ANN approach for the incipient faults detection in single phase induction motor

8/7/2019 ANN approach for the incipient faults detection in single phase induction motor

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ANN APPROACH FOR THE INCIPIENT FAULTS DETECTION IN SINGLE PHASE

INDUCTION MOTOR.

ABSTRACT:

Emerging technology of artificial neural networks has successfully been applied in a variety

of areas such as , fault detection, control, signal processing, and many other applications. This

paper presents an artificial neural network(ANN)methodology for the detection of incipient

faults in induction motor. The ANN based detector avoids the problems associated with

traditional incipient faults detection schemes by employing readily available information,

such as motor intake current, rotor speed ,and stator winding temperature. The two types of

ANN based incipient fault detectors are developed , ie , conventional and higher order

ANN fault detector. The results of the evaluation indicates that the higher order ANN

based incipient faults detector provides more accurate results.


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INTRODUCTION:

The use of small and medium size induction motors in industry and in home appliances is

extensive and continuous. These machines are exposed to wide variety of environments and

conditions and there by develop incipient faults. The turn-to turn insulation failure and bearing

wear are the most common types of faults to be investigated. The various causes for weaking of

insulation and bearing wear of the electrical machines are (i) aging,(ii)switching

surge,(iii)heat,(iv) contamination by oil (v)incipient faults, (vi)damage during installation, etc

.A turn-to turn short circuit of winding causes the decrease in equivalent turns of the winding of

the machine. It results in rise in input line current of winding . This causes the increased heating

in the core due to additional I2R losses and drop in speed. The increased heating will cause a

corresponding temperature rise of stator winding, there by decreasing the life expectancy of the

winding insulation. Stator winding insulation failure will cause additional shorted turns, further

rise in temperature, and this leads to increase in the rate of deterioration of winding insulation. Ifleft unchecked, this process will cause eventual destruction of the relative winding and render the

machine inoperative.

The motor bearings of induction motor are subject to

deterioration caused by inadequate or contaminated lubrication, misapplication, or misalignment

. The frictional losses within the motor bearings are used as a criterion to determine their

condition. The bearings losses that are negligible or within 5% of the overall losses of the motorand can be consider as good. However, bad bearings can cause permanent damage to them

selves as well as to the rotor or the stator winding and must be detected quickly.


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In earlier works, two parameters have been considered for the fault detection, which are motor

intake current(I) and rotor speed(w). However , in this paper , stator winding temperature(Tw) is

included as an additional parameter for better fault detection. The proposed artificial neural

network (ANN) based detector avoids the problem associated with traditional schemes, such as

continuous on-line monitoring by experts, parameter estimation approach, etc. In this technique

more readily available informations , such as , motor intake current , rotor speed and stator

winding temperature have been used for decision making.

Neural network toolbox in MATLAB environment is used to optimize thenetwork. The data required to train the neural network is generated in the laboratory on specially

designed single phase, squirrel cage 1- hp induction motor. The winding turns of one pole in a

four pole machine is brought out for the shortening in steps. This gives the effect of inter-turn

short circuit of motor winding, and it causes the imbalance in air gap flux.Thus , this produced

the mechanical stresses over the shaft of motor. This corresponds to the additional eccentric

loading caused by a worn bearing in the ac induction motor. This allows nondestructive

emulation of worn or failed induction motor bearings.

In this paper, ANN is trained with three inputs , such as (i) motor intake

current, (ii) speed , and stator winding temperature as a simple conventional network. Further

the original input space is expanded to six dimentions ,which is a higher order network. In both

these causes the ANN is tested satisfactorily . Results of evaluation indicate that the higher order

neural network based incipient fault detector predicts the winding and bearing condition of the

motor to a more satisfactory level of accuracy.


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ANN approach for the incipient faults detection in single phase induction motor

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