Diagnostics and prognostics based on adaptive time-frequency feature discrimination View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-09

AUTHORS

Jae Hyuk Oh, Chang Gu Kim, Young Man Cho

ABSTRACT

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/ vibration measurements. The capability is based on extracting relevant features of noise/ vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool. More... »

PAGES

1537-1548

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02990368

DOI

http://dx.doi.org/10.1007/bf02990368

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038910005


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