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

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

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

Product Details:

Place of Origin: Japan
Brand Name: Yasakawa
Model Number: SGM-02A5FJ12

Payment & Shipping Terms:

Minimum Order Quantity: 1
Price: negotiable
Packaging Details: New in original box
Delivery Time: 2-3 work days
Payment Terms: T/T, Western Union
Supply Ability: 100
Detailed Product Description
Brand: Yasakawa Model: SGM-02A5FJ12
Palce Of Origin: Japan Type: Servo Motor
Supply Voltage: 200V Current: 2.0A
Ins: B R/min: 3000
High Light:

ac servo motor

,

electric servo motor

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
 

 

 

 

 

 
 
 
 
 
 
 
 
 
 
 

 

 

Contact Details
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Contact Person: Anna

Tel: 86-13534205279

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