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Industrial Servo Motor Yaskawa Electric SERVO MOTOR 200V 3000/min SGM-02A312 New in box

    Buy cheap Industrial Servo Motor  Yaskawa Electric  SERVO MOTOR 200V 3000/min SGM-02A312 New in box from wholesalers
     
    Buy cheap Industrial Servo Motor  Yaskawa Electric  SERVO MOTOR 200V 3000/min SGM-02A312 New in box from wholesalers
    • Buy cheap Industrial Servo Motor  Yaskawa Electric  SERVO MOTOR 200V 3000/min SGM-02A312 New in box from wholesalers
    • Buy cheap Industrial Servo Motor  Yaskawa Electric  SERVO MOTOR 200V 3000/min SGM-02A312 New in box from wholesalers

    Industrial Servo Motor Yaskawa Electric SERVO MOTOR 200V 3000/min SGM-02A312 New in box

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    Brand Name : Yasakawa
    Model Number : SGM-02A312
    Price : negotiable
    Payment Terms : T/T, Western Union
    Supply Ability : 100
    Delivery Time : 2-3 work days
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    Industrial Servo Motor Yaskawa Electric SERVO MOTOR 200V 3000/min SGM-02A312 New in box

    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



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