Reduction of Redundancy of Symptom Observation Space in Systems Condition Monitoring

• Autorzy: Cempel C.
• Języki publikacji: EN

Abstrakty

EN

It was shown in this paper that classical approach to the assessment of systems condition evolution 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 here SD and SG, with high dynamics of evolution during system life. Moreover, we can add 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 of symptom measuring space, and choose the best symptom for condition observation.

Słowa kluczowe

EN

mechanical systems in operation; systems condition; modelling; evolution; symptom models; logistic vector; symptom observation; observation matrix; singular value decomposition (SVD); symptom discriminant; generalized fault symptoms

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2001
• Tom: vol. 49, nr 2
• Strony: 267--284
• Opis fizyczny: 5 rys., 1 tabela, bibliogr. 15 poz.

Twórcy

autor

Cempel C.

• Poznań University of Technology, ul. Piotrowo 3, PL-60965 Poznań, Poland (Politechnika Poznańska, Instytut Mechaniki Stosowanej),