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Summary: In recent years, data-driven approaches for remaining useful life (RUL) prognostics have gained widespread attention. This paper explores the degradation process of bearings and proposes an enhanced encoder-decoder framework for accurately predicting the remaining useful life of bearings by using trigonometric functions and cumulative operations to enhance the quality of health indicators. The experimental results demonstrate the superiority and feasibility of the proposed method compared to several state-of-the-art methods.
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Summary: This article introduces a novel feature-attention-based end-to-end approach for RUL prediction, which effectively improves the model's predictive performance. By dynamically allocating attention weights in the input data and combining the use of BGRU and convolutional neural networks for extracting long-term dependencies and capturing local features, abstract representations for RUL prediction are learned.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
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Summary: This research introduces a data-driven modeling method for estimating the remaining useful life of bearings using a T-S fuzzy inference system. Parameters of the model are identified through subtractive clustering method, and the maximum likelihood method of mixture distribution analysis is proposed for calculating cluster parameters on the time axis. The effectiveness of the proposed method is highlighted through numerical experiments benchmarked against existing methods.
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Jichao Zhuang et al.
Summary: This study introduces a cross-domain approach for predicting the remaining useful life of industrial machines. It utilizes a temporal convolution network and residual self-attention to extract hidden features, while also designing a new cross-domain adaption architecture to learn domain invariant features, showing effectiveness and superiority in comparison with other methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
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Tianfu Li et al.
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JOURNAL OF MANUFACTURING SYSTEMS
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