The invention discloses a bearing fault diagnosis and prediction method based on an extended
Kalman filtering algorithm, and the method comprises the following steps: 1) employing a
full service life cycle vibration
signal of a bearing; 2) constructing an AR model through the vibration
signal, carrying out the filtering analysis of the vibration
signal, and highlighting a signal correlated with a fault; 3) extracting
energy information correlated with a
wavelet packet coefficient through employing
wavelet packet transformation, and constructing a feature character; 4) carrying out the calculation of a
mahalanobis distance, constructing health indexes based on the
mahalanobis distance, converting the non-negative and non-
Gaussian distribution health indexes into
Gaussian distribution data through Box-Cox transformation, and determining a related abnormal threshold range through the features of
Gaussian distribution and the inverted Box-Cox transformation; 5) carrying out fitting analysis of
health index data in a loss period, constructing a degeneration model and a status
space model, updating
model parameters through employing current data and the extended
Kalman filtering algorithm, and predicting the remaining service life of the bearing. The method is higher in prediction precision, and is shorter in consumed time.