Variance-capturing forward-forward autoencoder (VFFAE): A forward learning neural network for fault detection and isolation of process data
Data-driven models have emerged as popular choices for fault detection and isolation (FDI)
in process industries. However, real-time updating of these models due to streaming data …
in process industries. However, real-time updating of these models due to streaming data …
[HTML][HTML] A graph embedding based fault detection framework for process systems with multi-variate time-series datasets
Due to the enormous potential of modelling, graph-based approaches have been used for
various applications in the process industries. In this study, we propose a fault detection …
various applications in the process industries. In this study, we propose a fault detection …
Reconstruction error‐based fault detection of time series process data using generative adversarial auto‐encoders
Faults in time series process data are typically difficult to detect due to the complex temporal
correlations of data samples. In this context, traditional unsupervised machine learning …
correlations of data samples. In this context, traditional unsupervised machine learning …
Generative adversarial wavelet neural operator: Application to fault detection and isolation of multivariate time series data
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 …
operation. However, traditional fault detection methods often struggle with issues such as …
Fault detection using Graph Neural Differential Auto-encoders (GNDAE)
In this study, we propose a Graph neural Differential Auto-encoder (GNDAE) model for fault
detection and process monitoring. The GNDAE framework is capable of dealing with graph …
detection and process monitoring. The GNDAE framework is capable of dealing with graph …