2results about How to "Active guidance" patented technology

This application relates to a cable distribution box with a moisture-proof and heat-dissipating structure, belonging to the field of electrical equipment technology. It includes a condensate collection structure located inside the box, which guides and collects condensate generated within the box through a V-shaped groove, a top water guide groove, a vertical water guide groove, and an annular water collection groove, with each part's inner wall coated with a hydrophilic coating; a heat dissipation module located outside the box; and a flow guiding component for transporting condensate from the condensate collection structure to the heat dissipation module, the flow guiding component including a hollow flow guiding column, a filter ring, and an air bladder ring. The heat dissipation module transports the condensate to the evaporation tank of the heat dissipation fins through a capillary core, flexible rubber tube, etc., and discharges the condensate to the outside through evaporation. This application achieves effective guidance and collection of condensate within the cable distribution box, and discharges it through evaporation, thus achieving the technical effect of moisture-proof and heat dissipation.

The application belongs to the technical field of industrial process monitoring, and particularly relates to a rolling bearing fault classification method based on multi-convolution network fusion. The method comprises the following steps: step S1: collecting original fault data, extracting the running parameters and size parameters of the corresponding bearing, and calculating three kinds of fault characteristic frequencies by using the above parameters; step S2: using continuous wavelet transform on the original fault data to obtain four kinds of time-frequency diagrams; step S3: dividing the time-frequency diagram set obtained by performing continuous wavelet transform on all original data into a training set and a verification set; step S4: training a fault classification model by using the training set; and step S5: verifying the classification performance of the model by using the verification set. The application has the beneficial effects of improving the explainability of the overall network, improving the flexibility of the network, and being higher in classification precision than the traditional classification.