Generative adversarial wavelet neural operator: Application to fault
  detection and isolation of multivariate time series data
Jyoti Rani Tapas Tripura Hariprasad Kodamana Souvik Chakraborty
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.