Deep learning, with its powerful multilayer nonlinear representation of deep neural
networks, enables models trained based on deep learning to describe the true distribution of …

Currently, the fault diagnosis with balanced data and distinct characteristics has received
mass concern, and the related research achievements are remarkable. However, because …

A deep learning method for fault diagnosis is proposed in this paper. The stacked sparse
auto encoder (SSAE) model with the theory of deep learning extracts deep feature …

Those fault detection and diagnosis (FDD) models can identify various faulty signals in
industrial processes by extracting features from process data with high nonlinearity and …

With the increasing complexity of industrial processes, the high-dimensional industrial data
exhibit a strong nonlinearity, bringing considerable challenges to the fault diagnosis of …

Effective process monitoring and fault diagnosis are of great significance to the safe
operation of industrial processes as production scale increases and production systems …

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 …

Currently, fault diagnosis, especially single fault diagnosis, has yielded fruitful research
results. However, for concurrent faults, which exist more widely in real industrial systems …

In the recent years, deep learning has been widely used in process monitoring due to its
strong ability to extract features. However, with the increasing layers of the deep network, the …

Intelligent fault diagnosis method is an important tool for ensuring the stability of industrial
processes. However, in the actual industrial process, forming a fault diagnosis model with …