Equipment Remaining Useful Life Prediction Method Using Multi-Scale Mamba Architecture

TIAN Congbao; WANG Zhaoqiang; HU Changhua; FAN Hongdong; ZHENG Ziyang; KONG Xiangyu

doi:10.7652/xjtuxb202605015

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Equipment Remaining Useful Life Prediction Method Using Multi-Scale Mamba Architecture

更新时间：2026-04-27

- Equipment Remaining Useful Life Prediction Method Using Multi-Scale Mamba Architecture
- Journal of Xi'an Jiaotong University Vol. 60, Issue 5, Pages: 153-163(2026)
- 作者机构：
  
  火箭军工程大学智控实验室，710025，西安
  浙江大学流体动力基础件与机电系统全国重点实验室，310058，杭州
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202605015
  CLC： TH17
- Received：20 June 2025，
  
  Published：10 May 2026
- 稿件说明：
移动端阅览
TIAN Congbao, WANG Zhaoqiang, HU Changhua, et al. Equipment Remaining Useful Life Prediction Method Using Multi-Scale Mamba Architecture[J]. Journal of Xi'an Jiaotong University, 2026, 60(5): 153-163.
DOI：

TIAN Congbao, WANG Zhaoqiang, HU Changhua, et al. Equipment Remaining Useful Life Prediction Method Using Multi-Scale Mamba Architecture[J]. Journal of Xi'an Jiaotong University, 2026, 60(5): 153-163. DOI： 10.7652/xjtuxb202605015.

摘要

针对现有基于Mamba模型的装备剩余寿命预测方法中存在的卷积核单一固定、局部时序特征捕捉能力不足的问题，提出了一种采用多尺度Mamba架构的剩余寿命预测方法。该方法构建了多尺度Mamba与门控循环单元并行网络：多尺度Mamba通过多尺度卷积层与结构化状态空间模型协同作用，捕捉装备长期退化趋势；门控循环单元借助门控机制提取短时动态特征，实现了长短期特征互补。设计互补特征动态融合模块，通过动态门控生成权重对上述两类特征加权融合，有效抑制冗余信息并强化关键特征。利用全连接层完成退化特征映射与剩余寿命预测。在美国航空航天局的航空发动机数据集上验证，结果表明：所提方法的均方根误差优于主流模型，评分函数较最优基线模型降低22%～73%；运行效率较最新的基于Mamba模型的方法提升57.5%。所提多尺度Mamba架构为装备剩余寿命预测提供了一种兼具精度与效率的可行方案。

Abstract

To address the issues of fixed single convolution kernels and insufficient local temporal feature extraction in existing Mamba-based equipment remaining useful life (RUL) prediction methods

a novel RUL prediction approach based on a multi-scale Mamba architecture is proposed. A parallel network combining multi-scale Mamba and gated recurrent units (GRUs) was constructed. The multi-scale Mamba captures long-term degradation trends through the synergistic interaction of multi-scale convolutional layers and structured state-space models

while the GRUs extract shortterm dynamic features via gating mechanisms

achieving complementary long-and short-term feature representation. A module for dynamic fusion of complementary features was designed to perform weighted fusion of these two types of features through dynamic gating weights

effectively suppressing redundant information and enhancing key features. Fully connected layers were then employed to map degradation features and predict RUL. Validation on the NASA's aircraft engine dataset demonstrates that the proposed method achieves superior root mean square error compared to mainstream models

with the scoring function reduced by 22%—73% compared to the best baseline model. Additionally

it improves computational efficiency by 57.5% over the latest Mamba-based methods.

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