Various microreactor designs under development aim at fulfilling electricity and heat
production requirements affordably and reliably in a variety of applications, like power …

Rotating machinery is the essential component in nuclear power plants (NPPs). Effective
fault detection and diagnosis is a main challenge in the operation and maintenance of NPPs …

Bearing fault diagnosis is of great importance to decrease the damage risk of rotating
machines and further improve economic profits. Recently, machine learning, represented by …

With the widespread application of deep learning in Internet of Things (IoT), remarkable
achievements have been made especially in rolling bearing fault diagnosis in rotating …

Various advanced reactor designs proposed in recent years envision deployment scenarios
which feature reactor operations with significantly reduced staffing or even completely …

The sensor signals collected by the nuclear reactor thermal hydraulic experimental system
are mixed with complex noise information. The uncertainty of sensor data directly affects the …

Vibration signals play a crucial role in mechanical fault diagnosis. However, they are
susceptible to various noise disturbances, presenting challenges for reliable fault detection …

Industrial robots are complex and critical equipment in intelligent manufacturing systems that
require ensuring safe and stable operation. The in situ fault diagnosis of industrial robots …

Introduction: This paper proposes a five-layer fully connected neural network for predicting
radiation parameters in a radiation space based on detector readings. Methods: The …

This study proposes a source distribution inversion convolutional neural network (SDICNN),
which is deep neural network model for the inversion of complex source distributions, to …