Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1693
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKaewkongka, T-
dc.contributor.authorAu, YHJ-
dc.contributor.authorRakowski, RT-
dc.contributor.authorJones, BE-
dc.coverage.spatial5en
dc.date.accessioned2008-02-22T16:05:28Z-
dc.date.available2008-02-22T16:05:28Z-
dc.date.issued2001-
dc.identifier.citationProc of IEEE Conference IMTC, Budapest. 3 1962- 1966.en
dc.identifier.isbn0-7803-6646-8-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/1693-
dc.description.abstractThis paper describes a novel method of rotodynamic machine condition monitoring using a wavelet transform and a neural network. A continuous wavelet transform is applied to the signals collected from accelerometer. The transformed images are then extracted as unique characteristic features relating to the various types of machine conditions. In the experiment, four types of machine operating conditions have been investigated: a balanced shaft; an unbalanced shaft, a misaligned shaft and a defective bearing. The back propagation neural network (BPNN) is used as a tool to evaluate the performance of the proposed method. The experimental results result in a recognition rate of 90 percent.en
dc.format.extent459750 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIEEEen
dc.subjectWavelet transformen
dc.subjectneural networken
dc.subjectrotodynamic machineryen
dc.titleContinuous wavelet transform and neural network for condition monitoring of rotodynamic machineryen
dc.typeConference Paperen
dc.identifier.doihttp://dx.doi.org/10.1109/IMTC.2001.929543-
Appears in Collections:Advanced Manufacturing and Enterprise Engineering (AMEE)
Dept of Design Research Papers



Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.