An Efficient Machine Learning Method for Inter-Turn Fault Detection and Torque Control in Bldc Motor

Nowadays, the brushless dc machines are highly involved in many applications and having high demand and rugged performance. During some tough situation which occurs in aircraft the motor must be well-grounded. In this situation an innovative model of detecting the fault method is evolved for limiting the speed of the BLDC motor. Below the deliberation of the complication of dynamic featuring the control system of BLDC motor with an analytic problem solving, LRGF is a technique of Artificial Neural Network with pole work method which is moved to design the system. During the fault analysis method, the fault signal was segregated with LRGFNN online. In between the adaptive lifting technique and adaptive threshold technique which are dispensed for identifying the faults from the separated fault signals in the presence of the errors such as mechanical and electrical errors. The performance of the fault identification system is denoted in motor which having the electrical simulation fault and mechanical fault. The vibration fault and over load fault are also identified.