Distributed analysis of motors fault in industrial environments

Immediate detection and diagnosis of existing faults and faulty behavior of electrical motors using electrical signals is an important interest of the power industry. Motor current signature analysis is a modern approach to diagnose faults of induction motors. The approach has some shortcomings that limit the accuracy of diagnosis. It is very vulnerable to the environmental noise, voltage harmonics, and operation of nonlinear equipment. Particularly operation of other similar motors nearby may result in wrong warnings being signaled. As a result interpreting current signals usually requires extra calculation and considerations. This paper investigates the significance of propagated fault signatures through distributed power systems. The aim is to explain and quantify the different observations of fault signals and hence diagnoses machine faults with a higher accuracy. A systematic approach has been employed to estimate the influence of fault signals in the currents of neighboring electrical motors. Further analysis in attenuation of electrical signals leads to a graphical framework that evaluates propagation of fault signals in power networks. In order to implement the concept of distributed diagnosis, fault indices and propagation charts, have been employed. The solution then has been evaluated using a scaled down simulation model.