Fault Detection and Diagnosis in Film Processing Plants · 2003-11-02 · HWAHAK KONGHAK Vol. 41,...

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585 HWAHAK KONGHAK Vol. 41, No. 5, October, 2003, pp. 585-591 * * ,† ** ** , * , ( ) 790-784 31 ** ( ) 730-350 93-1 (2003 10 10 !, 2003 7 10 "#) Fault Detection and Diagnosis in Film Processing Plants Dong-Myung Yoon, Young-Hak Lee*, Chonghun Han* , , Hun Sung An** and Sa Yun Chang** Department of Chemical Engineering, *Department of Chemical Engineering and ISYSTECH Co. Ltd., Pohang University of Science and Technology San 31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk 790-784, Korea **ToraySaehan Co. Ltd., 93-1, Imsu-dong, Gumi, Kyungbuk 730-350, Korea (Received 10 October 2002; accepted 10 July 2003) , ! !" #$ %! &. ()*" +, -./0" 1 234 567 89 :1, ;<!" =>? @9 A BC, DE F. BC G+ HC, BIJ !" KL MCJ, => @NO LPP Q!R ST UVF . DEF MC, = => W ST* X?!R, YZF ST ?[\ ]^. _‘!a. _0 bc cdO* ef!" ST gh* ij!R, 89 ]k]B l =>* L m n, opq N &rJ s, =>* ^N WL &rt, u1!a. Abstract - The fierce competition in the polymer film and sheet market requires the industry to satisfy much higher product quality specifications. This paper proposes a monitoring and diagnosis method based on multivariate statistical techniques which help us reduce the amount of off-spec product. The method has been applied to an industrial web forming plant and has proven that process faults such as the leak of polymer fluid can be early detected before it is developed into the production of bad quality product. Keywords: Film Process, Principal Component Analysis (PCA), Multivariate Statistical Process Control (MSPC), Fault Detec- tion, Diagnosis 1. , . !, " # $ %&’ () * +, - ./ 01 234 5 6 789+: 7;<= >? @ AB CD E:F, GD BH@ 6. I J K " BHL MNKO PQ, %&D RS, TPUVW X Y Z>[\]D G^_‘ V’ ab 4 E P cd ‘e f gh# K@f i. j %&: Kk l7 HJ gm(machine direction, MD) : ngm(cross direction, CD) f o$pq. ! %&D r gmsF tfe uv‘ w x‘ @q yx, ngm z{ | quadratic programming 4 Ec 0V_ E} ~ K@q[1]. d: " 1 gh’ ngm f gm f‘ V ‘7 (steady state) J ngm f xL q. I J gh’ ngm l7J EY #f uv V Q Z q. ngm gmD L fJ ; bVsF E PQ Bergh MacGregor[2] linear-quadratic- gaussian f ™4 VE E. ee ngm gm f VsF H@ 6s bVsF ngm f‘ ; f  }¡ 6[3,4]. ngm gm f-sF £∕4 ƒD §‘ ¤E'F, K2J G4 «‹E} ~ @fi. %&D RSL fE PEc gage f‘ H@q[5], fiflE °–D ²V ‡4 QE PQ K·4 ¢µ(identification), 7(estimation) fE H‘ 6qs[6,7], •‚ To whom correspondence should be addressed. E-mail: [email protected]

Transcript of Fault Detection and Diagnosis in Film Processing Plants · 2003-11-02 · HWAHAK KONGHAK Vol. 41,...

HWAHAK KONGHAK Vol. 41, No. 5, October, 2003, pp. 585-591

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Fault Detection and Diagnosis in Film Processing Plants

Dong-Myung Yoon, Young-Hak Lee*, Chonghun Han*,†, Hun Sung An** and Sa Yun Chang**

Department of Chemical Engineering, *Department of Chemical Engineering and ISYSTECH Co. Ltd., Pohang University of Science and Technology

San 31, Hyoja-dong, Nam-gu, Pohang, Kyungbuk 790-784, Korea**ToraySaehan Co. Ltd., 93-1, Imsu-dong, Gumi, Kyungbuk 730-350, Korea

(Received 10 October 2002; accepted 10 July 2003)

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Abstract − The fierce competition in the polymer film and sheet market requires the industry to satisfy much higher productquality specifications. This paper proposes a monitoring and diagnosis method based on multivariate statistical techniques

which help us reduce the amount of off-spec product. The method has been applied to an industrial web forming plant and has

proven that process faults such as the leak of polymer fluid can be early detected before it is developed into the production of

bad quality product.

Keywords: Film Process, Principal Component Analysis (PCA), Multivariate Statistical Process Control (MSPC), Fault Detec-

tion, Diagnosis

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Fig. 6. Plot of the first two PCs (a) corresponding to group one data, (b)corresponding to group two data.

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Fig. 8. Temperature changes of the first extruder, the first filter, the sec-ond extruder, the gear pump, the second filter, the melting line,the third filter, and the die.

Fig. 9. Real-time monitoring and fault detection in PC space: Film-bro-ken case.

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i : index for events

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P : loading matrix for [X]

pk : kth loading vector for [X]

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T : score matrix for [X]

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tk : kth score vector for [X]

V : covariance matrix of [E]

X : process data set

xij : obtained data

xi : column vector [i]

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HWAHAK KONGHAK Vol. 41, No. 5, October, 2003