The advancement of artificial intelligence algorithms has gained growing interest in
identifying the fault types in rotary machines, which is a high-efficiency but not a human-like …

Complex dynamic characteristics resulting from multi-system coupling and closed-loop
control are ubiquitous in modern industrial process data, presenting significant challenges …

The fusion of industrial artificial intelligence with the Industrial Internet of Things (IIoT) can
attain a heightened level of process monitoring in modern manufacturing processes. In …

Since the geological drilling process involves numerous variables and the relationships
between them are also complex, it is not easy to implement an accurate description of …

Common multivariate statistical quality-related process monitoring methods often separate
feature extraction from quality-related process modeling, which can lead to insufficient …

The severity of faults and the corresponding solutions in the complex and large-scale
modern manufacturing industry are determined based on their impact on product quality …

With the complexity and intelligence of the process, process monitoring plays a vital role in
ensuring production safety and product quality, in which quality-related fault detection …

The historical data collected from industrial processes are generally disturbed by ambient
noise and outliers. Hence, accurate estimation of process uncertainty is essential in order to …

Fault diagnosis plays a pivotal role in identifying the root causes of a fault. Current fault
diagnosis methods encounter the shortcomings being unable to assess the fault amplitude …

With increasing complexity of industrial processes, a number of variables are becoming
increasingly large in modeling and monitoring steps, which is particularly prominent in …