Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
This addresses fault detection for multivariate time series in industrial processes, offering an incremental improvement by integrating existing techniques into a novel framework.
The paper tackles fault detection and isolation in complex systems by proposing a generative adversarial wavelet neural operator (GAWNO), an unsupervised deep learning approach that combines wavelet neural operators and GANs to capture temporal and spatial dependencies, achieving promising results on datasets like the Tennessee Eastman Process and wastewater treatment plant data compared to established baselines.
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.