Fault detection and diagnosis of multi phase Induction motor drives with different optimization Approaches

Abstract

Induction motors are the practical applications in all kind of newlineindustries. Induction motors were appeared to several miscellaneous newlinefaults occurred during their operation. In this situation, numerous newlinemethods and several approaches have been introduced and determined newlinesuccessfully, in order to detect and diagnose the faults of induction newlinemotors. Fault diagnosis and detection to produce diminish the break newlinetime and improves its reliability and availability of the systems. The aim newlineof this thesis is to present innovative fault detection and classification in newlinethe induction motor, when compared with the existing techniques. Now newlinea day s numerous number of condition monitoring procedures are newlineavailable such the parameters like Electromagnetic field monitoring, newlinesearch coils, coils wounded through motor shafts, temperature newlinemeasurements, noise and vibration monitoring Chemical analysis. The newlinecondition monitoring is to obtain some advanced prediction with respect newlineto the development of any fault in the induction motor, so that the newlineoutage can be scheduled in safe and favorable method leading to lower newlinethe break time and lower capitalized losses. Chapter 3 deal with the newlineissues in the induction motor in rotor, stator, and shaft bearing faults and newlineexplains that the analysis of fault in the induction motor and how to newlineclassify the faults in the stator, rotor, bearings. The novel technique newlineLeast Mean Square (LMS) filter and a novel hybrid neural network with newlinevi newlineFlower Pollination Algorithm (FPA) are to analyze the fault in electrical newlinemachines. Chapter 4 deal with the new hybrid neural network with newlineParticle Swarm Optimization (PSO) Artificial Neural Networks (ANN) newlineis considered as the fault identification strategy.