Fault detection and isolation in complex systems are critical to ensure reliable and efficient operation. However, traditional fault detection methods often struggle with issues such as nonlinearity and multivariate characteristics of the time series variables. This article proposes a generative adversarial wavelet neural operator (GAWNO) as a novel unsupervised deep learning approach for fault detection and isolation of multivariate time series processes.The GAWNO combines the strengths of wavelet neural operators and generative adversarial networks (GANs) to effectively capture both the temporal distributions and the spatial dependencies among different variables of an underlying system. The approach of fault detection and isolation using GAWNO consists of two main stages. In the first stage, the GAWNO is trained on a dataset of normal operating conditions to learn the underlying data distribution. In the second stage, a reconstruction error-based threshold approach using the trained GAWNO is employed to detect and isolate faults based on the discrepancy values. We validate the proposed approach using the Tennessee Eastman Process (TEP) dataset and Avedore wastewater treatment plant (WWTP) and N2O emissions named as WWTPN2O datasets. Overall, we showcase that the idea of harnessing the power of wavelet analysis, neural operators, and generative models in a single framework to detect and isolate faults has shown promising results compared to various well-established baselines in the literature.

翻译：复杂系统的故障检测与隔离对于确保其可靠高效运行至关重要。然而，传统故障检测方法常面临时间序列变量非线性与多元特性的挑战。本文提出一种生成对抗小波神经算子（GAWNO），作为一种新颖的无监督深度学习方法，用于多元时间序列过程的故障检测与隔离。GAWNO融合了小波神经算子与生成对抗网络（GANs）的优势，能够有效捕捉底层系统中不同变量间的时间分布特征与空间依赖关系。基于GAWNO的故障检测与隔离方法包含两个主要阶段：第一阶段，在正常运行工况数据集上训练GAWNO以学习底层数据分布；第二阶段，利用训练好的GAWNO构建基于重构误差的阈值方法，通过偏差值实现故障检测与隔离。我们采用田纳西-伊士曼过程（TEP）数据集、Avedøre污水处理厂（WWTP）数据集及名为WWTPN2O的N2O排放数据集对所述方法进行验证。总体而言，我们展示了将小波分析、神经算子与生成模型融合于单一框架以检测和隔离故障的思路，相较于文献中多种成熟基线方法展现出更优的性能。