Background In response to the challenge that traditional fault diagnosis models are difficult
to maintain satisfactory accuracy when data distribution changes due to changes in process …

Fault detection has great significance for chemical process safety with the development of
science and technology. The conventional echo state network‐based fault detection method …

As one classical anomaly detection technology, support vector data description (SVDD) has
been successfully applied to nonlinear chemical process monitoring. However, the basic …

With the rapid development of sensor and computer technology, deep learning has received
extensive attention in the field of fault detection with powerful nonlinear feature extraction …

With the progress of technology, modern industrial monitoring data not only includes
traditional process data but also includes video data. In order to make full use of these multi …

The use of deep learning methods for fault detection and diagnosis (FDD) has continued to
gain research attention due to the continuous availability of data and the need for reliable …

In recent times, there has been a rising preference for employing deep learning models in
intelligent chemical process fault diagnosis. However, a considerable portion of the …

Process fault is one of the main reasons that a system may appear unreliable, and it affects
the safety of a system. The existence of different degrees of noise in the industry also makes …

Fault diagnostic methods based on deep learning for industrial processes are becoming a
research hotspot. Most existing methods focus on algorithmic improvements and attempt to …

When new faults are identified in complex industrial processes, the model parameters in
neural networks can be incrementally updated to adapt to new diagnosis tasks. However …