFF is set to -1 if H f value is less than 1 (it means that one of the position sensor signals is constant zero) and HFF is 0 in case of no fault. Maximum fault detection time is the time of on electrical rotation of rotor which is quite fast. H f = H A + H B + H C (1) However identification of faulty sensor is impossible through Hall Effect sensor Fault Flag. As it is discussed in previous section, the line voltages of BLDC motor are deteriorated due to position sensor failure. Therefore DFT analysis is used for pattern recognition of the line voltages. DFT of line voltages are calculated by (2) for specific intervals of time. The minimum time interval for proper fault detection is one electrical rotation of motor. Spectral Energy Density (SED) of computed frequency spectrum is determined by (3). SED difference of successive time intervals are calculated and analyzed to identify the faulty position sensor. V (f) = N 1 n=0 V n e j2πk n N k = 0, 1,..., N (2) E m (f) = V (f) 2 (3) Fig. 9. Phase currents of BLDC motor for H A = 1 III. FAULT DIAGNOSIS Each Hall Effect signal of BLDC motor has specific value at each instant of time with respect to permanent magnet ε m = E m (f) E m 1 (f) (4) Calculated SED errors of the BLDC motor line voltages for Hall Effect sensor failure of phase A for H A = 0 and H A = 1 faults are shown respectively in Table IV and Table V. As it is shown in the tables, it is possible to distinguish two

6 failure conditions of Hall Effect sensor through SED errors. The simulation model is also run for position sensor faults of phases B and C. The line voltages of BLDC motor for Hall Effect sensor faults of all phases are studied through the simulation model. TABLE IV SED VALUES FOR H A = 0 FAULT CONDITION Description Phase A Phase B Phase C SED befor fault [E m 1 (f)] SED after fault [E m(f)] SED error [ε m] TABLE V SED VALUES FOR H A = 1 FAULT CONDITION Description Phase A Phase B Phase C SED befor fault [E m 1 (f)] SED after fault [E m(f)] SED error [ε m] phase difference from the other two Hall Effect signals. A new and simple method has been introduced to calculate electrical degree delays with respect to the time [8]. If motor speed is known and it does not change during commutation intervals (controller keeps the motor speed constant), the time of one electrical degree rotation of BLDC motor can be determined in seconds by (5), where P is the pole numbers and ω ref is the reference speed of the controller in RPM [8]. t = 1 P/2(6 ω ref ) The proposed technique is implemented as embedded Matlab function in Simulink file of BLDC motor model. Fault tolerant control system of BLDC motor is tested under 0.1 N.m load torque and 2000 RPM reference speed for H A = 0 fault occurrence at t = 0.5 s. Speed characteristics of BLDC motor with the implemented fault tolerant control system is shown in Fig. 10. (5) Hall Effect Identification Flag (HIF) of each phase is introduced for faulty sensor identification. Numeric values are given to HIF of each phase according to SED errors of all three line voltages of BLDC motor as below, HIF is -1 if SED error is negative; HIF is 1 if SED error is positive. A multidimensional knowledge based table is developed for position sensors fault diagnosis according to Fault detection and identification flags (HFF and HIF) by analyzing the simulation results of BLDC motor for Hall Effect position sensor faults of all phases. One of the advantages of this technique is that it is not necessary to know the exact line voltages of BLDC motor for different speed and loads in advance. Multidimensional knowledge based table for position sensor fault diagnosis of BLDC motor is shown in Table VI. TABLE VI RULE BASED POSITION SENSOR FAULT IDENTIFICATION TABLE Fault type HIF HIF HIF HFF phase A phase B phase C No fault X X X 0 H A = H A = H B = H B = H C = H C = IV. REMEDIAL STRATEGY After identification of the faulty Hall Effect sensor, corresponding sensor signal is disconnected and is substituted with a generated commutation signal by microcontroller. Commutation signal is generated based on 120 electrical degree Fig. 10. Speed response of fault tolerant controlled BLDC motor drive for H A = 0 fault As it can be seen fault detection time is remarkably fast. Fault identification takes more time due to DFT analysis of the line voltages. Total time of fault diagnosis is 113 milliseconds for the simulation model that is fast enough to avoid further faults in BLDC motor drive. Amplitude of speed oscillation is reduced as soon as the signal of faulty Hall Effect sensor is substituted by microcontroller with the generated commutation signal. Speed of the BLDC motor was following the reference speed after almost 0.2 seconds. V. CONCLUSION Fault tolerant control system for Hall Effect position sensors failure of BLDC motor is discussed in this paper. Behavior of BLDC motor is studied for position sensor failure situations. Discrete Fourier transform analysis is used for pattern recognition of the line voltages of BLDC motor. Hall Effect sensor failure of BLDC motor is implemented on the verified simulation model. A knowledge based table is developed to identify the faulty sensor by analyzing the simulation results. Hence faulty position sensor is identified through spectral density error of the line voltages; exact knowledge of BLDC motor voltages for various speed and torque loads is not

7 needed. Commutation signal of faulty sensor is generated by microcontroller through correlation between Hall signals. The proposed fault tolerant system is capable to detect, identify and rectify the Hall Effect sensor break down in BLDC motor. Simulation results show correct detection and identification of position sensor fault by the proposed fault tolerant control system. Effectiveness of the remedial strategy is also proven by correct performance of BLDC motor under faulty condition. proposed system has a simple algorithm and can be implemented with a closed loop control scheme of BLDC motor on a single chip microcontroller. Consequently reliability of BLDC motor drive is improved. REFERENCES [1] A. Tashakori, M. Ektesabi, and N. Hosseinzadeh, Characteristics of suitable drive train for electric vehicle, in International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011), Vol. 2, pp , ASME, [2] A. Tashakori and M. Ektesabi, Comparison of different pwm switching modes of bldc motor as drive train of electric vehicles, World Academy of Science, Engineering and Technology, vol. 67, pp , [3] E. Balaban, A. Saxena, P. Bansal, K. Goebel, and S. Curran, Modeling, detection, and disambiguation of sensor faults for aerospace applications, IEEE Sensors Journal, vol. 9, no. 12, pp , [4] N. B. Samoylenko, Q. C. Han, and J. Jatskevich, Dynamic performance of brushless dc motors with unbalanced hall sensors, IEEE Transactions on Energy Conversion, vol. 23, no. 3, pp , [5] Y.-S. Jeong, S.-K. Sul, S. Schulz, and N. Patel, Fault detection and fault-tolerant control of interior permanent-magnet motor drive system for electric vehicle, IEEE Transactions on Industry Applications, vol. 41, no. 1, pp , [6] L. Wang, J. Liu, and X. Wu, Fault analysis on driving motors of lunar rover wheels, (Beijing), Aug International Conference on Electrical Machines and Systems, ICEMS [7] X.-Q. Liu, H.-Y. Zhang, J. Liu, and J. Yang, Fault detection and diagnosis of permanent-magnet dc motor based on parameter estimation and neural network, IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp , [8] A. Tashakori and M. Ektesabi, Stability analysis of sensorless bldc motor drive using digital pwm technique for electric vehicles, in Proceeding of 38th Annual Conference on IEEE Industrial Electronics Society, IECON 2012, pp , October 2012.

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