MARC details
| 000 -LEADER |
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| fixed length control field |
02793ntm a2200241 a 4500 |
| 001 - CONTROL NUMBER |
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| control field |
vtls000083327 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251117113244.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
141112t2013 my a f m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0004730(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
201905141048 |
| Level of effort used to assign nonsubject heading access points |
smfh |
| Level of effort used to assign subject headings |
201411261253 |
| Level of effort used to assign classification |
fawwaz |
| Level of effort used to assign subject headings |
201411141255 |
| Level of effort used to assign classification |
fawwaz |
| Level of effort used to assign subject headings |
201411121122 |
| Level of effort used to assign classification |
fawwaz |
| -- |
201411121120 |
| -- |
fawwaz |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
TP370.9.M38 R53 2013 r Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Muhammad Ridzuan Mamat |
| 245 10 - TITLE STATEMENT |
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| Title |
Utilizing multiple linear regression technique for interential measure of continuous-based process monitoring / |
| Statement of responsibility, etc. |
Muhammad Ridzuan Mamat |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2013 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xii, 31 p. : |
| Other physical details |
ill. ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-ROM |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Project paper (Bachelor of Chemical Engineering) -- Universiti Malaysia Pahang - 2013 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Bibliography : p. 29-31 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
The present conventional MSPC has several weaknesses in process fault detection and diagnosis. Some researchers in this filed had commented that the MSPC is a powerful tool for data complexity reduction and fault detection in the significant fault appearance data. The current fault detection and diagnosis method via MSPC is limited to significant faults and does not point put the insignificant ones accurately. In the real time, all variable will be used in monitoring. However in this case only a few of them are truly important. By developed modeling based on multiple linear regressions the relationship between these variables can be figure out. Multiple linear regressions (MLR) is a method used to model the linear relationship between a dependent variable and one or more independent variables. Some assumption should be made in order to obtain an accurate data analysis. The assumptions are variables should normally distribute, a linear relationship between the independent and dependent variables must exist and also the variable should be measure without an error. MLR is probably the most widely used in dendroclimatology for developing models to reconstruct climate variables. Besides they also proposed for control charting methods for lumber manufacturing and profile monitoring applied in public health surveillance. The methods to perform this modeling involve two phases which are Phase I: offline modeling and monitoring and Phase II: online monitoring. As a conclusion, the MLR method is success introduced as a significant improvement compared to the conventional method. On top of those objectives, the original goals of SPC are also been considered as well as carried together, such a way that the productivity of multivariate process monitoring is improved |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Chemical engineering |
| General subdivision |
Mathematical models |