Abstract:
Preventative motor fault detection is of prime importance for modern plant management. During research into rotor faults, performed at the Motor Systems Resource Facility (MSRF), an optimized rotor fault detection and classification method was
proposed. In critical process applications, the suggested method would provide the foundation for continually monitoring a machine in a noninvasive way and enhancing the ability of maintenance systems to identify impending rotor failures. This would then
drive maintenance schedules more efficiently since the diagnosis data can be made available to a conventional operator interface station over an open network. With the advent of a current signature analysis algorithm, many industries will be driven toward on-line, noninvasive diagnostic solutions. The proposed method can provide the information to diagnose problems accurately and quantitatively using motor dynamic eccentricity sidebands as a universal rotor fault detection and classification index. Also, related research into the effects of rotor fault isolation from load torque will enable a determination of the relative severity of a broken rotor bar or any type of air-gap asymmetries. The objective of this work is to implement a proof of concept laboratory test of the suggested method. Three induction machines were tested on a dynamometer at twenty-eight loading points and different source and load conditions, verifying detection accuracy of the implemented technique.
