The operating state of bearing affects the performance of rotating machinery; thus, how to accurately extract features from the original vibration signals and recognise 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 shapelet transformation is proposed to calculate the parameter of one‐dimensional ternary model that is usually selected by trial and error. Then XGBoost is used to recognise the faults from the obtained features, and artificial bee colony algorithm (ABC) is introduced to optimise the parameters of XGBoost. Moreover, for improving the performance of intelligent algorithm, an improved strategy where the evolution is guided by the probability that the optimal solution appears in certain solution space is proposed. 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 traditional extraction strategies. And with the help of improved ABC algorithm, the performance of XGBoost classifier could be optimised; the accuracy could be improved from 97.02% to 98.60% compared with the traditional classification strategy.
The effect of Y intercalation
on the atomic, electronic, and magnetic
properties of the graphene/Co(0001) interface is studied using state-of-the-art
density functional theory calculations. Different structural models
of the graphene/Y/Co(0001) interface are considered: (i) graphene/Y/Co(0001),
(ii) graphene/1ML-YCo
2
/Co(0001), and (iii) graphene/bulk-like-YCo
2
(111). It is found that the interaction strength between graphene
and the substrate is strongly affected by the presence of Y at the
interface and the electronic structure of graphene (doping and the
appearance of the energy gap) is defined by the Y concentration. For
the Co-terminated interfaces between graphene and the metallic support
in the considered systems, the electronic structure of graphene is
strongly disturbed, leading to the absence of the linear dispersion
for the graphene π band; in the case of the Y-terminated interfaces,
a graphene layer is strongly
n
-doped, but the linear
dispersion for this band is preserved. Our calculations show that
the magnetic anisotropy for the magnetic atoms at the graphene/metal
interface is strongly affected by the adsorption of a graphene layer,
giving a possibility for one to engineer the magnetic properties of
the graphene/ferromagnet systems.
<abstract>
<p>Aiming at improving the operating efficiency of air freight station, the problem of optimizing the sequence of inbound/outbound tasks meanwhile scheduling the actions of elevating transfer vehicles (ETVs) is discussed in this paper. First of all, the scheduling model in airport container storage area, which considers not only the influence of picking sequence, optimal ETVs routing without collision, but also the assignment of input and output ports, is established. Then artificial bee colony (ABC) is proposed to solve the above scheduling issue. For further balancing the abilities of exploration and exploitation, improved multi-dimensional search (IMABC) algorithm is proposed where more dimensions will be covered, and the best dimension of the current optimal solution is used to guide the evolutionary direction in the following exploitation processes. Numerical experiments show that the proposed method can generate optimal solution for the complex scheduling problem, and the proposed IMABC outperforms original ABC and other improved algorithms.</p>
</abstract>
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