IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 19, NO. 4, DECEMBER 2004
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ANFIS-Based Diagnosis and Location of Stator Interturn Faults in PM Brushless DC Motors M. A. Awadallah, Student Member, IEEE, and M. M. Morcos, Senior Member, IEEE
Abstract—An automatic scheme for fault diagnosis and location of stator-winding interturns in permanent-magnet brushless dc motors is presented. System performances under healthy and faulty operation are obtained via a discrete-time model. Waveform of the electromagnetic torque is monitored and processed using discrete Fourier transform and short-time Fourier transform to derive proper diagnostic indices. Two adaptive neuro–fuzzy inference systems (ANFIS) are developed to automate the fault diagnosis process. Test results show an acceptable performance for ANFIS in detecting the fault. Index Terms—Adaptive fuzzy systems, fault diagnosis, fault location, permanent-magnet (PM) brushless dc motors.
I. INTRODUCTION
T
HE interturn fault of motor windings usually starts as an undetected insulation failure between two adjacent turns, then develops to a short circuit isolating a number of turns. In some cases, the fault occurs as a result of an electric arc connecting two points of the winding. Detecting the changes in the axial leakage flux could predict and locate stator short circuits of three-phase induction motors [1]. Immediately after disconnecting the supply, spectral components of the machine line voltages could be also used to diagnose the fault [2]. The present work introduces a comprehensive automatic scheme based on adaptive neuro–fuzzy inference systems (ANFIS) for identification of stator short circuits in brushless dc motors. The current-source inverter-fed 12 V, 1000-r/min, six-pole motor considered in this work is shown in Fig. 1. Motor performance is obtained under constant speed operation through the numerical discrete-time model presented in [3]. The electromagnetic (EM) torque waveform is monitored and processed using Fourier transform to derive suitable diagnostic indices. Two independent ANFIS are developed to automate the process of diagnosing and locating the short-circuit fault. II. FEATURE EXTRACTION
Normal operation of the drive included perfect and imperfect commutations as well as noisy operation in order to prevent ANFIS from issuing a false-fault alarm under such conditions. Shorted section of the winding was represented by an electromotive-force (EMF) source in series with a resistance and a self inductance; mutual inductances to healthy windings were also considered. Parameters of the shorted and healthy sections of the faulty phase were evaluated by modifying the normal phase Manuscript received May 12, 2003. Paper no. PESL-00064-2003. The authors are with Kansas State University, Manhattan, KS 66502 USA (e-mail:
[email protected]). Digital Object Identifier 10.1109/TEC.2004.837273
Fig. 1.
Schematic diagram of the integrated drive systems.
parameters based on the number of faulty turns. Resistance, mutual inductance, and EMF amplitudes are all proportional to the number of turns; self inductance is proportional to the square of the number of turns. On the other hand, the fault current was considered an additional state variable of the system under faulty operation. Current waveforms under fault were almost identical to those of normal operation. The current source inverter configuration of the drive, along with the current chopping action, make the control circuitry (not the machine) form the current waveforms. The fault produced consistent curve dips in the EM torque waveform. The dips were due to two factors: reduction in the EMF amplitude across the faulty phase, and contribution of the fault current. Magnitude of the torque dips characterized the number of faulty turns, and the switching states during which dips occurred identified the faulty phase. Torque waveforms at 1000 r/min and 20-A current command, under normal operation and three shorted turns of phase A, are shown in Figs. 2 and 3, respectively. Torque waveform was processed using the discrete Fourier transform (DFT). The frequency at which the third spectral component appears, as well as the magnitudes of the third and ninth components, determined the number of shorted turns. shorttime Fourier transform (STFT) was used to obtain the spectral contents of the torque signal during the first three states. The signed magnitudes of the first component of each sequence—together—could identify the faulty phase. III. ANFIS DEVELOPMENT The comprehensive diagnosis process of the fault was automated through two independent ANFIS, one for shorted turns determination (ANFIS ) and another for faulty phase identification (ANFIS ). Both systems were first-order Sugeno-type with three inputs and 12 Gaussian membership functions per input. The inputs to ANFIS represented the diagnostic indices to determine the number of shorted turns, while its output was oriented to be zero during normal operation, or an integer implying the number of shorted turns under fault conditions.
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IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 19, NO. 4, DECEMBER 2004
TABLE I SUMMARIZED TESTING RESULTS OF THE DIAGNOSTIC SYSTEM
ANFIS was designed to operate sequentially upon a fault signal issued by ANFIS . Inputs of ANFIS were the three phase-identification indices; its output was an integer indicating the faulty phase. Both systems were trained based on a data set containing indices derived at various healthy and faulty operations, and different loading conditions. Testing results, summarized in Table I, show acceptable performance of the proposed scheme in fault diagnosis and location. IV. CONCLUSION
Fig. 2. Developed torque at 1000 r/min and 20-A current command under normal operation.
An automatic diagnostic scheme for interturn faults of the PM brushless dc motors is presented. System performance under normal operation was obtained through a discrete-time numerical model. Faulty performance was obtained by modifying the model to accommodate the short circuit and account for the fault current. Healthy and faulty characteristics were studied, and the EM torque waveform was selected to be monitored in order to diagnose the fault. Processing a whole electrical cycle of the torque waveform using DFT, the frequency of the third component and the magnitudes of the third and ninth components could characterize the number of shorted turns. Signed magnitudes of the first spectral components during the first three switching states, derived by STFT, identified the faulty phase. Two independent ANFIS were developed to automate the full diagnosing process. Both systems performed acceptably well as indicated by test results. REFERENCES
Fig. 3. Developed torque at 1000 r/min and 20-A current command with three shorted turns on phase A.
[1] J. Penman, H. G. Sedding, B. A. Lloyd, and W. T. Fink, “Detection and location of interturn short circuits in the stator windings of operating motors,” IEEE Trans. Energy Conversion, vol. 9, pp. 652–658, Dec. 1994. [2] S. Nandi and H. A. Toliyat, “Novel frequency domain based technique to detect incipient stator inter-turn faults in induction machines,” in Proc. IEEE Ind. Applicat. Soc. Annu. Meeting, 2000, pp. 367–374. [3] N. A. Demerdash and T. W. Nehl, “Dynamic modeling of brushless DC motors for aerospace actuation,” IEEE Trans. Aerosp. Electron. Syst., vol. AES-16, pp. 811–821, Nov. 1980.
