Using support vector machines for recognizing shifts in correlated manufacturing processes

Author

Chinnam, Ratna Babu ; Kumar, Vinay S.

Author_Institution

Dept. of Ind. & Manuf. Eng., Wayne State Univ., Detroit, MI, USA

Volume

3

fYear

2001

fDate

2001

Firstpage

2276

Abstract

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries due to the fact that data from these facilities are auto-correlated. Several attempts have been made in the literature to extend traditional SPC techniques to deal with auto-correlated parameters. However, these extensions pose several serious limitations. This paper demonstrates that support vector machines can be extremely effective in minimizing both type-I errors and type-II errors in these auto-correlated processes

Keywords

correlation methods; learning automata; manufacture; neural nets; statistical process control; SPC; autocorrelated data; autocorrelated parameters; autocorrelated processes; correlated manufacturing processes; error minimization; shift recognition; statistical process control; support vector machines; type-I errors; type-II errors; Data engineering; Error correction; Error probability; Industrial control; Manufacturing industries; Manufacturing processes; Monitoring; Process control; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.938521

Filename

938521