Motors Fault Recognition Using Distributed Current Signature Analysis
type: CreativeWork
description: Immediate detection and diagnosis of existing faults and faulty behaviour of electrical motors using electrical signals is one of the important interests of the power industry. Motor current signature analysis is a modern approach to diagnose faults of induction motors. This thesis investigates the significance of propagated fault signatures through distributed power systems, aiming at explaining and quantifying different observations of faults signals and hence diagnoses machine faults with a higher accuracy. Electrical indicators of faults, unlike other fault indicators, (e.g. vibration signals), propagate all over the network. Therefore fault signals may be manipulated by operation of neighbouring motors and the system‘s environmental noise. Both simulation and practical results clearly demonstrate the signal interference and hence confusion in diagnosis due to presence of a faulty motor nearby. Thus a knowledge based system is necessary to understand the meaning of the signals manifested at various parts of the distributed power system. On another side, taking into account that fault signals are travelling all over the network, several observations can be made for events in the network. In this thesis the idea of cross evaluation of fault signals considering signal propagation will be discussed and analysed. The research attempts to improve diagnosis reliability with a simple and viable framework of decision making. The thesis scope is limited to monitoring behaviour of induction motors in distributed power systems. These types of electrical motors are the main load of most industries. In this thesis, existing formulations of fault signatures would not be significantly disturbed, as distributed diagnosis can fit into an existing framework of current signature analysis. The research takes advantage of multiple areas of study to formulate propagation of fault signals while they are travelling in a scaled down distributed power system. At the beginning, a systemati…
abstract: Immediate detection and diagnosis of existing faults and faulty behaviour of electrical motors using electrical signals is one of the important interests of the power industry. Motor current signature analysis is a modern approach to diagnose faults of induction motors. This thesis investigates the significance of propagated fault signatures through distributed power systems, aiming at explaining and quantifying different observations of faults signals and hence diagnoses machine faults with a higher accuracy. Electrical indicators of faults, unlike other fault indicators, (e.g. vibration signals), propagate all over the network. Therefore fault signals may be manipulated by operation of neighbouring motors and the system‘s environmental noise. Both simulation and practical results clearly demonstrate the signal interference and hence confusion in diagnosis due to presence of a faulty motor nearby. Thus a knowledge based system is necessary to understand the meaning of the signals manifested at various parts of the distributed power system. On another side, taking into account that fault signals are travelling all over the network, several observations can be made for events in the network. In this thesis the idea of cross evaluation of fault signals considering signal propagation will be discussed and analysed. The research attempts to improve diagnosis reliability with a simple and viable framework of decision making. The thesis scope is limited to monitoring behaviour of induction motors in distributed power systems. These types of electrical motors are the main load of most industries. In this thesis, existing formulations of fault signatures would not be significantly disturbed, as distributed diagnosis can fit into an existing framework of current signature analysis. The research takes advantage of multiple areas of study to formulate propagation of fault signals while they are travelling in a scaled down distributed power system. At the beginning, a systemati…
author: Gheitasi, Alireza
keywords: Fault finding, Induction motors, Distributed diagnosis, Current signature analysis, Power system monitoring, Fault modelling
date published: 2012
date modified: 2026-07-27T18:46:13Z
provider: AUT University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: AUT University