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.

提出了一种新的无监督深度学习方法，即生成对抗小波神经算子（GAWNO），用于多元时间序列过程的故障检测和隔离。该方法结合了小波神经算子和生成对抗网络（GANs）的优势，有效地捕捉底层系统中不同变量的时间分布和空间依赖关系，并通过基于重构误差的阈值方法来检测和隔离故障。通过在正常操作条件下的数据集上训练 GAWNO，并使用 Tennessee Eastman Process（TEP）数据集、Avedore 污水处理厂（WWTP）和 WWTPN2O 数据集验证了所提出的方法，结果显示相比文献中各种建立良好基线，将小波分析、神经算子和生成模型结合到一个框架中以检测和隔离故障的想法得到了有希望的结果。