Rotating machinery often works under time-varying speeds, and nonstationary conditions
and harsh environments make its key parts, such as rolling bearings and gears, prone to …

Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …

Rotating machinery is widely used in the industry. They are vulnerable to many kinds of
damages especially for those working under tough and time-varying operation conditions …

In recent years, deep learning has been proved to be a promising bearing fault diagnosis
technology. However, most of the existing methods are based on single-task learning. Fault …

Due to the complex and rugged working environment of real machinery equipment, the
resulting fault information is easily submerged by severe noise interference. Additionally …

The important foundation for water resource management and utilization is effective monthly
runoff prediction. In this study, a new coupled model for predicting monthly runoff is …

When the rolling bearing fails, the fault features contained in bearing vibration signal are
easily submerged by fortissimo noise interference signals, and have obvious non-stationary …

Vibration signals from rotating machineries are usually of multi-component and modulated
signals. Hilbert–Huang transform (HHT), hereby referring to the combination of empirical …

Feature extraction is an essential process in the intelligent fault diagnosis of rotating
machinery. Although existing feature extraction methods can obtain representative features …

Data-intelligent algorithms tailored for short-term energy forecasting can generate
meaningful information on the future variability of solar energy developments. Traditional …