A concise survey of the literature on cyclostationarity of the last 10 years is presented and an
extensive bibliography included. The problems of statistical function estimation, signal …

This paper deals with the state-of-the-art strategies and techniques based on vibro-acoustic
signals that can monitor and diagnose malfunctions in Internal Combustion Engines (ICEs) …

Blind deconvolution algorithms prove to be effective tools for fault identification, being able
to extract excitation sources from noisy observations only. In this scenario, the present paper …

Recent studies in the field of diagnostics and prognostics of machines have highlighted the
key role played by non-stationarity–often in the form of cyclostationarity–or non-Gaussianity …

Blind deconvolution (BD) methods can extract fault signatures from noisy observations.
Among all the BD methods, maximum second-order cyclostationarity blind deconvolution …

Owing to the rotation and reciprocation of mechanical equipment, their vibration signals
inherently exhibit cyclic stationary characteristics. This paper provides a phenomenological …

Neural networks (NN) have generated extensive interest in the field of machine condition
monitoring (MCM). Many applications are however adapting structures and approaches from …

Blind deconvolution (BD) is an effective technology for rotating machinery fault detection
because it significantly weakens noise and reduces the interference of the system …

The number of research papers dealing with vibration-based condition monitoring has been
exponentially growing in recent decades. As a consequence, one may identify some trends …

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 …