In this paper, the degradation model is assumed and the model parameters are estimated from the vibration signal data of the induced-drift blower, and the result is used to diagnose equipment abnormalities and predict remaining useful life(RUL). Using the field degradation data, the model parameters in the degradation model were estimated using the maximum likelihood estimation method. In the case of the linear degradation model, the slope parameter was tested to find the starting time period of the degradation process and the remaining useful life is estimated repeatedly using the accelerated degradation model. Using the field vibration data the suitable degradation models and the effective monitoring and estimation methods for preventive maintenance are analyzed. Time period 1 to 4 were sections in which degradation of equipment did not occur. For time period 5 to 6, the linear increase in the vibration signal was verified through the linear degradation model. After time period 7, the degradation has been accelerated exponentially. The remaining useful life was estimated through the accelerated degradation model. These results shows that the alert signal in time period 5 to 6, the alarm signal in period 7 to 9, and the warning signal after time period 10 should be displayed based on the diagnostic analysis. The vibration signal data obtained from the Induce-drift blower was used in the degradation model, and the model parameters of degradation model were estimated using the field before the failure obtained from the field to check whether the equipment was deteriorated and whether the residual useful life is enough to delay preventive maintenance to the next time period.