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Machine failure prognostic is concerned with the generation of long term predictions and the estimation of the probability density function of the remaining useful life. For this we propose a framework for data-driven prediction of RUL. To solve the problem of lacking direct condition information in predicting equipment residual useful life (RUL), particle-filtering model is built for equipment's RUL prediction with indirect information, which is easy to get .This paper introduces a particle-filtering modeling approach for predicting the remaining lifetime of the wind turbine gearbox based on information of SCADA system monitoring. Data from the SCADA system for the wind turbine gearbox were used to validate the proposed methodology. The outcome shows that the PF method has a better effect on RUL prediction.Finally, the model verified through on-site data collection. It shows that the method is practical value in the prediction of remaining life. A new way for state recognition of complex equipment is provided.

A Particle-Filtering Approach for Remaining Useful Life Estimation of Wind Turbine Gearbox