Extracting the failure related information from vibration signals is a very important aspect of
vibration-based fault detection for rolling bearing Multiscale entropy and its improvement …

Condition monitoring and fault diagnosis of wind turbines is an attractive yet challenging
task. This paper presents a novel data-driven fault diagnosis scheme for wind turbines …

Bearing vibration signals typically have nonlinear components due to their interaction and
coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal …

Greater difference in data distribution of rotating machinery under multiple operating
conditions, which increases the difficulty of fault diagnosis. To solve this problem, a multi …

In the signal analysis context, the entropy concept can characterize signal properties for
detecting anomalies or non-representative behaviors in fiscal systems. In motor fault …

In order to extract the non-linear fault characteristics of rolling bearings more accurately, a
novel nonlinear dynamical analysis method, referred to as the refined composite multi-scale …

To achieve better fault diagnosis of rotating machinery, this paper presents a novel
intelligent fault diagnosis model based on singular value manifold features (SVMF) …

For cross-domain fault diagnosis of rolling bearing, the method of how to find and select
similar features between the source and target domains is still a key problem. Toward this …

Fault diagnosis of critical rotating machinery components is necessary to ensure safe
operation. However, the commonly used rotating machinery fault diagnosis methods are …

The multiscale fluctuation dispersion entropy algorithm (MFDE) is widely used to extract the
characteristics from a variety of complex nonlinear signals, including bearing signals, due to …