Industrial Servo Motor New YASKAWA SERVO MOTOR 0.318-m 3000/min
SGM-02A3G26
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
OTHER SUPERIOR PRODUCTS
Yasakawa Motor, Driver SG- Mitsubishi Motor HC-,HA-
Westinghouse Modules 1C-,5X- Emerson VE-,KJ-
Honeywell TC-,TK- Fanuc motor A0-
Rosemount transmitter 3051- Yokogawa transmitter EJA-
Contact person: Anna
E-mail: wisdomlongkeji@163.com
Cellphone: +0086-13534205279
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Other techniques include vibration analysis, acoustic noise
measurement, torque profile analysis, temperature analysis, and
magnetic field analysis [28, 30]. These techniques require
sophisticated and expensive sensors, additional electrical and
mechanical installations, and frequent maintenance. Moreover, the
use of a physical sensor in a motor fault identification system
results in lower system reliability compared
to other fault identification systems that do not require extra
instrumentation. This is due to the susceptibility of the sensor to
fail added to the inherent susceptibility of the induction motor to
fail.
Recently, new techniques based on artificial intelligence (AI)
approaches have been introduced, using concepts such as fuzzy logic
[32], genetic algorithms [28], and Bayesian classifiers [18, 34].
The AI-based techniques can not only classify the faults, but also
identify the fault severity. These methods build offline signatures
for each motor operating condition and an online signature for the
status of a motor being monitored. A
classifier compares the previously learned signatures with
the signature generated online in order to classify the motor
operating condition and identify the fault severity.
However, most of these AI-based techniques require large datasets.
These dataset are used to learn a signature for each motor
operating condition that is being considered for classification.
Thus, a large amount of data is needed to train such algorithms in
order to cover the most common motor operating conditions, and
obtain good motor fault classification accuracy. Moreover, AI-based
techniques for motor fault classification may not be sufficiently
robust to classify faults from different motors from those used in
the training process. Additionally, these datasets are
usually not available, involve destructive testing, and
considerable time to generate.