Data Challenges for Structural Health Monitoring of Electrical Machines

Nuclear power stations typically run electrical machines on a conservative hard-life basis, i.e., by specifying the number of operating hours and start-up/shut-down cycles before replacement. Not only is this approach costly, but it does not provide through-life performance information to extend operations or understand the failure modes. While there is a large amount of research on fault detection within induction motors, little has been conducted on extracting true load-related electrical and modal signatures that properly reflect the behavior of the machines. Many types of industrial plants use large rotating machines to convert electrical input into mechanical loads. The focus of this work is induction motors. These induction motors require three phase power and rotate at the slip frequency which is slightly slower than the synchronous power grid frequency. The light load and low slip cause operating conditions that present a significant challenge to structural health monitoring. The overwhelming influence of the power grid frequency on the stator current data makes it difficult to analyze sidebands which are used to detect rotor faults. Researchers have yet to devise an effective strategy for isolating the adjacent sidebands to enable the detection of rotor faults in lightly loaded induction motors, such as those in nuclear power plants. This work focuses on applying a modified extended Kalman filter to the stator current data and performing spectral subtraction to remove the closely coupled power grid frequency to observe the sidebands of the motors. The results for the analyses showed that the Kalman filter was able to create a model of the power grid frequency allowing for the signal to be removed from the data. This work also analyzes the subtraction of the rotor frequency from the acceleration data with the addition of a continuous wavelet transform prefilter.