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In order to enhance interference immunity and reduce the breakage of rotating shafts (rotors) which happens frequently especially in submersible permanent magnet synchronous motor (PMSM) applications, first a typical rotor structure is established serving as the comparative object, and then two improved schemes with optimal mechanical structure are proposed. Rotor strength, rotor stiffness, and the influence of unilateral magnetic pull on the rotor deflection are calculated analytically. The modal simulation, stress field simulation, and unilateral magnetic pull simulation are analyzed using finite element method (FEM). Position deviation and bending deformation of the rotor caused by the unilateral magnetic pulling force is taken into account to improve the simulation accuracy. The simulation results illustrate that the submersible rotor improved by the proposed schemes presents more excellent stiffness and strength performance compared with the traditional scheme, which indicates that the proposed schemes have important reference values for the structural design of submersible rotor.

As an undesired and instantaneous failure in the production of cold-rolled strip products, strip breakage results in yield loss, reduced work speed and further equipment damage. Typically, studies have investigated this failure in a retrospective way focused on root cause analyses, and these causes are proven to be multi-faceted. In order to model the onset of this failure in a predictive manner, an integrated multi-source feature-level approach is proposed in this work. Firstly, by harnessing heterogeneous data across the breakage-relevant processes, blocks of data from different sources are collected to improve the breadth of breakage-centric information and are pre-processed according to its granularity. Afterwards, feature extraction or selection is applied to each block of data separately according to the domain knowledge. Matrices of selected features are concatenated in either flattened or expanded manner for comparison. Finally, fused features are used as inputs for strip breakage prediction using recurrent neural networks (RNNs). An experimental study using real-world data instantaneous effectiveness of the proposed approach.

The range of available materials to be used in the field of Smart textile is vast, but not thoroughly analysed in depth. This paper presents analysis and testing of textile materials suitable for tensiometric functioning in Smart textile applications. The research was focused on finding ideal proportions of appropriate materials combinations. Supplied samples differs in carrier fibre and conductive fibre material and its amount in resulting threads. Evaluation of the samples is based on the electrical and mechanical parameters before and during the testing. Samples underwent series of pull-test cycles during which the electrical parameters (4-wire contact resistance and inductance) and physical behaviour (stretchability, cyclic stress resistivity and force necessary for breakage) were observed and recorded. Correlation between material composition of each sample and changes of the aforementioned parameters during testing was evaluated. Impregnation can improve thread structure and physical behaviour. Lycra has proven as extremely flexible and capable of sustaining same level resistance even during cyclic stress. It can be concluded that textile threads with adequate composition can be used as sensoric, heating or monitoring equipment.