False-alarm and non-detection probabilities for on-line quality control via HMM
On-line quality control during production calls for monitoring produced items according to some prescribed strategy. It is reasonable to assume the existence of system internal non-observable variables so that the carried out monitoring is only partially reliable. In this note, under the setting...
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| Principais autores: | , , , |
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| Formato: | article |
| Idioma: | English |
| Publicado em: |
International Journal of Mathematical Analysis
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| Assuntos: | |
| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/50046 |
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| Resumo: | On-line quality control during production calls for monitoring produced
items according to some prescribed strategy. It is reasonable to
assume the existence of system internal non-observable variables so that
the carried out monitoring is only partially reliable. In this note, under
the setting of a Hidden Markov Model (HMM) and assuming that
the evolution of the internal state changes are governed by a two-state
Markov chain, we derive estimates for false-alarm and non-detection
malfunctioning probabilities. Kernel density methods are used to approximate
the stable regime density and the stationary probabilities.
As a side result, alternative monitoring strategies are proposed. |
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