The massive high-dimensional measurements accumulated by distributed control systems
bring great computational and modeling complexity to the traditional fault diagnosis …

Fault diagnosis, which identifies the root cause of the observed out-of-control status, is
essential to counteracting or eliminating faults in industrial processes. Many conventional …

In the era of industrial big data, traditional shallow machine learning-based data analytical
technologies cannot handle complex industrial system fault detection issue effectively. In …

As representation scheme can severely limit the window by which the system observes its
world, deep learning for fault diagnosis is put forward in this paper. It is a real time online …

Fault diagnosis is critical in industrial systems since early detection of problems can not only
save valuable time but also reduce maintenance costs. The feature extraction process of …

In industrial systems, the vibration signals of rolling bearings are influenced by changing
operating conditions and strong environmental noise, therefore they are often characterized …

Fault identification for closed-loop control systems is a future trend in the field of fault
diagnosis. Due to the inherent feedback adjustment mechanism, a closed-loop control …

Data-driven fault diagnosis for dynamic process faces three challenges. Firstly, models are
hard to establish to describe the multivariate coupled correlations. Secondly, in an actual …

Fault detection and diagnosis have always been the key techniques for safe and reliable
operation of industrial processes. However, the high dimension and noise of process …

Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of
industrial equipment. In recent years, deep learning models have been widely used in data …