ANFIS-Based Fault Detection in Brushed and Brushless DC Motors: A Hybrid Intelligence Approach
DOI:
https://doi.org/10.59247/jfsc.v3i2.312Keywords:
Adaptive Neuro Fuzzy Inference System (ANFIS), Brushless DC Motor, Fault Detection, Fuzzy Logic, Motor FaultsAbstract
Electric motors are a key component in industrial automation and renewable energy systems. Faults like short-circuit and overload conditions may cause performance deterioration, overheating, or even permanent damage. Conventional fault detection techniques depend on threshold-based methods, which are not efficient in handling nonlinear system behavior. The following research introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) method for fault detection of short-circuit and overload faults in BLDC and DC motors. Through the assessment of input parameters like current, voltage, speed, and temperature, the model efficiently classifies fault conditions with greater accuracy than traditional methods. The outcomes affirm the capability of ANFIS in dealing with nonlinear relationships and enhancing fault detection reliability.
References
R. K. Roy, A. Chatterjee, and D. Chatterjee, “A current signature based stator main winding fault detection technique for single-phase induction machine,” 2nd International Conference on Control, Instrumentation, Energy & Communication (CIEC), pp. 284-288, 2016, https://doi.org/10.1109/CIEC.2016.7513837.
H. Wang, S. Lu, G. Qian, J. Ding, Y. Liu, and Q. Wang, “A two-step strategy for online fault detection of high-resistance connection in BLDC motor,” IEEE Transactions on Power Electronics, vol. 35, no. 3, pp. 3043-3053, 2020, https://doi.org/10.1109/TPEL.2019.2929102.
A. Hajary, R. Kianinezhad, S. G. Seifossadat, S. S. Mortazavi, and A. Saffarian, “Detection and localization of open-phase fault in three-phase induction motor drives using second order rotational park transformation,” IEEE Transactions on Power Electronics, vol. 34, no. 11, pp. 11241-11252, 2019, https://doi.org/10.1109/TPEL.2019.2901598.
J. -K. Park, and J. Hur, “Detection of inter-turn and dynamic eccentricity faults using stator current frequency pattern in IPM-type BLDC motors,” IEEE Transactions on Industrial Electronics, vol. 63, no. 3, pp. 1771-1780, 2016, https://doi.org/10.1109/TIE.2015.2499162.
S. Lu, Y. Qin, J. Hang, B. Zhang, and Q. Wang, “Adaptively estimating rotation speed from DC motor current ripple for order tracking and fault diagnosis,” IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 3, pp. 741-753, 2019, https://doi.org/10.1109/TIM.2018.2852978.
H. Wang, J. Wang, X. Wang, S. Lu, C. Hu, and W. Cao, “Detection and evaluation of the interturn short circuit fault in a BLDC-based hub motor,” IEEE Transactions on Industrial Electronics, vol. 70, no. 3, pp. 3055-3068, 2023, https://doi.org/10.1109/TIE.2022.3167167.
J. -K. Park, C. -L. Jeong, S. -T. Lee, and J. Hur, “Early detection technique for stator winding inter-turn fault in BLDC motor using input impedance,” IEEE Transactions on Industry Applications, vol. 51, no. 1, pp. 240-247, 2015, https://doi.org/10.1109/TIA.2014.2330067.
W. Cao, et al., “Analysis of inter-turn short-circuit faults in brushless DC motors based on magnetic leakage flux and back propagation neural network,” IEEE Transactions on Energy Conversion, vol. 38, no. 4, pp. 2273-2281, 2023, https://doi.org/10.1109/TEC.2023.3285899.
J. Fang, W. Li, H. Li, and X. Xu, “Online inverter fault diagnosis of buck-converter BLDC motor combinations,” IEEE Transactions on Power Electronics, vol. 30, no. 5, pp. 2674-2688, 2015, https://doi.org/10.1109/TPEL.2014.2330420.
T. Nag, S.B. Santra, A. Chatterjee, D. Chatterjee, and A.K. Ganguli, “Fuzzy logic-based loss minimisation scheme for brushless DC motor drive system,” IET Power Electronics, vol. 9, no. 8, pp. 1581-1589, 2016, https://doi.org/10.1049/iet-pel.2015.0714.
B. Ganguly and A. Chatterjee, “MQTT protocol based extensive smart motor control for electric vehicular application,” 2020 IEEE 7th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), pp. 1-5, 2020, https://doi.org/10.1109/UPCON50219.2020.9376452.
B. Ganguly, A. Chatterjee, A. Chatterjee, and S. Paul, “Diagnosis of stator winding fault of single-phase induction motor employing wavelet induced residual-convolutional neural network,” 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), pp. 1-5, 2020, https://doi.org/10.1109/PEDES49360.2020.9379665.
B. Ganguly, R. K. Ray, A. Chatterjee and S. Paul, “A deep learning aided intelligent framework for condition monitoring of electrical machinery,” 2023 IEEE Devices for Integrated Circuit (DevIC), pp. 82-86, 2023, https://doi.org/10.1109/DevIC57758.2023.10134832.
A. Chatterjee, “Wind-PV based generation with smart control suitable for grid-isolated critical loads in onshore India,” Journal of The Institution of Engineers (India): Series B, 2022, https://doi.org/10.1007/s40031-022-00827-2.
A. Chatterjee, and S. Ghosh, “PV based isolated irrigation system with its smart IoT control in remote Indian area,” 2020 International Conference on Computer, Electrical & Communication Engineering (ICCECE), pp. 1-5, 2020, https://doi.org/10.1109/ICCECE48148.2020.9223110.
S. Ghosh, A. Chatterjee, and D. Chatterjee, “A smart IoT based non-intrusive appliances identification technique in a residential system,” 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), pp. 1-6, 2020, https://doi.org/10.1109/PESGRE45664.2020.9070275.
S. Ghosh, A. Chatterjee, and D. Chatterjee, “An improved load feature extraction technique for smart homes using fuzzy-based NILM,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-9, 2021, https://doi.org/10.1109/10.1109/TIM.2021.3095093.
A. Chatterjee, “Analysis of a self-excited induction generator with fuzzy PI controller for supporting domestic loads in a microgrid,” Journal of Fuzzy Systems and Control, vol. 1, no. 2, pp. 61-65, 2023, https://doi.org/10.59247/jfsc.v1i2.42.
A. Chatterjee, S. Ghosh, and A. Mitra, “Hybrid generation scheme for delivering irrigation loads and other critical loads with smart IoT based control,” IEEE Transactions on Industry Applications, vol. 60, no. 1, pp. 828-837, 2024, https://doi.org/10.1109/TIA.2023.3322114.
A. Chatterjee, “Wind power generation for isolated loads with IoT-based smart load controller,” Journal of Fuzzy Systems and Control, vol. 2, no. 2, pp. 92-96, 2024, https://doi.org/10.59247/jfsc.v2i2.210.
A. Chatterjee, and B. Banerjee, “Grid-secluded induction generator with ANN and interval type-2 fuzzy based controller for wind power generation with smart load control,” Qeios, 2023, https://doi.org/10.32388/D4GAVP.2.
A. Chatterjee, “Wind power forecasting using type-2 fuzzy control and its optimization based on artificial neural network for small scale wind power,” Journal of Fuzzy Systems and Control, vol. 2, no. 3, pp. 170-175, 2024, https://doi.org/10.59247/jfsc.v2i3.259.
A. Chatterjee, K. Roy, and D. Chatterjee, “A gravitational search algorithm (GSA) based photo-voltaic (PV) excitation control strategy for single phase operation of three phase wind-turbine coupled induction generator,” Energy, vol. 74, pp. 707-718, 2014, https://doi.org/10.1016/j.energy.2014.07.037.
S. B. Santra, A. Chatterjee, D. Chatterjee, S. Padmanaban, and K. Bhattacharya, “High efficiency operation of brushless DC motor drive using optimized harmonic minimization based switching technique,” IEEE Transactions on Industry Applications, vol. 58, no. 2, pp. 2122-2133, 2022, https://doi.org/10.1109/TIA.2022.3146212.
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