Declining
strength is a key limitation for the promotion and application
of rubber–cement-based composites. Silane coupling agents (SCAs)
provide a new idea for improving the weak interface of rubber–cement-based
materials, but the mechanism of SCA modification in the improvement
of mechanical properties is not well established. In this paper, for
the first time, a multi-scale exploration of SCA-modified rubber–cement-based
materials was conducted to explore the modification effect and to
reveal the modification mechanism using a combination of experiments
and simulations. The experimental results showed that the mechanical
properties of KH570-modified rubber–cement-based composites
were greatly improved as compared with the composites treated with
KH550, KH560, and A151. The modification treatment can effectively
introduce KH570 molecules into the weak interface, thus effectively
repairing the interfacial defects between the rubber particles and
the cementitious material. The molecular dynamics simulation results
show that the weak intermolecular interaction between the butadiene
group of butadiene rubber and the calcium–silica structure
of C–S–H is the essence of the weak combination of the
two phases. The mechanical occlusion between KH570 molecules and rubber
molecular chains can effectively bond the elastic rubber to the hard
cement matrix, improving the frictional resistance and chemical bonding
at the interface and giving it full play in the excellent deformation
properties of rubber when the composite material is stressed.
Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensive human labour and prior knowledge are also highly required during these selections. To solve the above problems, a multimodel decision fusion method based on Deep Convolutional Neural Network and Improved Dempster-Shafer Evidence Theory (DCNN-IDST) is proposed for the inspection of rolling bearing. To solve the defect of the original evidence theory method in the fusion of high-conflict evidence, the fuzzy consistency matrix is introduced. By calculating the factor weight, the reliability and rationality of D-S evidence theory are improved. The DCNN model can learn features from the original data and carry out adaptive feature extraction for multiple sensor information. The features extracted by DCNN adaptively are input into multiple network models for decision fusion. The new method of DCNN-IDST multimodel decision fusion is applied to detect the damage of rolling bearings. To evaluate the effectiveness of the proposed method, both the BP neural network and RBF neural network are used to set up a multigroup comparison test. The result demonstrates that the proposed method can detect the fault of the rolling bearing effectively and achieve the highest diagnosis accuracy among all the tested methods in the experiment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.