The performance of diagnosis model highly depends on the amount and quality of data used
in training. However, it is difficult to collect sufficient fault data in practical applications …

Industrial separators play a pivotal role in production processes of various sectors such as
chemical, pharmaceutical, biotechnology, oil extraction and food industries, with over 3000 …

Sparse & noisy monitoring data leads to numerous challenges in prognostic and health
management (PHM). Big data volume but poor quality with scarce healthy states information …

Train axle bearing faults pose safety risks if unaddressed, but acquiring sufficient labeled
fault samples for training models is challenging due to high data collection costs and risks …

With the rapid development of industrial applications using permanent magnet synchronous
motors (PMSMs) and the Internet of Things, the demand for using robust fault diagnosis …

Recent refinements in Industry 4.0 and Machine Learning demonstrate the positive effects of
using deep learning models for intelligent maintenance. The primary benefit of Deep …

Permanent magnet synchronous motors (PMSMs) have played a key role in commercial and
industrial applications, ie electric vehicles and wind turbines. They are popular due to their …

Incorporating domain-specific prior knowledge within neural networks to enhance features
has shown to improve the model's classification performance. However, existing …