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<p>Production safety and efficiency depends on the reliability of the critical components in industrial equipment. However, signals measured by sensors may have complicated nonlinear and long-term sequential dynamics, which are difficult to predict faults correctly. To overcome these challenges, especially the detection of the subtle early-stage fault features and the ultra-long faults modeling, in this paper, a new SPHT-LSTM model based on hierarchical Transformers and LSTMs have been proposed. Multi-scale feature decoupling and dynamic fusion is made possible by the dual-channel architecture (superposition S1 long-term trends and progressive P2 short-term variations) based on the temporal sensitivity of LSTMs and the global dependency capturing of Transformers. Also, a Mean Performance Degradation (MPD) measure is proposed to measure degradation using segmented mean analysis to minimize noise and increase saliency of fault features. The univariate/ multivariate dataset and engineering data experimental validation reveals that SPHT-LSTM attains a prediction accuracy of 89% and 96% of early weak faults and accelerated degradation stages, respectively, which is much higher than that of the traditional techniques. Such findings indicate its strength and usefulness in condition-based maintenance (CBM) in the industrial environment.</p>

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SPHT-LSTM: a novel industrial equipment condition prediction method

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Bo Hu,
  • Yongjun Zhang,
  • Jin Guo,
  • Fengwei Jing

摘要

Production safety and efficiency depends on the reliability of the critical components in industrial equipment. However, signals measured by sensors may have complicated nonlinear and long-term sequential dynamics, which are difficult to predict faults correctly. To overcome these challenges, especially the detection of the subtle early-stage fault features and the ultra-long faults modeling, in this paper, a new SPHT-LSTM model based on hierarchical Transformers and LSTMs have been proposed. Multi-scale feature decoupling and dynamic fusion is made possible by the dual-channel architecture (superposition S1 long-term trends and progressive P2 short-term variations) based on the temporal sensitivity of LSTMs and the global dependency capturing of Transformers. Also, a Mean Performance Degradation (MPD) measure is proposed to measure degradation using segmented mean analysis to minimize noise and increase saliency of fault features. The univariate/ multivariate dataset and engineering data experimental validation reveals that SPHT-LSTM attains a prediction accuracy of 89% and 96% of early weak faults and accelerated degradation stages, respectively, which is much higher than that of the traditional techniques. Such findings indicate its strength and usefulness in condition-based maintenance (CBM) in the industrial environment.