Physics-informed deep learning for signal compression and reconstruction of big data in industrial condition monitoring
Physics-informed deep learning for signal compression and reconstruction of big data in industrial condition monitoring
复制标题
DOI：
10.1016/j.ymssp.2021.108709
复制
发表时间：
2022
影响因子：
8.4
通讯作者：
Matthew Russell;Peng Wang
中科院分区：
文献类型：
--
作者：
Matthew Russell;Peng Wang
物联网的出现使机器能够配备始终在线的传感器，这些传感器可以为基于云的监测系统提供健康信息，以进行故障和健康管理（PHM），这大大提高了可靠性，避免了车间机器和流程的停机时间。另一方面，实时监控产生大量数据，导致高效和有效的数据传输（从车间到云端）和分析（在云端）面临重大挑战。受限于工业硬件能力，特别是互联网带宽，大多数解决方案从数据压缩（在传输之前，在本地计算设备处）结合数据重构（在传输之后，在云中）的角度来处理数据传输。然而，现有的数据压缩技术可能不适应数据的特定于域的特性，因此在解决高压缩比方面具有限制，其中信号细节的完全恢复对于揭示机器状况是重要的。这项研究将深度卷积自动编码器（DCAE）与局部结构和物理信息损失项集成在一起，这些损失项包含PHM领域知识，例如频率内容对机器故障诊断的重要性。此外，提出了故障分割自动编码器复用（FDAM）来减轻多个不相交的操作条件对重建保真度的负面影响。所提出的方法进行了评估的两个案例研究，和基于自相关的噪声分析提供了深入了解整个机器的健康和操作条件的相对性能。结果表明，物理通知DCAE压缩优于流行的数据压缩方法，如压缩传感，主成分分析（PCA），离散余弦变换（DCT），和DCAE与标准的损失函数。FDAM可以进一步提高某些机器条件下的数据重建质量。
The onset of the Internet of Things enables machines to be outfitted with always-on sensors that can provide health information to cloud-based monitoring systems for prognostics and health management (PHM), which greatly improves reliability and avoids downtime of machines and processes on the shop floor. On the other hand, real-time monitoring produces large amounts of data, leading to significant challenges for efficient and effective data transmission (from the shop floor to the cloud) and analysis (in the cloud). Restricted by industrial hardware capability, especially Internet bandwidth, most solutions approach data transmission from the perspective of data compression (before transmission, at local computing devices) coupled with data reconstruction (after transmission, in the cloud). However, existing data compression techniques may not adapt to domain-specific characteristics of data, and hence have limitations in addressing high compression ratios where full restoration of signal details is important for revealing machine conditions. This study integrates Deep Convolutional Autoencoders (DCAE) with local structure and physics-informed loss terms that incorporate PHM domain knowledge such as the importance of frequency content for machine fault diagnosis. Furthermore, Fault Division Autoencoder Multiplexing (FDAM) is proposed to mitigate the negative effects of multiple disjoint operating conditions on reconstruction fidelity. The proposed methods are evaluated on two case studies, and autocorrelation-based noise analysis provides insight into the relative performance across machine health and operating conditions. Results indicate that physically-informed DCAE compression outperforms prevalent data compression approaches, such as compressed sensing, Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), and DCAE with a standard loss function. FDAM can further improve the data reconstruction quality for certain machine conditions.