As a multi-resolution analysis method rooted rigorously in mathematics, wavelet transform
(WT) has shown its great potential in rotary machine fault diagnosis, characterized by …

Collaborative fault diagnosis has become a hot research topic in fault detection and
identification, greatly benefiting from emerging multisensory fusion techniques and newly …

Recently proposed variational signal decomposition methods like adaptive chirp mode
decomposition (ACMD) and generalized dispersive mode decomposition (GDMD) have …

Rolling bearings are the core components of rotating machinery, and their normal operation
is crucial to entire industrial applications. Most existing condition monitoring methods have …

The extraction of fault resonance bands from a full frequency band has always stood as a
classical and effective strategy for fault diagnosis in rotating machinery. Among the existing …

In recent years, the rolling bearing fault diagnosis technique based on deep learning (DL)
provides a more intelligent and reliable way for the safe operation of mechanical systems …

Since the cyclostationarity in vibration signals is the key to judge the rotating machine health
state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single …

Rotating machinery typically consists of multiple rotating components, and its fault signals
contain not only periodic impulse components caused by local defects but also periodic …

Resonance demodulation of vibration signals is an essential strategy for rolling bearing fault
diagnosis. However, most resonance demodulation methods focus on searching for only …

Informative frequency band identification methods are used to automatically design
bandpass filters to enhance fault signatures in vibration measurements. Blind and targeted …