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{"title":"EMERSON Control Techniques, Nidec Motor Corp MGM-340-CONS-0000 2HP 6.5AMP 230VAC","imgUrl":"https:\/\/img.chinax.com\/nimg\/43\/9c\/41533caad0ad02fcad13796baf0f-200x200-1\/emerson_control_techniques_2c_nidec_motor_corp_mgm_340_cons_0000_2hp_6_5amp_230vac_3000rpm_servo_motor_encoder.jpg","attrs":{"Brand Name":"Emerson","Model Number":"MGM-340-CONS-0000","Certification":"CE","Place of Origin":"U.S.A."}}
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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Shenzhen Wisdomlong Technology CO.,LTD Mainly engaged in automation parts, PLCS,DCS,sensors, relays, dirves,encoder, servo drive,soft starter,touch screen,instrument,process control,electric transmission and factory automation aspects of the project design,software programming,technical service and ... Shenzhen Wisdomlong Technology CO.,LTD Mainly engaged in automation parts, PLCS,DCS,sensors, relays, dirves,encoder, servo drive,soft starter,touch screen,instrument,process control,electric transmission and factory automation aspects of the project design,software programming,technical service and ...
