CBM focuses on expensive long-life assets that are subject to condition monitoring. Two key issues are diagnosing an asset’s state of health, and providing a prognosis of its remaining useful life (RUL).
Our approach employs proportional hazards modeling to pinpoint the key risk factors that threaten the health of the asset from all signals obtained during health monitoring. This hazard estimate (the conditional probability of failure) is then blended with economic considerations to establish optimal CBM decisions.
The optimal decisions provide a recommendation for the maintenance action to be taken at next inspection, along with an estimate of the asset’s RUL from both an economic perspective and a specified definition of failure.