ANFIS-Based Fault Detection in Brushed and Brushless DC Motors: A Hybrid Intelligence Approach

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.