NDLI: Automatic feature extraction of waveform signals for in-process diagnostic performance improvement

Content Provider	IEEE Xplore Digital Library
Author	Jionghua Jin Jianjun Shi
Copyright Year	1998
Description	Author affiliation: Dept. of Ind. & Oper. Eng., Michigan Univ., Ann Arbor, MI, USA (Jionghua Jin)
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, the system diagnostic 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.
Starting Page	4716
Ending Page	4721
File Size	588298
Page Count	6
File Format	PDF
ISBN	0780347781
ISSN	1062922X
DOI	10.1109/ICSMC.1998.727597
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-10-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Signal processing Condition monitoring Fault detection Face detection Production systems Manufacturing processes Testing Force sensors Fault diagnosis
Content Type	Text
Resource Type	Article

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Automatic feature extraction of waveform signals for in-process diagnostic performance improvement