用于联合故障诊断和剩余使用寿命估计的注意力增强多任务学习的泄漏鲁棒评估和数据规模敏感性
Leakage-Robust Evaluation and Data-Scale Sensitivity of Attention-Enhanced Multi-Task Learning for Joint Fault Diagnosis and Remaining Useful Life Estimation
- Western Illinois University（西伊利诺伊大学）
- Ahsanullah University of Science and Technology（阿山努拉科技大学）
- Trine University（特莱恩大学）
- Sapienza University of Rome（罗马第一大学）
- Bangladesh University of Engineering and Technology（孟加拉工程技术大学）
- Chittagong University of Engineering and Technology（吉大港工程技术大学）
- Rajshahi University of Engineering & Technology (RUET)（拉杰沙希工程技术大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究联合故障诊断和剩余使用寿命估计的多任务深度学习模型中，滑动窗口序列划分对性能的影响。采用AMTLNet架构，引入基于块的泄漏审核划分协议，通过实验得出任务稳定性与标签来源有关，贡献了评估协议和相关证据。
AI中文摘要：
联合执行故障分类和剩余使用寿命（RUL）回归的多任务深度学习模型在预测性维护中越来越常用，但滑动窗口序列划分为训练集和测试集的方式会严重影响性能。我们使用注意力增强多任务架构AMTLNet在三个公共基准上研究此问题。结果表明，简单划分会使分类准确率从真实的20%-60%膨胀到99.9%，或因退化类表示降至0%。为此引入基于块的、泄漏审核的划分协议，并使用五个种子、单向方差分析和Tukey HSD检验评估模型。在C-MAPSS上，AMTLNet在分类上与单任务CNN-LSTM基线相当，准确率达84.12±0.96%，R2为0.86±0.01，显著优于简单多任务基线。在较小的轴承和液压数据集上，多任务训练不稳定但故障模式不同。消融结果表明多头注意力分支是回归稳定性的主要贡献者。本研究贡献了可重复使用的泄漏审核协议、种子透明评估，并证明特定任务的稳定性更多取决于标签来源而非任务类型。
英文摘要：
Multi-task deep learning models that jointly perform fault classification and remaining useful life (RUL) regression are increasingly used in predictive maintenance, yet reported performance can be strongly affected by how sliding-window sequences are split into training and test sets. We investigate this issue using AMTLNet, an attention-enhanced multi-task architecture, on three public benchmarks: NASA C-MAPSS, NASA IMS, and the UCI Hydraulic System dataset. We show that naive splitting can inflate classification accuracy from a genuine 20-60 percent to 99.9 percent, or reduce it to 0 percent through degenerate class representation. To address this, we introduce a chunk-based, leakage-audited splitting protocol and evaluate all models using five seeds, one-way ANOVA, and Tukey HSD tests. On C-MAPSS, with 19,976 leakage-free training windows, AMTLNet matches a single-task CNN-LSTM baseline in classification, achieving 84.12 +/- 0.96 percent accuracy with Tukey p = 1.0, and reaches an R2 of 0.86 +/- 0.01 while significantly outperforming a naive multi-task baseline. On the smaller Bearing and Hydraulic datasets, multi-task training is unstable, but the failure mode differs: classification degrades for Bearing, whereas regression degrades for Hydraulic. We relate this asymmetry to label provenance and propose a practical framework for deciding when joint training is appropriate under data scarcity. Ablation results show that the multi-head attention branch is the main contributor to regression stability. Removing it reduces R2 from 0.861 to 0.766 and more than doubles classification variance, whereas the convolutional branch contributes little to regression despite using about one-third of the parameters. This study contributes a reusable leakage-audit protocol, seed-transparent evaluation, and evidence that task-specific stability depends more on label provenance than on task type.
