With the development of intelligent manufacturing and automation, the precision and
complexity of mechanical equipment are increasing, which leads to a higher requirement for …

Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …

Gearbox fault diagnosis is expected to significantly improve the reliability, safety and
efficiency of power transmission systems. However, planetary gearbox fault diagnosis …

Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of
practical importance. For this purpose, ensemble transfer convolutional neural networks …

Deep learning has evolved to a prevalent approach for machinery fault diagnosis in recent
years. However, the high demanding for training data amount refrains its implementation. In …

The dependence on big data and lengthy training time discount the advantages of deep
learning methods applied in machinery fault diagnosis. Moreover, the performance of deep …

Condition monitoring (CM) of mechanical systems such as gear transmissions can be
performed with vibration measurements and processing of the recorded signals for …

Soil organic carbon (SOC) plays an important role in soil functioning and also global C
balance. Visible-near-infrared (Vis-NIR) spectroscopy can be regarded as a cost-effective …

Features describing the state of industrial gearboxes and their extraction from the mixed
noisy signal are always an issue of concern. Unfortunately, traditional feature extraction …

Unsupervised domain adaptation (DA) provides a promising approach for tackling fault
diagnosis tasks of target datasets without labeled data and has been actively studied in …