Disclosed is a rolling bearing
remaining life prediction method based on
feature fusion and particle filtering. According to an index calculation process, firstly, original features are extracted from
bearing vibration signals, the extracted original features are clustered by the adoption of a relevance clustering method, then, one typical feature is selected from each cluster to form optimal feature sets, and finally the feature sets are fused by the adoption of a weight fusion method into a final recession index. According to a life prediction process, firstly,
smoothing and
resampling are carried out on the recession index, the time interval is adjusted to be an expected value, state-
space model initial parameters are calculated by the adoption of least square fitting, then,
model parameters are updated in real time according to new
observation data, and finally the
remaining life of a bearing can be predicted. According to the rolling bearing
remaining life prediction method based on
feature fusion and particle filtering, the difference between the life prediction result and a true value is small, and the application effect is good.