The impulse signal in large rotating machinery with damage fault is sparse, weak, coupled,
and even nonperiodic in intermittent operation. To extract this complex signal is a key topic …

In order to extract fault impulse feature of large-scale rotating machinery from strong
background noise, a sparse feature extraction method based on sparse decomposition …

In recent years, singular spectrum decomposition (SSD) has been recognized as a powerful
signal decomposition technique and has become a useful tool for fault signature extraction …

The fault diagnosis of rotating machinery is quite important for the security and reliability of
the overall mechanical equipment. As the main components in rotating machinery, the gear …

Fault diagnosis under time-varying operational conditions is always the challenge in
industrial systems. The chirplet transform (CT) provides an effective first-order approximation …

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis
has drawn considerable attention. In terms of the rich information hidden in fault vibration …

Rolling element bearing and gear are the typical supporting or rotating parts in mechanical
equipment, and it has important economy and security to realize their quick and accurate …

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 …

In order to diagnose an incipient fault in rotating machinery under complicated conditions, a
fast sparse decomposition based on the Teager energy operator (TEO) is proposed in this …

Group-sparse mode decomposition (GSMD) is an efficient signal decomposition algorithm
for separating harmonic signals, but fails to split modes for periodic pulse signals. In order to …