  The operating state of bearing directly affects the performance of rotating
machinery and how to accurately and decisively extract features from the
original vibration signal and recognize the faulty parts as early as possible
is very critical. In this study, the one-dimensional ternary model which has
been proved to be an effective statistical method in feature selection is
introduced and shapelets transformation is proposed to calculate the parameter
of it which is also the standard deviation of the transformed shaplets that is
usually selected by trial and error. Moreover, XGBoost is used to recognize the
faults from the obtained features, and an improved artificial bee colony
algorithm(ABC) where the evolution is guided by the importance indices of
different search space is proposed to optimize the parameters of XGBoost. Here
the value of importance index is related to the probability of optimal
solutions in certain space, thus the problem of easily falling into local
optimality in traditional ABC could be avoided.The experimental results based
on the failure vibration signal samples show that the average accuracy of fault
signal recognition can reach 97% which is much higher than the ones
corresponding to other extraction strategies, thus the ability of extraction
could be improved. And with the improved artificial bee colony algorithm which
is used to optimize the parameters of XGBoost, the classification accuracy
could be improved from 97.02% to about 98.60% compared with the traditional
classification strategy
