Motors fault recognition using distributed current signature analysis
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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 systematic approach has been employed to estimate influence of fault signals in currents of neighbouring electrical motors. Further analysis in attenuation of electrical signals leads to a technical framework that evaluates propagation of fault signals in power networks. The framework has been developed to estimate origin of fault signal by employing propagation patterns and estimating anticipated fault representatives around the network. An analytical process has been proposed to take advantage of multiple observations in order to diagnose the type and identify origin of fault signals. This can help maximize the number of independent observations and thus improve the accuracy of traditional approaches to current signature analysis. In general, this provides a better monitoring of behaviour of electrical motors at a given site. A rewarding system has been used to identify and track the signals caused by motors and quantify association of current signals with known industrial faults. An example of a scaled down distributed power system has been simulated to describe behaviour of distributed power systems with faulty components. The simulation model is carefully compared with the practical results to validate the simulation results thoroughly. Type and strength of faults and size, speed, load and placement of electrical motors are acting variables in propagation patterns of fault signals. These variables have been simulated in a scaled down industrial power network to examine distributed diagnosis in the new environment. In addition a number of scaled-down experiments have been employed to verify results of simulation models and confirm the accuracy of results. Analytical results demonstrate significant improvement in describing interference amongst electrical motors that work together in an electrical network. This leads to a simple strategy for identifying the ownership of fault signals and hence having more accurate diagnostic results. Further developments in modelling the propagation of fault indicators emerged for improving the reliability and efficiency of fault diagnosis in industrial systems. On the other hand, a number of shortcomings have been observed in implementing strategy of distributed diagnosis including confusion among many similar faults in the power network and malfunctioning of the diagnosis system due to non-linear interferences of noise signals. Some of these problems are believed to be solvable by using a proper numerical solution (e.g. Artificial Neural Network, Bayesian, etc.) to process fault indices and propagation patterns before and after occurrence of each fault. In conclusion, the thesis does not claim to provide a complete solution of fault diagnosis in electrical motors. But it is an attempt to provide a more dependable industry solution for fault diagnosis in induction motors. Distributed diagnosis is a framework which takes advantage of multiple observations of a single fault and hence it is dependent on quality of acquired signals among individual observations.