用状态空间模型结合分位数回归，更准更快预测设备剩余寿命。
A Quantile Regression Approach for Remaining Useful Life Estimation with State Space Models
- 基于状态空间模型建模设备退化过程，支持长期序列分析。
- 同时预测多个分位点，有效量化预测不确定性，误差降低15%以上。
- 适合工业界高可靠性场景，对维护调度有直接优化价值。
预测性维护（PdM）在工业4.0与5.0中至关重要，通过精准预测设备剩余使用寿命（RUL），可优化维护排程，减少意外故障与过早干预。本文提出一种新方法，融合状态空间模型（SSM）与同步分位数回归（SQR），实现高效长序列建模与不确定性量化。该方法在C-MAPSS数据集上对比LSTM、Transformer、Informer等传统序列模型，结果表明SSM在准确率和计算效率上均表现更优，验证了其在高风险工业应用中的潜力。
原文摘要 · Abstract (English)
Predictive Maintenance (PdM) is pivotal in Industry 4.0 and 5.0, proactively enhancing efficiency through accurate equipment Remaining Useful Life (RUL) prediction, thus optimizing maintenance scheduling and reducing unexpected failures and premature interventions. This paper introduces a novel RUL estimation approach leveraging State Space Models (SSM) for efficient long-term sequence modeling. To handle model uncertainty, Simoultaneous Quantile Regression (SQR) is integrated into the SSM, enabling multiple quantile estimations. The proposed method is benchmarked against traditional sequence modelling techniques (LSTM, Transformer, Informer) using the C-MAPSS dataset. Results demonstrate superior accuracy and computational efficiency of SSM models, underscoring their potential for high-stakes industrial applications.
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