Towards enhancing insulation condition monitoring of electric machines using a signal processing based technique

The Oil/Gas industry is facing two major problems with its aging electric power generators. First, the Oil/Gas industry experience long outages of these power generation units for re-winding, during a time when power is extremely needed. Second, these fixed-interval scheduled maintenance practices are very costly as on average they cost around US$1 million per machine. To address these problems, we propose to develop an effective, reliable and automatic signal processing based technique to monitor the insulation condition of electric machines.