The operating state of insulators is directly related to the stability of power transmission line.
The existing methods for insulator state recognition cannot achieve satisfactory …

Accurate prediction of combustion state is crucial for an in-depth understanding of furnace
performance and optimize operation conditions. Traditional data-driven approaches such as …

The sintering temperature (ST) is a critical index for condition monitoring and process control
of coal-fired equipment and is widely used in the production of cement, aluminum, electricity …

Industrial data contain a lot of noisy information, which cannot be well suppressed in deep
learning models. The current industrial data classification models are problematic in terms of …

Effective and efficient monitoring of furnace combustion state and measurement of heat
release rate are important and pressing problems in the power industry. However, traditional …

Combustion condition monitoring is a fundamental and critical issue that needs to be
addressed in the wide-load operation of coal-fired boilers. In this paper, an unsupervised …

This work investigates the usage of deep neural networks for predicting the thermal output of
a 3 MW, grate-fired biomass boiler, based on routinely measured operating parameters and …

Maintaining a normal burning temperature is essential to ensuring the quality of nonferrous
metals and cement clinker in a rotary kiln. Recognition of the temperature condition is an …

The self-blast state of a glass insulator directly affects the safety and reliability of
transmission lines. To address the insufficient generalization ability of existing detection …

In the coal-fire industry, recognition of burning condition is vital for combustion control and
optimization as it can provide early warning of abnormal conditions in the combustion …