Szczegóły publikacji
Opis bibliograficzny
Artificial Immune Systems for data classification in planetary gearboxes condition monitoring / Edyta BRZYCHCZY, Piotr Lipiński, Radosław Zimroz, Patryk Filipiak // W: Advances in Condition Monitoring of Machinery in Non-stationary Operations : proceedings of the third international conference on Condition Monitoring of Machinery in Non-stationary Operations CMMNO 2013 / eds. Giorgio Dalpiaz, [et al.]. — Berlin ; Heidelberg : Springer-Verlag, cop. 2014. — (Lecture Notes in Mechanical Engineering ; ISSN 2195-4356). — ISBN: 978-3-642-39347-1; e-ISBN: 978-3-642-39348-8. — S. 235–247
Autorzy (4)
- AGHBrzychczy Edyta
- Lipiński Piotr
- Zimroz Radosław
- Filipiak Patryk
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 81768 |
|---|---|
| Data dodania do BaDAP | 2014-06-11 |
| DOI | 10.1007/978-3-642-39348-8_20 |
| Rok publikacji | 2014 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | Third international conference on Condition Monitoring of Machinery in Non-Stationary Operations |
| Czasopismo/seria | Lecture Notes in Mechanical Engineering |
Abstract
In the paper a problem of diagnostic data classification is discussed. The classic condition monitoring approach requires two examples of machines: one in a good and one in a bad condition. From the industrial perspective such a requirement is often very difficult to fulfill, especially in the case of machines with an unique design. To overcome it, we proposed to use the Artificial Immune System (AIS) based approach to classify multidimensional diagnostic data. AIS allows to recognize a change of the machine condition based on a training phase using the dataset related to a good condition. To validate the proposed procedure and assess efficiency of the condition recognition, an extra data set from another machine (of the same type) in a bad condition was used. In the paper several key issues related to the selection of parameters have been discussed.
