The proposed transfer learning-based fault diagnosis models have achieved good results in
multi-source domain generalization (MDG) tasks. However, research on single-source …

As the production demand and external environment change, the same production process
may have multiple stable working conditions, ie, multimode process. The traditional process …

Abstract Domain adaptation effectively addresses the issue of varying vibration data
conditions in fault diagnosis. However, current domain adaptation methods rarely consider …

In a rotating machinery system, a single fault of one component often causes damage to
other related components, thus inducing compound faults. Without compound fault data to …

Oil–gas–water three-phase flow has multiple flow states, exhibiting dynamic, nonlinear, and
instantaneous behaviors. Monitoring and analysis of flow state are crucial for ensuring safe …

The existing fault diagnosis methods based on deep transfer learning achieve domain
adaptation by matching shared features between the source and target domains. However …

In recent years, significant progress has been achieved in the intelligent fault diagnosis of
bearings based on transfer learning. However, existing methods overlook the presence of …

Background In response to the challenge that traditional fault diagnosis models are difficult
to maintain satisfactory accuracy when data distribution changes due to changes in process …

Considering that in the fault diagnosis of bearing based on relation network, using the
sample mean value as the class prototype for each class is susceptible to outliers, resulting …

Loads and strains in critical areas play a crucial role in aircraft structural health monitoring,
the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct …