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  • Open Access

    ARTICLE

    Detection Method for Bolt Loosening of Fan Base through Bayesian Learning with Small Dataset: A Real-World Application

    Zhongyun Tang1,2,3, Hanyi Xu2, Haiyang Hu1,3,*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-29, 2026, DOI:10.32604/cmc.2025.070616 - 09 December 2025

    Abstract With the deep integration of smart manufacturing and IoT technologies, higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection. For industrial fans, base bolt loosening faults are difficult to identify through conventional spectrum analysis, and the extreme scarcity of fault data leads to limited training datasets, making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity. This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution. This More >

  • Open Access

    ARTICLE

    Fruit Ripeness Prediction Based on DNN Feature Induction from Sparse Dataset

    Wan Hyun Cho1, Sang Kyoon Kim2, Myung Hwan Na1, In Seop Na3,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 4003-4024, 2021, DOI:10.32604/cmc.2021.018758 - 24 August 2021

    Abstract Fruit processing devices, that automatically detect the freshness and ripening stages of fruits are very important in precision agriculture. Recently, based on deep learning, many attempts have been made in computer image processing, to monitor the ripening stage of fruits. However, it is time-consuming to acquire images of the various ripening stages to be used for training, and it is difficult to measure the ripening stages of fruits accurately with a small number of images. In this paper, we propose a prediction system that can automatically determine the ripening stage of fruit by a combination… More >

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