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Modelling and data mining in vibration condition monitoring

Cempel C.

Prace Naukowe Instytutu Górnictwa Politechniki Wrocławskiej. Konferencje

1999

Vol. 86, nr 24

33-46

It was shown in this paper that classical approach to systems condition evolution assessment can be much improved by special processing of observed symptoms of condition. When we have a large symptom data base, we can apply singular value decomposition (SVD), as the newest data mining procedure to obtain a symptom and condition evolution model. By using SVD it is possible to have two additional independent fault discriminants: named CD and SG, with high dynamics of evolution. Moreover we can an additional column of system life count, as the first approximation of a logistic vector describing the unit life history. It is also possible to use the value of a pseudo - determinant of a symptom observation matrix, and correlation between this new discriminant and the symptom observation matrix to minimize the redundancy measuring space, and chose the best symptom for condition observation.