Automatic feature extraction of waveform signals for in-process diagnostic performance improvement
dc.contributor.author | Jin, Jionghua (Judy) | en_US |
dc.contributor.author | Shi, Jianjun | en_US |
dc.date.accessioned | 2006-09-11T17:53:15Z | |
dc.date.available | 2006-09-11T17:53:15Z | |
dc.date.issued | 2001-06 | en_US |
dc.identifier.citation | Jin, Jionghua; Shi, Jianjun; (2001). "Automatic feature extraction of waveform signals for in-process diagnostic performance improvement." Journal of Intelligent Manufacturing 12(3): 257-268. <http://hdl.handle.net/2027.42/46524> | en_US |
dc.identifier.issn | 0956-5515 | en_US |
dc.identifier.issn | 1572-8145 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/46524 | |
dc.description.abstract | In this paper, a new methodology is presented for developing a diagnostic system using waveform signals with limited or with no prior fault information. The key issues studied in this paper are automatic fault detection, optimal feature extraction, optimal feature subset selection, and diagnostic performance assessment. By using this methodology, a diagnostic system can be developed and its performance is continuously improved as the knowledge of process faults is automatically accumulated during production. As a real example, the tonnage signal analysis for stamping process monitoring is provided to demonstrate the implementation of this methodology. | en_US |
dc.format.extent | 580765 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Kluwer Academic Publishers; Springer Science+Business Media | en_US |
dc.subject.other | Economics / Management Science | en_US |
dc.subject.other | Manufacturing, Machines, Tools | en_US |
dc.subject.other | Automation and Robotics | en_US |
dc.subject.other | Production/Logistics | en_US |
dc.subject.other | Automatic Feature Extraction | en_US |
dc.subject.other | Haar Transform | en_US |
dc.subject.other | Waveform Signals | en_US |
dc.subject.other | Process Monitoring | en_US |
dc.subject.other | Fault Diagnosis | en_US |
dc.title | Automatic feature extraction of waveform signals for in-process diagnostic performance improvement | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor, MI, 48109-2117 | en_US |
dc.contributor.affiliationother | Department of Systems and Industrial Engineering, The University of Arizona, P.O. Box 210020, Tucson, Arizona, 85721-0020 | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/46524/1/10845_2004_Article_337289.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1023/A:1011248925750 | en_US |
dc.identifier.source | Journal of Intelligent Manufacturing | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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