Rolling-element bearing vibrations are random cyclostationary, that is they exhibit a
cyclical behaviour of their statistical properties while the machine is operating. This property is so
symptomatic when an incipient fault develops that it can be efficiently exploited for diagnostics.
This paper gives a synthetic but comprehensive discussion about this issue. First, the
cyclostationarity of bearing signals is proved from a simple phenomenological model. Once this
property is established, the question is then addressed of which spectral quantity can adequately
characterise such vibration signals. In this respect, the cyclic coherence - and its multi-dimensional
extension in the case of multi-sensors measurements -- is shown to be twice optimal: first to
evidence the presence of a fault in high levels of background noise, and second to return a relative
measure of its severity. These advantages make it an appealing candidate to be used in adverse
industrial environments. The use and interpretation of the proposed tool are then illustrated on
actual industrial measurements, and a special attention is paid to describe the typical "cyclic spectral
signatures" of inner race, outer race, and rolling-element faults.
Identification of fault size of rolling element bearing on outer race from vibration signature analysis
