机械多模态时间序列中基于自监督跨模态重构的鲁棒故障检测
Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction
- Aalborg University（奥尔堡大学）
- EPFL（洛桑联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对多模态时间序列故障检测中模态独立处理和分布偏移问题，提出基于跨模态重构的自监督框架，利用重构误差和自适应阈值，在三个工业案例中显著提升分布外鲁棒性。
AI中文摘要：
故障检测在工业系统中至关重要，它能够早期识别异常行为，从而提高安全性、可靠性和运行效率。现代系统日益依赖异构传感模态，这些模态捕获底层物理过程的互补方面。然而，现有的基于数据驱动的异常检测方法通常独立处理每种模态，或使用简单的特征级融合，限制了它们利用表征正常系统行为的跨模态关系的能力。此外，这些方法的性能通常假设训练和部署分布相似，而实际运行受到变化的工况、环境影响和系统退化等因素的影响，这些因素会引起分布偏移并降低检测性能，尤其是在未见过的工况下。在本工作中，我们提出了一种基于异构时间序列传感器数据跨模态重构的多模态异常检测框架。该框架不是独立建模每种模态，而是通过从其他模态重构每种模态来学习系统动力学，从而利用跨模态的互补信息。这在不要求显式时间对齐或相同采样率的情况下整合了跨传感通道的信息，同时提高了对传感器噪声、缺失测量和特定模态干扰的鲁棒性。为了解决实际部署中的分布偏移问题，我们使用跨模态重构误差和一种自适应测试时阈值机制来识别异常，该机制能够适应变化的工况。在三个工业案例研究上的实验表明，该方法具有强大的故障检测性能，并且在分布外条件下显著提高了鲁棒性，其中在最具挑战性的运行工况下取得了最大的改进。
英文摘要：
Fault detection is essential in industrial systems, enabling early identification of abnormal behaviour and improving safety, reliability, and operational efficiency. Modern systems increasingly rely on heterogeneous sensing modalities that capture complementary aspects of the underlying physical process. However, existing data-driven anomaly detection methods often process each modality independently or use simple feature-level fusion, limiting their ability to exploit cross-modal relationships that characterize normal system behaviour. Their performance also commonly assumes similar training and deployment distributions, whereas real-world operation is affected by changing operating conditions, environmental influences, and system degradation that induce distribution shifts and reduce detection performance, especially in unseen regimes. In this work, we propose a multimodal anomaly detection framework based on cross-modal reconstruction of heterogeneous time-series sensor data. Rather than modeling each modality independently, the framework learns system dynamics by reconstructing each modality from the others, thereby exploiting complementary information across modalities. This integrates information across sensing channels without requiring explicit temporal alignment or identical sampling rates, while improving robustness to sensor noise, missing measurements, and modality-specific disturbances. To address distribution shifts during real-world deployment, anomalies are identified using cross-modal reconstruction error and an adaptive test-time thresholding mechanism that adjusts to changing operating conditions. Experiments on three industrial case studies show strong fault detection performance and substantially improved robustness under out-of-distribution conditions, with the largest gains observed in the most challenging operating regimes.
