Accurate prediction of wind speed can not only help to develop strategies for wind turbine
operation, but also reduce impact on power systems when wind energy is integrated into the …

The presence of incorrect data leads to the decrease of condition-monitoring big data quality.
As a result, unreliable or misleading results are probably obtained by analyzing these poor-…

Accurate wind speed forecasting plays an essential role in scheduling wind power generation.
Currently, most existing models predict wind speed just based on temporal features and …

The presence of repetitive transients in vibration signals is a typical symptom of local faults
of rotating machinery. Infogram was developed to extract the repetitive transients from …

Fault diagnosis of wind turbine drivetrains is vital to maintain the reliability of wind turbines
and stochastic resonance (SR) is regarded as a powerful method to amplify the fault-induced …

Due to often working in the environment of variable speeds and loads, it is an enormous
challenge to achieve high-accuracy fault diagnosis (FD) of rolling bearings (RB) via existing …

Most traditional overdamped monostable, bistable and even tristable stochastic resonance (SR)
methods have three shortcomings in weak characteristic extraction: (1) their potential …

Traditional deep learning methods rely on big data heavily, which makes bearing fault
diagnosis with small samples under variable working conditions a tricky problem. The extremely …

Machinery generally operates under severe and complex conditions, and therefore, the
monitoring signals acquired from machinery would inevitably be accompanied by various types …

<p>China is a particularly critical country for global tobacco control. It has the world's largest
number of smokers and is a prize target for the multinational tobacco companies. This article …