{"id":217,"date":"2011-03-08T09:09:43","date_gmt":"2011-03-08T14:09:43","guid":{"rendered":"http:\/\/www.livingreliability.com\/en\/?p=217"},"modified":"2025-11-06T06:02:55","modified_gmt":"2025-11-06T11:02:55","slug":"defeating-cbm","status":"publish","type":"post","link":"https:\/\/www.livingreliability.com\/en\/posts\/defeating-cbm\/","title":{"rendered":"Defeating CBM"},"content":{"rendered":"

The following slide presentation on the importance of distinguishing failures from suspensions on work orders,\u00a0 refers to the Proportional Hazard Model (PHM) and the shape parameter \u03b2. The shape parameter is calculated from a good sample of condition monitoring and age data by using the EXAKT CBM optimizing system. EXAKT uses PHM by including CBM (i.e. condition monitoring) data in an extended Weibull equation. The equation for the PHM (extended Weibull) that relates the failure rate to both age t, and condition monitoring data Zi<\/sub> is:<\/em><\/p>\n

\"h(t,\mathbf{Z}(t);\beta,\eta,\gamma)=\frac{\beta}{\eta}(\frac{t}{\eta})^{\beta-1}exp(\sum_{i=1}^{m}\gamma_{i}Z_{i}(t))\"
\n<\/strong><\/p>\n

where \u03b2>0 is the shape parameter, \u03b7>0 is the scale parameter, and \u03b3<\/strong> =( \u03b31<\/sub>,\u03b32<\/sub>,\u2026 \u03b3m<\/sub>,) is the coefficient vector for the condition monitoring variable (covariate) vector Z<\/strong>(t)<\/em>. The parameters \u03b2, \u03b7, and \u03b3<\/strong>, will be estimated by the numerical algorithm in EXAKT.<\/p>\n