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- W4387143375 abstract "Infrared thermography (IRT) is applicable in non-contact temperature distribution measurement, becoming a widely-adopted condition monitoring tool. However, the multiscale characteristics and spatiotemporal degradation difference in infrared image streams hinder the extraction of representative degradation features for effective remaining useful life (RUL) prediction. Thus, a multiscale sparse ensemble network (MSEN) is presented to address this challenge. First, a hierarchical residual network (HRN) applies multiscale kernels to learn complementary degradation patterns and constructs a hierarchical connection structure to promote interactivity between scales. Moreover, a diversity-induced ensemble method synergistically integrating network pruning and local minima perturbation is developed to induce a series of varying HRNs. Then, MSEN combines these HRNs with diverse sparsity and local minima for accurate machine prognostics without largely increasing the parameter storage burden. A case study on an infrared image dataset is performed to evaluate the accuracy and competitiveness of the presented method." @default.
- W4387143375 created "2023-09-29" @default.
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- W4387143375 date "2023-08-26" @default.
- W4387143375 modified "2023-10-18" @default.
- W4387143375 title "A Multiscale Sparse Ensemble Network for Machine Prognostics Based on Infrared Image Streams" @default.
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- W4387143375 doi "https://doi.org/10.1109/case56687.2023.10260391" @default.
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