Industrial Servo Motor Yaskawa Electric SERVO MOTOR 200V 3000/min
SGM-02A312 New in box
SPECIFITIONS
Current: 0.89A
Volatge: 200V
Power :100W
Rated Torque: 0.318-m
Max speed: 3000rpm
Encoder: 17bit Absolute encoder
Load Inertia JL kg¡m2¢ 10−4: 0.026
Shaft: straight without key
Thus, high costs associated with equipment to emulate the faults or
destructive tests to generate datasets to train this method are not
involved. The second advantage is related to scalability of the
monitoring process. The signatures for the training and monitoring
stages are normalized in amplitude. However, the signatures of the
monitoring stage are not only normalized in amplitude, but also in
frequency. This normalization in frequency of the signatures of the
monitoring stage is a function of the signatures of the training
stage. Thus, the signatures from the training and monitoring stages
for the same motor operating condition have similar amplitude and
frequency. These signatures with similar amplitude and frequency
for the same motor operating condition are essential in the
monitoring stage to yield high level of motor fault monitoring
accuracy. Accordingly, the training and monitoring stages yield
signatures that are independent of motor rated power, number of
poles, level of load torque, and operating frequency of the real
motor that is being monitored.
Thus, this method constitutes a powerful tool for induction motor
fault monitoring. This is demonstrated and verified by the
experimental results given in Chapter 5 of this thesis.
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Thus, high costs associated with equipment to emulate the faults or
destructive tests to generate datasets to train this method are not
involved. The second advantage is related to scalability of the
monitoring process. The signatures for the training and monitoring
stages are normalized in amplitude. However, the signatures of the
monitoring stage are not only normalized in amplitude, but also in
frequency. This normalization in frequency of the signatures of the
monitoring stage is a function of the signatures of the training
stage. Thus, the signatures from the training and monitoring stages
for the same motor operating condition have similar amplitude and
frequency. These signatures with similar amplitude and frequency
for the same motor operating condition are essential in the
monitoring stage to yield high level of motor fault monitoring
accuracy. Accordingly, the training and monitoring stages yield
signatures that are independent of motor rated power, number of
poles, level of load torque, and operating frequency of the real
motor that is being monitored.
Thus, this method constitutes a powerful tool for induction motor
fault monitoring. This is demonstrated and verified by the
experimental results given in Chapter 5 of this thesis.
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Contact person: Anna
E-mail: wisdomlongkeji@163.com
Cellphone: +0086-13534205279