Presented By
AKSHAYA CHATURVEDI
ANIL KUMAR
JEETENDRA KUMAR SRIVASTAVA

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ABSTRACT
3 phase Induction motor play a pivotal role in industry and there is a strong demand for their reliable and safe operation. They are generally reliable but eventually do wear out. Faults and failures of induction motor can lead to excessive downtimes and generate large losses in terms of maintenance and lost revenues. Induction motors are critical components in many industrial processes. Therefore online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor having different types of faults like winding faults. To model shorted stator turns, the model assumes that the one of the phase has two windings in series, representing the unaffected portion and the shorted portion. This work presents a reliable method for the detection of stator winding faults based on monitoring the stator current amplitudes. In this method, fuzzy logic is used to make decisions about the motor condition. The fuzzy system is able to identify the motor stator condition with high accuracy.
THEORY
Because of costly machinery repair, extended process down time, health and safety problems, a trend in modern industry is to focus attention on fault detection and predictive maintenance strategies for industrial plant. It is known that approximately 36% of induction motor failures are caused by failure of the stator winding, and it is believed that these faults begin as undetected turn-to-turn faults in a coil, which progress to catastrophic phase-to-phase or phase-to-ground short circuit faults. To achieve prior warning of failure, shorted turns within the stator winding coil must be detected or predicted in effect to avoid failure.
Modeling of induction motors with shorted turns is the first step in the design of turn fault detection systems. Simulation of transient and steady state behaviour of motors with these models enable correct evaluation of the measured data by diagnostic techniques. The major difficulty in the diagnosis is the lack of a well processing of input data.
WORKING PRINCIPLE:
This project applies fuzzy logic, to the diagnosis of induction motor stator conditions and opens circuit conditions in phases, based on the amplitude of stator currents. In fact, fuzzy logic is reminiscent of human thinking process and natural language enabling decisions to be made based on vague information.
When conducting the fault diagnosis, there are several situations in which an object is not obviously “good” or “bad”, but may fall into some interior range. This method has been chosen because fuzzy logic has proven ability in mimicking human decisions, and the stator voltage and phase condition monitoring problem has typically been solved. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe the stator current amplitudes.
A knowledge base, comprising rule and databases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference.
The aim of this project is to present a useful and straight forward method to simulate inter -turn short circuits and open circuit in any of stator phases for diagnostic purposes. The inter-turn fault can be simulated by disconnecting one or more turns making up a stator phase winding. Models are simulated using the MATLABR SIMULINK and simulation results are presented.
Fuzzy systems rely on a set of rules. These rules, while superficially similar, allow the input to be fuzzy, i.e. more like the natural way that humans express knowledge. Thus, a power engineer might refer to an electrical machine as“somewhat secure” or a “little overloaded”. This linguistic input can be expressed directly by a fuzzy system. Therefore, the natural format greatly eases the interface between the engineer knowledge and the domain expert.
Conclusion
Fuzzy logic based measurement and health evaluation system has been developed and implemented. This application allows fast failure state estimation. The more detailed investigation to point out the difficult conditions of the machine under different stator fault conditions of induction motor can be performed. This is a highly versatile technology for condition monitoring and fault analysis of motors. It solves the shutdown Problems and ensures safe working environment in continuous process industry.