This invention discloses a method for determining the bearing slippage failure threshold based on state
information fusion analysis. First, a bearing twin
simulation model is constructed, and the bearing cage slippage rate and stress-strain of each component are measured simultaneously. An improved L-P theory is used to construct a bearing life theoretical model and calculate the corresponding PV value. Second, the bearing cage center-of-
mass motion trajectory is measured, and its maximum, minimum, average, and standard deviation are calculated to construct a center-of-
mass trajectory fusion index to measure the cage motion stability.
Bearing vibration acceleration data is measured, and a bearing life prediction
state model is constructed based on the ARMA-transfer learning
algorithm. Finally, based on the obtained bearing life theoretical model, PV value, center-of-
mass motion trajectory fusion index, and bearing life prediction
state model, and combined with
fuzzy clustering evidence theory, a bearing state information decision-making layer fusion is performed to determine the bearing slippage failure threshold. This invention enables accurate calculation of the bearing slippage threshold under varying operating conditions.