Abstract: In rotating machinery one of the critical components
that is prone to premature failure is the rolling bearing.
Consequently, early warning of an imminent bearing failure is much
critical to the safety and reliability of any high speed rotating
machines. This study is concerned with the application of Recurrence
Quantification Analysis (RQA) in fault detection of rolling element
bearings in rotating machinery. Based on the results from this study it
is reported that the RQA variable, percent determinism, is sensitive
to the type of fault investigated and therefore can provide useful
information on bearing damage in rolling element bearings.
Abstract: It is sometimes difficult to differentiate between
innocent murmurs and pathological murmurs during auscultation. In
these difficult cases, an intelligent stethoscope with decision support
abilities would be of great value. In this study, using a dog model,
phonocardiographic recordings were obtained from 27 boxer dogs
with various degrees of aortic stenosis (AS) severity. As a reference
for severity assessment, continuous wave Doppler was used. The data
were analyzed with recurrence quantification analysis (RQA) with
the aim to find features able to distinguish innocent murmurs from
murmurs caused by AS. Four out of eight investigated RQA features
showed significant differences between innocent murmurs and
pathological murmurs. Using a plain linear discriminant analysis
classifier, the best pair of features (recurrence rate and entropy)
resulted in a sensitivity of 90% and a specificity of 88%. In
conclusion, RQA provide valid features which can be used for
differentiation between innocent murmurs and murmurs caused by
AS.