Gearbox Monitoring Using Induction Machine Stator Current Analysis

Author(s):  
Shahin Hedayati Kia ◽  
Humberto Henao ◽  
Gerard-Andre Capolino
Author(s):  
El Houssin El Bouchikhi ◽  
Vincent Choqueuse ◽  
Mohamed Benbouzid ◽  
Jose A. Antonino-Daviu

Author(s):  
Souad Saadi Laribi ◽  
Azzedine Bendiabdellah

This chapter focuses on the monitoring and diagnosis of induction machine faults, particularly the broken rotor bars. The design of a system for monitoring, detecting, and locating incipient faults for different loads of the machine is achieved by the use of advanced intelligent techniques based on ANFIS-based neuro-fuzzy network. The knowledge base is based on indicators derived from the stator current spectral analysis of the machine which in addition has to detect and assess the number of faulty bars.


Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3713
Author(s):  
Daniel R. Ramirez ◽  
Cristina Martin ◽  
Agnieszka Kowal G. ◽  
Manuel R. Arahal

In this paper, a fuzzy-logic based operator is used instead of a traditional cost function for the predictive stator current control of a five-phase induction machine (IM). The min-max operator is explored for the first time as an alternative to the traditional loss function. With this proposal, the selection of voltage vectors does not need weighting factors that are normally used within the loss function and require a cumbersome procedure to tune. In order to cope with conflicting criteria, the proposal uses a decision function that compares predicted errors in the torque producing subspace and in the x-y subspace. Simulations and experimental results are provided, showing how the proposal compares with the traditional method of fixed tuning for predictive stator current control.


Sign in / Sign up

Export Citation Format

Share Document