A study is presented to compare the performance of gear fault detection using artificial
neural networks (ANNs) and support vector machines (SMVs). The time-domain vibration …

Improving the availability of wind turbines is critical for minimising the cost of wind energy,
especially offshore. The development of reliable and cost‐effective gearbox condition …

A multi-fault classification of gears has been attempted by support vector machine (SVM)
learning techniques with the help of time–frequency (wavelet) vibration data. A suitable …

The fault characteristics of the rolling bearings of wind turbine gearboxes are unstable under
actual operating conditions. Problems such as inadequate fault sample data, imbalanced …

Deep learning (DL) has been widely developed and applied in gearbox fault diagnosis of
smart manufacturing systems due to its powerful feature representation ability. However …

Early detection of induction motor faults has been a main subject of investigation for many
years. Several approaches have been proposed for identifying one or more faults treated in …

As one of the most immensely growing renewable energies, the wind power industry also
experiences a high failure rate and operation & maintenance cost. Therefore, the condition …

A hybrid model with multiple sub-classifier is a powerful classifier for intelligent fault
diagnosis of gearboxes. Each sub-classifier is used to deal with different gear or bearing …

Performing condition monitoring on rotating machines which operate under fluctuating
conditions remains a challenge, with robust diagnostic techniques being required for the …

Signals captured in rotating machines to obtain the status of their components can be
considered as a source of massive information. In current methods based on artificial …