Yaskawa Servo Motor SGM Series SERVO MOTOR 200W 200V 3000/min SGM-02A3NT12

 

 

 

 

Additionally, a method using the motor internal physical condition based on a socalled pendulous oscillation of the rotor magnetic field space vector orientation has been introduced for motor fault classification [7, 25, 27]. This technique classifies the faults and identifies the fault severity of induction motors with broken rotor bars or inter-turn short circuit. However, this index identification based technique needs to evaluate each
new motor to obtain the correct set of indexes in order to correctly classify the faults.

 

The two new methods for motor fault classification and monitoring that are the subject of this thesis are briefly introduced in the next section.

 

 

This thesis presents two new methods. The first method is an induction motor fault diagnostic technique, which classifies two types of motor faults: broken rotor bars and inter-turn short circuits. Additionally, this method identifies the motor fault severity. This method will be referred to as the diagnostic method throughout the thesis. The second method is an induction motor fault monitoring technique which classifies the operating condition of an induction motor as faulty or healthy. This second method is a robust technique, because it can be trained with datasets generated by Finite Element methods and monitors the faults of real induction motors independently of their power ratings, number of poles, level of load torque, and operating frequency. This robustness is   demonstrated experimentally in Chapter 5. The second method is referred to as the
monitoring method throughout the thesis.