This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …

In industrial systems, continuous monitoring of process conditions is crucial. Existing
discriminant analysis methods, including quadratic discriminant analysis (QDA), Fisher …

Industrial processes usually exhibit great nonlinearity generated from the effects of complex
mechanisms, system integrations and multiple working conditions. Although a variety of …

The safe operation of the fluid machine is greatly affected by fault states. With the
development of data collection technology in process industrial systems, data-based …

In large-scale industrial fault detection, a distributed model is typically established on the
basis of blocked units. However, blocked distributed methods consider units as independent …

One challenge faced by data-driven fault diagnosis methods is that they may perform well
over the operating modes where the historical data are collected, but fail to generalize to …

With the scale expansion of industrial processes, safety has become one of its important
links and the requirements for safety monitoring are getting higher. How to realize timely and …

Fault diagnosis (FD) is crucial for ensuring the safety and stability of industrial processes. In
real industrial processes, fault features in measurement data are prone to be misidentified …

A novel framework for fault diagnosis is proposed. New rules are presented to enhance
decision making under a probabilistic latent model. The proposed decision rules improve …

Hand trajectories are widely used for gesture recognition, action analysis, and sign
language translation. Effective hand trajectory feature extraction facilitates accurate and fast …