To predict the remaining useful life for the key structures of
heavy-duty railway wagons using condition monitoring data, methods for
the coupler body with and without visible cracks were proposed. First, a
method based on the delay time and hypothesis testing was proposed,
considering the case without visible cracks in the coupler body. Then,
for the case of visible cracks, methods based on a hypothetical
distribution and support vector regression with the Kalman filter were
proposed. Finally, by taking the coupler body monitoring data as an
example, the prediction accuracies of the proposed methods were
compared. The results indicated that the prediction method that only
considers the common characteristics of the research objects had an
average relative error of 57.56% for the coupler structure with a long
lifespan. Considering the delay time of the current state of the
structure and the assumed distribution prediction method, the relative
error was reduced to 34.52%, and the remaining useful life prediction
value fluctuated sharply with respect to the service mileage. On this
basis, considering the performance degradation process of the structure,
the change in the remaining useful life prediction value was smoother,
and the relative error was 43.67%. The methods for predicting the
remaining useful life of railway heavy-duty coupler bodies using
condition monitoring data have important theoretical and practical value
for improving vehicle safety, reducing maintenance costs, and accurately
evaluating the remaining useful life.
Remaining Useful Life Estimation Considering Prior Accelerated Degradation Data and Bayesian Inference for Multi-Stress Operating Conditions
