Abstract
Induction motor plays an important role in the industrial, commercial and residential industries, owing to its immense advantages over the opposite types of motors. Such motors have to operate under different operating conditions that cause engine degradation leading to fault occurrences. There are numerous fault detection techniques available. There are numerous fault detection techniques available. The technique used in this paper to prove the effect of static air gap eccentricity on behaving or performing of the three-phase induction motor is the artificial neural network (ANN) as ANN depends on detecting the fault on the amplitude of positive and negative harmonics of frequencies. In this paper, we used two motors to achieve real malfunctions and to get the required data and for three different load tests. In this paper, we adopted MCSA to detect the fault based on the stator current. The ANN training algorithm used in this paper is back propagation and feed forward. The inputs of ANN are the speed and the amplitudes of the positive and the negative harmonics, and the type of fault is the output. To distinguish between healthy and faulty motor, the input data of ANN are well-trained via experiments test. The methodology applied in this paper was MATLAB and present how we can distinguish between healthy and faulty motor.
Highlights
Induction machines are the basis of all modern industries
The static eccentricity in the induction motor leads to an asymmetric air-gap, this asymmetric air-gap caused by the magneto-motive force of the stator by the continuity of the rotor electro-motive force harmonics
For analyzing the harmonic spectrum, we introduce a new signal process method which is called Fast Fourier Transform (FFT) instead of ordinary frequency in stator current and induction motor fault diagnose
Summary
Induction machines are the basis of all modern industries. Even so, as other motors, they will fail due to its heavy-duty work, amid poor working, manufacturing, and composition factors. Static Air-Gap Eccentricity Fault Detection of Induction Motor Using Artificial Neural Network (Ann). The air-gap eccentricity is a very significant problem in induction motors; it is caused by unequal air gap and calls eccentricity fault. There are two kinds of air-gap eccentricity, the dynamic air-gap eccentricity (DE), the static air gap eccentricity (SE), and combination of both types called mixed eccentricity. The geometric axis of the rotor and stator are same as, in case of static eccentricity the rotor rotates about its geometric axis as shown, which is not the geometric axis of the stator, in case of dynamic eccentricity as depicted in Fig. 2c; the rotor is not concentric and rotates around the stator’s geometric axis [7]. Ramana Murthy p= number of pair of poles v= order of stator time harmonics that are present in the power supply of the motor
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