EVALUATION OF MOTOR BEARING CONDITION IN COMBINED CYCLE POWER PLANT USING VIBRATION ANALYSIS

Abstract

Electrical motor is a device that converts electrical to mechanical energy to perform a specified job and it is very significant in any plant. Any sudden failure in an electrical motor shall cause effect on the plant performance and monetary loss to the plant. Similarly, in a combined cycle power plant, many critical motors where those motors and loads must run non-stop without failures. Any failure will eventually cause downtime and production loss. Therefore, a proper maintenance philosophy and analysis can lead to proper motor maintenance in line with optimized time and cost. This study evaluated the condition of critical motor in combined cycle power plant using vibration analysis with SKF Microlog GX Series CMXA 75. This study also proposed the precision maintenance schedule subject to maintenance time and cost for critical motor. A comparison between precision maintenance and predictive maintenance cost of critical motor were established. Two critical motors were selected which are Low Pressure Feedwater Pump Motor (LPFW) and Cooling Tower (CT) Fan Motor. LPFW motor function to feed water to Heat Recovery Steam Generator (HRSG) LP section whereas CT fan motor is used to cool down the condensate water coming out from steam turbine condenser. The results of the vibration will reflect the performance of the motor and the maintenance action plan needed to be taken if required. Vibration data were collected in gravity energy (gE) peak to peak and mm/s (millimeter per second). The data then analyzed through the SKF software and Microsoft Excel. Standard results for motor less than 132kW size, vibration shall be lower than 2.0gE and 2.0mm/s respectively depending on the motor specification and its application. The maintenance cost is calculated and compared with the cost of maintenance without this vibration analyses. The cost comparison shows that, performing vibration analyses and detecting the defect or root cause in earlier stage will be cost effective compared to the defect worsen. By taking vibration data, the fault can be predicted and detected in early stage and can be eliminated with lower cost compared to without taking vibration data.