Rethinking Reliability in Terms of Margins

Ideally, reliability methods should support assessing and managing system health by utilizing the integrated health information of all the system assets. An important aspect is that reliability data employed in these methods are an approximated integral representation of past industrywide operational experience. Thus, they neglect an asset's present health status (obtainable, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Asset health should be informed solely by that specific asset's current and past performance data and should not be an approximated integral representation of past industrywide operational experience. Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models. In addition, propagating quantitative health data from the asset level to the system level is made challenging by the diverse nature and structure of health data elements (e.g., vibration spectra, temperature readings, and expected failure time). Ideally, in a predictive maintenance context, system reliability models would support decision-making by propagating available health information from the asset level to the system level to provide a quantitative snapshot of system health and identify the most critical assets. This paper directly addresses the limitations of current reliability methods by proposing a different approach to reliability modeling: a method that relies on asset diagnostic, prognostic, and monitoring data to measure asset health. Propagating health data from the asset level to the system level is performed through reliability models, not in terms of probability but rather in terms of margin, with margin being the “distance” between the asset's present status and an undesired event (e.g., failure or unacceptable performance).