Rolling bearing fault diagnosis method based on gramian angular difference field and dynamic self-calibrated convolution module.

To address the problem of insufficient feature extraction abilities of traditional fault diagnosis methods under conditions of sample scarcity and strong noise interference, a rolling bearing fault diagnosis method based on the Gramian Angular Difference Field (GADF) and Dynamic Self-Calibrated Convolution (DSC) is proposed. First, the GADF method converts one-dimensional signals into GADF images to capture nonlinear relationships and periodic information in time-series data. Second, a dynamic self-calibrated convolution module is introduced to enhance the feature extraction ability of the model.

A discrimination model in waste plastics sorting using NIR hyperspectral imaging system.

Classification of plastics is important in the recycling industry. A plastic identification model in the near infrared spectroscopy wavelength range 1000-2500 nm is proposed for the characterization and sorting of waste plastics using acrylonitrile butadiene styrene (ABS), polystyrene (PS), polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). The model is built by the feature wavelengths of standard samples applying the principle component analysis (PCA), and the accuracy, property and cross-validation of the model were analyzed.