The sintering process, as a primary modus of the blast furnace ironmaking industry, has
enormous economic value and environmental protection significance for the iron and steel …

The blast furnace is a highly energy-intensive, highly polluting, and extremely complex
reactor in the ironmaking process. Soft sensors are a key technology for predicting molten …

An imbalanced number of faulty and normal samples causes serious damage to the
performance of the conventional diagnosis methods. To settle the data-imbalance fault …

Data-driven soft sensing modeling is becoming a powerful tool in the ironmaking process
due to the rapid development of machine learning and data mining. Although various soft …

Nowadays, data-driven soft sensors have become mainstream for the key performance
indicators prediction, which guarantees the safety and stability of the industrial process. The …

In modern industrial systems, diagnosing faults in time and using the best methods becomes
increasingly crucial. It is possible to fail a system or to waste resources if faults are not …

Fault classification plays a crucial role in the industrial process monitoring domain. In the
datasets collected from real-life industrial processes, the data distribution is usually …

With the advent of the big data era and the timeliness requirements of data processing, a
large amount of streaming industrial big data is continuously obtained in real time. Facing …

In the wake of the era of big data, the techniques of deep learning have become an essential
research direction in the machine learning field and are beginning to be applied in the steel …

Data‐driven soft sensing approaches have been a hot research field for decades and are
increasingly used in industrial processes due to their advantages of easy implementation …