Demagnetization Fault Detection on BLDC by Adaptive Neuro Fuzzy Inference System

Main Article Content

Kreangsuk kraikitrat
Bunyarit Wangngon

Abstract

This research presents a method for detecting permanent magnet damage anomalies in a brushless direct current motor (BLDC Motor) by applying the adaptive neuro-fuzzy interface system (ANFIS). The input data for ANFIS has 4 lead inputs. Derived from the magtitude and position of the third order harmonics of the stator current and back electromotive force (Back-EMF) from the BLDC Motor. The ANFIS construction is ideal for detecting any malfunctions. It is the combined structure of the fuzzy logic system (FLS) and artificial neural networks (ANN) methods. In the FLS part, the membership function is used as the triangular and choose the principle of function approximation as sugeno fuzzy model and in ANN choose feed-forward network, there is transfer function at hidden layer and output layer is tan-sigmoid transfer function (tansig) and linear transfer function (purelin) respectively and have a learning style back-propagation learning from the test, it was found that the learning error of ANFIS was 3.62E-03 and the accuracy in the detection of anomalies was 98.81%.

Downloads

Download data is not yet available.

Article Details

Section
Research Paper

References

Albrecht, P. F., Appiarius, J. C., McCoy, R. M., Owen, E. L., & Sharma, D. K. (1986). Assessment of the reliability of motors in utility applications-Updated. IEEE Transactions on Energy conversion, (1), 39-46.

Kim, H. K., Kang, D. H., & Hur, J. (2015). Fault detection of irreversible demagnetization based on space harmonics according to equivalent magnetizing distribution. IEEE Transactions on Magnetics, 51(11), 1-4.

Kang, D. H., Kim, H. K., & Hur, J. (2015, September). Irreversible demagnetization diagnosis of IPM-type BLDC motor using BEMF harmonic characteristics based on space harmonics. In 2015 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 6956-6961). IEEE.

Li, Z. X., Yang, G. L., Fan, Y. M., & Li, J. H. (2021). Irreversible demagnetization mechanism of permanent magnets during electromagnetic buffering. Defence Technology, 17(3), 763-774.

Usman, A., & Rajpurohit, B. S. (2020). Modeling and Classification of Stator Inter-Turn Fault and Demagnetization Effects in BLDC Motor Using Rotor Back-EMF and Radial Magnetic Flux Analysis. IEEE Access, 8, 118030-118049.

Usman, A., Sharma, V. K., & Rajpurohit, B. S. Harmonic Analysis of a BLDC Motor Under Demagnetization Fault Conditions. In 2020 IEEE 9th Power India International Conference (PIICON) (pp. 1-5). IEEE.

Usman, A., Joshi, B. M., & Rajpurohit, B. S. (2019, October). Modeling and analysis of demagnetization faults in BLDC motor using hybrid analytical-numerical approach. In IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society (Vol. 1, pp. 1198-1203). IEEE.

Kim, H. K., & Hur, J. (2016). Dynamic characteristic analysis of irreversible demagnetization in SPM-and IPM-type BLDC motors. IEEE Transactions on Industry Applications, 53(2), 982-990.

Kim, B. C., Lee, J. H., & Kang, D. W. (2020). A study on the effect of eddy current loss and demagnetization characteristics of magnet division. IEEE Transactions on Applied Superconductivity, 30(4), 1-5.

Kim, H. K., Hur, J., Kim, B. W., & Kang, G. H. (2010, September). Characteristic analysis of IPM type BLDC motor considering the demagnetization of PM by stator turn fault. In 2010 IEEE Energy Conversion Congress and Exposition (pp. 3063-3070). IEEE.

Usman, A., & Rajpurohit, B. S. (2020, January). Numerical Analysis of Stator Inter-turn Fault and Demagnetization effect on a BLDC Motor using Electromagnetic Signatures. In 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020) (pp. 1-6). IEEE.

Madhav, N., & Sadakale, R. (2020, July). Analysis of demagnetized BLDC Motor using MATLAB Simulink model and AWT analysis. In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.

Kim, D. H., Im, J. H., Zia, U., & Hur, J. (2020, October). Online Detection of Irreversible Demagnetization Fault with Non-excited Phase Voltage in Brushless DC Motor Drive System. In 2020 IEEE Energy Conversion Congress and Exposition (ECCE) (pp. 748-753). IEEE.

Mati, D., & Kuli, F. (2010, September). Artificial neural networks broken rotor bars induction motor fault detection. IEEE. In Symposium on Neural Network Applications in Electrical Engineering (pp. 23-25).

Drira, A., & Derbel, N. (2011, March). Classification of rotor fault in induction machine using Artificial Neural Networks. In Eighth International Multi-Conference on Systems, Signals & Devices (pp. 1-6). IEEE.

Kolla, S. R., & Altman, S. D. (2007). Artificial neural network based fault identification scheme implementation for a three-phase induction motor. ISA transactions, 46(2), 261-266.

Dias, C. G., & Chabu, L. E. (2008, June). A fuzzy logic approach for the detection of broken rotor bars in squirrel cage induction motors. In 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence) (pp. 1987-1991). IEEE.

Zouzou, S. E., Laala, W., Guedidi, S., & Sahraoui, M. (2009, December). A fuzzy logic approach for the diagnosis of rotor faults in squirrel cage induction motors. In 2009 Second International Conference on Computer and Electrical Engineering (Vol. 2, pp. 173-177). IEEE.

Laala, W., Guedini, S., & Zouzou, S. (2011, September). Novel approach for diagnosis and detection of broken bar in induction motor at low slip using fuzzy logic. In 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives (pp. 511-516). IEEE.

Souad, L., Youcef, M., & Samir, M. (2017, October). Use of Neuro-fuzzy technique in diagnosis of rotor faults of cage induction motor. In 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B) (pp. 1-4). IEEE.

Moghadasian, M., Shakouhi, S. M., & Moosavi, S. S. (2017, September). Induction motor fault diagnosis using ANFIS based on vibration signal spectrum analysis. In 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP) (pp. 105-108). IEEE.

Ballal, M. S., Khan, Z. J., Suryawanshi, H. M., & Sonolikar, R. L. (2007). Adaptive neural fuzzy inference system for the detection of inter-turn insulation and bearing wear faults in induction motor. IEEE Transactions on Industrial Electronics, 54(1), 250-258.

Faiz, J., & Mazaheri-Tehrani, E. (2017). A novel demagnetization fault detection of brushless DC motors based on current time-series features. In 2017 IEEE 11th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED) (pp. 160-166). IEEE.

Yilmaz, M. S., & Ayaz, E. (2009, May). Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data. In IEEE EUROCON 2009 (pp. 1140-1145). IEEE.

Hanselman, D. C. (2003). Brushless permanent magnet motor design. The Writers' Collective.

Goktas, T., Zafarani, M., & Akin, B. (2016). Discernment of broken magnet and static eccentricity faults in permanent magnet synchronous motors. IEEE Transactions on Energy Conversion, 31(2), 578-587.