Title :
Monitoring of induction machines load torque disturbances: an alternative NN-based method
Author :
Filippetti, F. ; Grellet, G. ; Salles, G. ; Francesch, G. ; Tassoni, C.
Author_Institution :
Bologna Univ., Italy
Abstract :
This paper addresses the problem of the real time rebuilding of the load torque disturbances in asynchronous machines. Since the load pattern modifies the motor´s supply current, it should be possible to use the current pattern to rebuild torque pattern, utilizing the machine itself as a torque sensor. In the paper the problem is studied utilizing both relationships developed under simplifying assumptions and a more complex model of the machine. The results obtained are compared with the experimental ones. Reference is made to low frequency torque disturbances, that cause a quasistationary machine behavior. It is shown that a neural network approach can be an alternative and efficient method for the torque pattern recognition.
Keywords :
asynchronous machines; computerised monitoring; electric machine analysis computing; neural nets; pattern recognition; torque; torque measurement; NN-based method; asynchronous machines; current pattern; induction machine load torque disturbances; induction machine monitoring; load model; load pattern; low frequency torque disturbances; machine anomalies; motor supply current modification; neural network; periodic model; quasistationary machine behavior; torque monitoring; torque pattern rebuilding; torque pattern recognition; torque sensor; Communication system control; Condition monitoring; Control systems; Electric variables control; Induction machines; Microprocessors; Shafts; Steady-state; Testing; Torque control;
Conference_Titel :
Industry Applications Conference, 1998. Thirty-Third IAS Annual Meeting. The 1998 IEEE
Conference_Location :
St. Louis, MO, USA
Print_ISBN :
0-7803-4943-1
DOI :
10.1109/IAS.1998.732267