Computer applications have considerably shifted from single data processing to machine
learning in recent years due to the accessibility and availability of massive volumes of data …

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of
machinery. The majority of these machines comprise rotating components and are called …

Learning information from a single or a few samples is called few-shot learning. This
learning method will solve deep learning's dependence on a large sample. Deep learning …

The chiller fault diagnosis is of great significance to maintain the normal operation of the
HVAC system and indoor comfort. Due to the difficulty in collecting the chiller's fault data, we …

The advent of 3D printing has transformed manufacturing. However, extending the library of
materials to improve 3D printing quality remains a challenge. Defects can occur when …

Abstract Industry 4.0 denotes smart manufacturing, where rotating machines predominantly
serve as the fundamental components in production sectors. The primary duty of …

The arc-fault phenomenon in photovoltaic (PV) systems has emerged as a major problem in
recent years. Existing studies on arc-fault detection in conventional PV systems primarily …

To address the problem of the low recognition rate of time-frequency domain methods
gearbox fault identification, a method featuring decision-level fusion of DS evidence theory …

In this work, the feasibility of applying a digital twin combined with machine learning
algorithms (convolutional neural network and random forest classifier) to predict the …

Deep learning is widely adopted in the field of fault diagnosis because of its powerful feature
representation capabilities. The existing diagnosis methods are always proposed based on …