Faults diagnosis based on system model in a discrete-part machining system

Root cause identification is one of deciding factors in current manufacturing competitions. For a discrete-part machining system, it is very challenging to identify the faults, since the final product variation caused by faults is an accumulation from all stations. This paper explores a faults diagn...

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Published in2007 IEEE International Conference on Industrial Engineering and Engineering Management pp. 1221 - 1225
Main Authors Du, S.C., Xi, L.F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2007
Subjects
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ISBN1424415284
9781424415281
ISSN2157-3611
DOI10.1109/IEEM.2007.4419386

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Abstract Root cause identification is one of deciding factors in current manufacturing competitions. For a discrete-part machining system, it is very challenging to identify the faults, since the final product variation caused by faults is an accumulation from all stations. This paper explores a faults diagnosis methodology for the root causes identification of a serial machining system. Firstly, a system model is described to capture the relationship between process faults and product quality. Then based on the model, the maximum likelihood estimation algorithms are built to estimate the key parameters of measurement data, such as the mean value and variance, which followed by a hypothesis testing method to determine the root causes at certain confidence level. A real machining case illustrates the effectiveness of the proposed faults diagnosis methodology.
AbstractList Root cause identification is one of deciding factors in current manufacturing competitions. For a discrete-part machining system, it is very challenging to identify the faults, since the final product variation caused by faults is an accumulation from all stations. This paper explores a faults diagnosis methodology for the root causes identification of a serial machining system. Firstly, a system model is described to capture the relationship between process faults and product quality. Then based on the model, the maximum likelihood estimation algorithms are built to estimate the key parameters of measurement data, such as the mean value and variance, which followed by a hypothesis testing method to determine the root causes at certain confidence level. A real machining case illustrates the effectiveness of the proposed faults diagnosis methodology.
Author Du, S.C.
Xi, L.F.
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Snippet Root cause identification is one of deciding factors in current manufacturing competitions. For a discrete-part machining system, it is very challenging to...
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StartPage 1221
SubjectTerms Discrete part machining system
Error correction
Fault diagnosis
Faults diagnosis
Fixtures
Industrial engineering
Machining
Manufacturing
Parameter estimation
Process control
Quality control
Solid modeling
Surface finishing
Title Faults diagnosis based on system model in a discrete-part machining system
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