Detection and diagnosis of faults in electrical motors using electrical signals is one of the important interests of the power industry. One of the main challenges is that of the interferences among various propagated signals through the power network. Operational parameters like size of motors, speed of rotation, spatial distribution of network components and others ention problem.

This thesis investigates the significance of propagated fault signatures through distributed power systems, aiming at explaining and quantifying different observations of fault signals and hence diagnoses of machine faults with a higher accuracy.

A systematic approach has been employed in modelling the key acting parameters in a typical industrial distributed motor system. The effect of typical faults as they travel through the network has been studied. A framework has been developed to estimate the origin of fault signal by employing propagation patterns and estimating anticipated fault representatives around the network.

Analytically results demonstrate significant improvement in isolating interference among electrical motors that work together in electrical power system networks. This leads to a simple strategy for identifying the ownership of fault signals and hence having more accurate diagnostic results.