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<p>In response to the issues of incomplete local data degradation characterization and multi-scale, non-stationary characteristics exhibited during the degradation process of rotary machinery vibration signals, a remaining useful life (RUL) prediction method based on trend encoding and multi-scale spatio-temporal feature fusion is proposed. Firstly, a trend encoding method is introduced to compensate for the missing temporal information and long-term degradation information in vibration signal samples, enhancing the degradation characterization ability of local data. Subsequently, a soft threshold self-attention mechanism is proposed for the adaptive fusion of trend-encoded features and vibration signal features, to prevent imbalanced weight distribution. Finally, a spatio-temporal feature fusion network, MACNN-Informer, is designed. It possesses multi-scale spatial feature extraction capabilities and can effectively capture long-distance dependencies in sequential features, thereby better revealing the degradation characteristics of vibration signals at different degradation stages. Experimental results show that, compared with methods such as MSCNN, the proposed method–despite being slightly slower in inference speed–achieves the lowest prediction error and oscillation amplitude, making it well-suited for RUL prediction of critical rotating machinery components such as rolling bearings.</p>

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A remaining useful life prediction method for rotating machinery based on trend encoding and multi-scale spatio-temporal feature fusion

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Yurong Guo,
  • Jie Zhang,
  • Junliang Wang,
  • Jian Mao,
  • Feifan Lu

摘要

In response to the issues of incomplete local data degradation characterization and multi-scale, non-stationary characteristics exhibited during the degradation process of rotary machinery vibration signals, a remaining useful life (RUL) prediction method based on trend encoding and multi-scale spatio-temporal feature fusion is proposed. Firstly, a trend encoding method is introduced to compensate for the missing temporal information and long-term degradation information in vibration signal samples, enhancing the degradation characterization ability of local data. Subsequently, a soft threshold self-attention mechanism is proposed for the adaptive fusion of trend-encoded features and vibration signal features, to prevent imbalanced weight distribution. Finally, a spatio-temporal feature fusion network, MACNN-Informer, is designed. It possesses multi-scale spatial feature extraction capabilities and can effectively capture long-distance dependencies in sequential features, thereby better revealing the degradation characteristics of vibration signals at different degradation stages. Experimental results show that, compared with methods such as MSCNN, the proposed method–despite being slightly slower in inference speed–achieves the lowest prediction error and oscillation amplitude, making it well-suited for RUL prediction of critical rotating machinery components such as rolling bearings.