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Industrial Servo Motor New Yaskawa SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12

    Buy cheap Industrial Servo Motor New Yaskawa  SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12 from wholesalers
     
    Buy cheap Industrial Servo Motor New Yaskawa  SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12 from wholesalers
    • Buy cheap Industrial Servo Motor New Yaskawa  SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12 from wholesalers
    • Buy cheap Industrial Servo Motor New Yaskawa  SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12 from wholesalers
    • Buy cheap Industrial Servo Motor New Yaskawa  SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12 from wholesalers
    • Buy cheap Industrial Servo Motor New Yaskawa  SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12 from wholesalers

    Industrial Servo Motor New Yaskawa SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12

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    Brand Name : Yasakawa
    Model Number : SGM-02A5FJ12
    Price : negotiable
    Payment Terms : T/T, Western Union
    Supply Ability : 100
    Delivery Time : 2-3 work days
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    Industrial Servo Motor New Yaskawa SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12

    Industrial Servo Motor New Yaskawa SERVO MOTOR 200V SGM-02A5FJ12 SGM-02A5FJ12


    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


    The second method presented in this thesis is an induction motor fault monitoring technique based on the air gap torque profile analysis, associated with machine learning techniques to classify the operating condition of an induction motor as healthy or faulty. These machine learning techniques are based on GMMs and RPSs. The important novel nature of this approach is two-fold. First, the necessary healthy and faulty motor signatures to train this method are obtained from finite element simulations, not from experimental data. Second, the signatures can be applied to different classes of induction motors through a novel normalization process. A faulty condition represents any number of broken rotor bars. The signatures used in the training stage are based on the air gap torque profile of an induction motor simulated by a time-stepping Finite Element method.

    In the monitoring stage a new signature is built for the developed torque. This torque is calculated online from a new set of three-phase stator voltages and currents acquired from an actual induction motor being monitored. A comparison of the signatures obtained at the training and monitoring stages classifies the motor operating condition.

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    This monitoring method has two main advantages. The first advantage is the robustness of the monitoring processes, in which the training stage uses data generated by finite element simulations, in order to monitor the operating conditions of real induction motors during the actual operating (monitoring) stage. This is accomplished with high levels of motor fault monitoring accuracy, as shown by the experimental results given in Chapter 5. It should be pointed out that the training process is performed offline, while the monitoring process is performed online. These training and monitoring processes based on data from different sources (simulations and real motors operating data, respectively) show the robustness of the method.

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    Contact person: Anna
    E-mail: wisdomlongkeji@163.com
    Cellphone: +0086-13534205279



    Product Tags:

    ac servo motor

      

    electric servo motor

      
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