Industrial Servo Motor New YASKAWA SERVO MOTOR 0.318-m 3000/min SGM-02A3G26

 

 

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.