Early fault detection, both in equipment and the products in process, is of paramount
importance in industrial processes to ensure the quality of the final product, avoid abnormal …

Discovery of new materials is increased after the introduction of high accuracy machine
learning techniques in the field of material science. Traditional way of discovering new …

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses
massive volumes of unlabeled data to train neural networks. SSL techniques have evolved …

Distributed anode current (DAC) is a high-dimensional spatial-distributed signal that can be
measured online in the industrial aluminum electrolysis process. The difference of …

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 …

The working condition evaluation of the aluminum reduction cell is the basis of formulating
operation strategy, ensuring production safety and realizing stable and optimized operation …

Convolutional neural networks (CNNs) are considered black boxes due to their robust
nonlinear fitting capability. In the context of fault diagnosis for rotating machinery, it may …

Rolling bearings are the core components of mechanical and electrical systems. A practical
fault diagnosis scheme is the key to ensure operational safety. There are excessive …

Superheat degree identification of aluminum electrolysis cell is a typical knowledge-
intensive work which is the precondition of efficient production. However, existing methods …

The dynamic correlation analysis of cell-spatial information (distributed anode current signal,
DACS) is of great significance in the regional-refined control of industrial aluminum …