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This document discusses condition monitoring of induction motors using motor current signature analysis based on artificial neural networks. It describes various faults that can occur in induction motors like bearing faults and broken rotor bars. Motor current signature analysis is used to detect faults by analyzing the harmonic components in the motor's current spectrum. An artificial neural network can be trained to identify fault patterns in current signatures to predict and locate faults. The neural network is used to detect and locate faults on the stator windings of an induction motor based on its current signature analysis.
Prem ppt on mcsa
- 1. CONDITION MONITORING OF INDUCTION MOTOR USING MOTOR CURRENT SIGNATURE ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORK
- 2. Content a) What is Condition Monitoring?? b) Faults in Induction Motor c) Fault Detection Techniques d) Motor Current Signature Analysis e) Process of detection of fault f) Artificial Neural Network
- 3. What is Condition Monitoring?? Condition monitoring is a technique or process of observing the operating characteristics of machine in such a way that any change in the trend of target parameters could be used to predict the need for maintenance before any serious deterioration occurs. Condition monitoring implies the continuous evaluation of the health of equipments.
- 4. Condition monitoring plays a vital role for fault detection in induction motor. The induction motors are most widely used motors in industrial, commercial and residential sectors because of enormous merits of these over other types of available electrical motors. These motors work under various operating stresses, which deteriorate their motor conditions giving rise to faults.
- 5. FAULTS IN INDUCTION MOTOR Bearing Fault 40% Stator Fault 30% Broken Rotor Bar 10% Other Fault 20%
- 6. Cause of Fault in Induction Motor Mechanical stress Frequent starts of the motor at rated voltage Misalignment of bearings Due to failure of insulation of the stator winding. Over Voltage, Under Voltage, Overload
- 7. NECESSITY OF FAULT DIAGNOSTIC To increase reliability to decrease the possibility of production loss due to machine breakdown. To extend the motor life time.
- 9. MOTOR CURRENT SIGNATURE ANLYSIS MCSA tests are performed online without interrupting production with motor running under the load at normal operating conditions. MCSA is monitoring stator current of the motor. Motor stator windings are used as transducer in MCSA, picking the signals (induced currents) from the rotor (but also revealing information about the state of the stator).
- 10. Process of detection of fault Whenever a fault occurs in a three-phase induction motor, it is mostly due to broken rotor bars, air-gap eccentricity and stator windings short circuits. As a result of these faults, various magnetic flux components are produced in the magnetic circuit of the induction motor. This gives rise to harmonic components in the line current of the stator, which is detectable by current transducers and spectrum analysers.
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- 12. Block diagram of MCSA
- 13. Process of detection of fault When a fault is present, the frequency spectrum of the line current becomes different from healthy motor. By comparing the faulty spectrum with the healthy spectrum the detection of various faults can be identified.
- 14. Current Spectrum of Healthy Motor Current Spectrum of Faulty Motor
- 15. ARTIFICIAL NEURAL NETWORK The ANN represents information-processing systems formed by interconnecting simple-processing units called neurons.
- 16. This system consists of detection and the location of a fault on the stator windings of a three phase induction motor by using the Neural Networks . The number of input nodes and the number of output nodes are determined by the number of patterns to be identified.
- 17. Prediction With Neural Network It involves two steps:-Training and Learning Different faults have different characteristics in their fault patterns, which gives rise to unique current signature patterns.
- 18. Block diagram of fault location procedure
- 19. Future Objective Determination of stator fault , broken rotor bar of induction motor using motor current signature analysis and ANN.
- 20. Reference N Mehala and R. Dahiya, Motor Current Signature Analysis and its Applications in Induction Motor Fault Diagnosis, International Journal of Systems Applications, Engineering & Development Volume 2 W T Thomson: “On-Line MCSA to Diagnose Shorted Turns in Low Voltage Stator Windings of 3-Phase Induction Motors Prior to Failure”, IEEE, PES&IAS IEMDC, MIT,Boston. S. Rajakarunakaran, et al., “Artificial Neural Network for Fault Detection in Rotary System “.