As the key rotating parts in machinery, it is crucial to extract the latent fault features of rolling
bearing in machinery condition monitoring to avoid the occurrence of sudden accidents …

Sparse decomposition is a novel method for the fault diagnosis of rolling element bearing,
whether the construction of dictionary model is good or not will directly affect the results of …

In the early fault diagnosis of rolling bearing, the vibration signal is mixed with a lot of noise,
resulting in the difficulties in analysis of early weak fault signal. This article introduces …

The rolling element bearings used in rotating machinery generally include multiple
coexisting defects. However, individual defect–induced signals of bearings simultaneously …

The feature of rolling element bearings' multi-type faults is very hard to extract using
common feature extraction method such as envelope demodulation, and the main reason is …

The existence of periodic impacts in collected vibration signal is the representative symptom
of rolling bearing localized defect. Due to the complicacy of the working condition, the fault …

Singular value decomposition (SVD) is widely used in condition monitoring of modern
machine for its unique advantages. A novel relative change rate of singular value kurtosis …

The rolling bearing fault signal under strong background noise is very weak because of
environmental noise impaction and the attenuation of signal. The feature extraction of rolling …

Aiming at the problem that the vibration signals of rolling bearings working in a harsh
environment are mixed with many harmonic components and noise signals, while the …

It is significant to detect the fault type and assess the fault level as early as possible for
avoiding catastrophic accidents. In the early fault diagnosis of rolling bearing, the vibration …