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Fault diagnosis in thermoplastic inj...

Zhang, Jin.

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Fault diagnosis in thermoplastic injection molding via mold cavity pressure signal analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fault diagnosis in thermoplastic injection molding via mold cavity pressure signal analysis./
作者:	Zhang, Jin.
面頁冊數:	113 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5313.
Contained By:	Dissertation Abstracts International67-09B.
標題:	Engineering, Electronics and Electrical. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3234266
ISBN:	9780542879845

Fault diagnosis in thermoplastic injection molding via mold cavity pressure signal analysis.
Zhang, Jin.

Fault diagnosis in thermoplastic injection molding via mold cavity pressure signal analysis. - 113 p.

Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5313.

Thesis (Ph.D.)--University of Louisville, 2006.

This dissertation presents the development of a novel approach for process monitoring and diagnostics in a thermoplastic injection molding process via cavity pressure signal analysis using the pattern recognition techniques. In this application of pattern recognition techniques for process fault diagnostic system development, principle component analysis is applied to reduce the dimensionality of the original signals; Process "fingerprints" were developed via wavelet decomposition of the "reduced" signal using multi-resolution analysis. Feature elements of the "fingerprints" were defined element-by-element statistical index comparison. The final feature elements were classified and interpreted with the aid of artificial neural networks, for process condition monitoring and fault diagnosis.

ISBN: 9780542879845Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Fault diagnosis in thermoplastic injection molding via mold cavity pressure signal analysis.
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500 $a Source: Dissertation Abstracts International, Volume: 67-09, Section: B, page: 5313.
500 $a Adviser: Suraj M. Alexander.
502 $a Thesis (Ph.D.)--University of Louisville, 2006.
520 $a This dissertation presents the development of a novel approach for process monitoring and diagnostics in a thermoplastic injection molding process via cavity pressure signal analysis using the pattern recognition techniques. In this application of pattern recognition techniques for process fault diagnostic system development, principle component analysis is applied to reduce the dimensionality of the original signals; Process "fingerprints" were developed via wavelet decomposition of the "reduced" signal using multi-resolution analysis. Feature elements of the "fingerprints" were defined element-by-element statistical index comparison. The final feature elements were classified and interpreted with the aid of artificial neural networks, for process condition monitoring and fault diagnosis.
520 $a In thermoplastic injection molding, mold cavity pressure signals facilitate process monitoring and diagnosis. The "healthy" process condition and parameter settings which lead to a "healthy" cavity pressure profiles are obtained using design of experiments and response surface analysis. The diagnostic system developed is shown to be fast, robust and adaptable. This methodology is suitable for on-line use for fault diagnostics and the run-to-run process control.
590 $a School code: 0110.
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650 4 $a Engineering, Mechanical. $3 783786
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