Induction Machine Rotor and Stator Faults Detection by Applying the N-F Network

Induction Machine Rotor and Stator Faults Detection by Applying the N-F Network

Souad Saadi Laribi, Azzedine Bendiabdellah
DOI: 10.4018/978-1-5225-6989-3.ch010
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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.
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Modelisation Of The Machine

A three-phase cage motor is considered; its rotor consists of Nb insulated bars uniformly distributed on the surface of the rotor and short-circuited by two rings. In order to study its performances, a model of the motor is used, where the cage is considered as a mesh circuit as depicted in Figure 1. The number of differential equations obtained is equal to the number of bars plus one in order to take into consideration one of the two rings (Benouzza, 2006; Meo, Gentile & Ometto, 2003; Laribi, 2016; Laribi et al., 2018).

Under classical assumptions, the mathematical model of the machine is given by the equations of the tensions below:



Figure 1.

Mesh circuit of the cage rotor


Stator Current Spectral Content With And Without Faults

Healthy Motor

In the case of the healthy operation of the induction cage motor, there appears on the stator current spectrum a single harmonic at the frequency 50 Hz which is the fundamental one (Asfani et al., 2012; Costa, Kashiwagi & Mathias, 2015; Chen et al., 2007).

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