The tremendous success of deep learning and transfer learning broadened the scope of
fault diagnosis, especially the latter further improved the diagnosis accuracy under multiple …

Deep transfer learning provides a feasible fault diagnosis method for intelligent mechanical
systems. However, this method usually assumes that the source domain and the target …

In bearing fault diagnosis, multichannel data can contain more abundant and complete fault
information to alleviate the influence of accidental factors in a single channel. To fully …

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 imbalanced dataset in actual engineering negatively affects the precision of fault
diagnosis because of the severe lack of collected fault data. To effectively address this issue …

Two-stage prediction methods based on Wiener processes are widely used to describe the
degradation process of components. However, a single type of drift function cannot …

The single sensor is difficult to acquire the complete degradation information of the
component, and the degradation model cannot adaptively track the staged degradation …

When a stacked denoising auto-encoder (SDAE) manually sets several parameters, the
gradient of neuron weight becomes dispersed, reducing the ability to retrieve sensitive fault …

Transfer learning is an excellent approach to deal with the problem that the target domain
label can not be adequately obtained when rolling bearing cross-condition fault detection. A …

To improve the accuracy of bearing fault diagnosis in a multisensor monitoring environment,
it is necessary to obtain more accurate and effective fault classification features for bearings …