针对多工况故障诊断中数据分布差异大的难题，提出注意力多尺度时序融合网络。
Attention-Based Multiscale Temporal Fusion Network for Uncertain-Mode Fault Diagnosis in Multimode Processes
- 用多尺度卷积与门控循环单元提取局部与长短期特征
- 通过实例归一化抑制工况特有信息，提升共享特征表达
- 设计时序注意力机制聚焦关键时间点，适合工业系统故障诊断
多工况过程中的故障诊断对保障工业系统安全运行至关重要。其核心挑战在于不同工况下的监测数据分布差异显著，导致模型难以提取与系统健康状态相关的共享特征表示。为此，本文提出一种新型方法——基于注意力的多尺度时序融合网络（AMTFNet）。该方法采用多尺度深度卷积与门控循环单元，分别提取多尺度上下文局部特征和长短时序特征；通过实例归一化抑制工况特异性信息；并设计时序注意力机制，聚焦具有更高跨工况共享信息的关键时间点，从而提升故障诊断精度。模型在Tennessee Eastman过程数据集和三相流装置数据集上进行验证，实验表明其诊断性能优异且模型规模小。源代码将发布于GitHub：https://github.com/GuangqiangLi/AMTFNet。
原文摘要 · Abstract (English)
Fault diagnosis in multimode processes plays a critical role in ensuring the safe operation of industrial systems across multiple modes. It faces a great challenge yet to be addressed - that is, the significant distributional differences among monitoring data from multiple modes make it difficult for the models to extract shared feature representations related to system health conditions. In response to this problem, this paper introduces a novel method called attention-based multiscale temporal fusion network. The multiscale depthwise convolution and gated recurrent unit are employed to extract multiscale contextual local features and long-short-term features. Instance normalization is applied to suppress mode-specific information. Furthermore, a temporal attention mechanism is designed to focus on critical time points with higher cross-mode shared information, thereby enhancing the accuracy of fault diagnosis. The proposed model is applied to Tennessee Eastman process dataset and three-phase flow facility dataset. The experiments demonstrate that the proposed model achieves superior diagnostic performance and maintains a small model size. The source code will be available on GitHub at https://github.com/GuangqiangLi/AMTFNet.
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