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<DIV><FONT face=Calibri><SPAN class=apple-style-span><B
style="mso-bidi-font-weight: normal"><U><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 18pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: pt-br; mso-fareast-language: pt-br; mso-bidi-language: ar-sa">Seminário
Conjunto UFSCar/ICMC – 05/09/2014 - 14h00</SPAN></U></B></SPAN></FONT></DIV>
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<P style="mso-line-height-alt: 13.5pt"><SPAN><STRONG><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 14pt">LOCAL: Sala 4005 -
</SPAN>ICMC-USP</STRONG></SPAN><SPAN> <STRONG><SPAN
style="mso-spacerun: yes"> </SPAN><o:p></o:p></STRONG></SPAN></P>
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class=MsoNormal><STRONG><SPAN><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 14pt">TÍTULO</SPAN>: ARA
and ARI Imperfect Repair Models: A Case Study in Industry</SPAN><SPAN
style="FONT-FAMILY: myriadroman; COLOR: #001f46; FONT-SIZE: 14pt; mso-bidi-font-family: tahoma; mso-font-kerning: 18.0pt"><o:p></o:p></SPAN></STRONG></P>
<P style="mso-line-height-alt: 13.5pt"><SPAN><STRONG><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 14pt">PALESTRANTE</SPAN>:
Enrico A. Colosimo – UFMG<o:p></o:p></STRONG></SPAN></P>
<P style="TEXT-ALIGN: justify; MARGIN: 0cm 0cm 0pt; mso-layout-grid-align: none"
class=MsoNormal><SPAN><STRONG><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 14pt">RESUMO</SPAN>:
</STRONG></SPAN><SPAN style="FONT-SIZE: 14pt"><BR><BR></SPAN><SPAN
style="FONT-FAMILY: 'Arial','sans-serif'; FONT-SIZE: 14pt">An appropriate
maintenance policy is essential to reduce expenses and risks related to
equipment failures. A fundamental aspect to be considered when specifying such
policies is to understand the behavior of the failure intensity for the systems
under study. The usual assumptions of minimal or perfect repair at failures are
not adequated for many real world systems, requiring the application of
imperfect repair (IR) models. In this paper, the classes ARA and ARI of IR
models proposed by Doyen and Gaudoin (2004) are explored. Likelihood functions
for such models are derived, assuming Power Law Process and a general memory m.
Based on this, punctual and interval parametric estimates were obtained for a
real dataset involving failures in trucks used by a mining company for models
ARAm and ARIm, m = 1; 2; 4; 6; 8; 12;1, and also for the model based on minimal
repair assumption. The maximum of the likelihood function value was used as the
criteria for model selection, and the best _tted model (ARI1) estimated
parameters, namely, shape and scale parameters for PLP, and the e_ciency of
repair parameter were obtained. They provided evidences that the trucks tend to
fail more frequently over time, justifying the necessity for preventive
maintenance, and also, that the repairs after failures tend to leave the
equipment in a state between as good as new and as bad as old. These results are
a valuable information for the mining company, and can be used to support
decision making regarding preventive maintenance
policy.<o:p></o:p></SPAN></P></SPAN></SPAN></FONT></DIV>
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