Aiming at the application of deep learning in fault diagnosis, mechanical rotating equipment
components are prone to failure under complex working environment, and the industrial big …

Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical
equipment. With the rapid development of deep learning technology, the methods based on …

Deep learning methods based on vibration signals of rotating machinery have been
continuously developed in fault diagnosis. However, there are still three challenges in …

Bearing vibration signals typically have nonlinear components due to their interaction and
coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal …

Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness,
damping, friction, coupling effects, and defects. Consequently, their vibration signals display …

Human life has become smarter by utilizing big data, telecommunication technologies, and
wearable sensors over pervasive computing to give better healthcare services. Big data is …

Deep learning-related technologies have achieved remarkable success in the field of
intelligent fault diagnosis. Nevertheless, the traditional intelligent diagnosis methods are …

In light of the inadequacies of traditional P-wave arrival picking algorithms using long and
short windows, which exhibit poor anti-noise ability and do not meet the requirements for …

Software-defined network (SDN) is a new network structure, which has the characteristics of
centralized management and programmable, and is widely used in the field of Internet of …

With the rapid development of smart ships, the ship maintenance model is also changing. In
order to extract the fault characteristics of diesel engine thermal parameters more easily …