Reliability modeling in a predictive maintenance context: A margin-based approach

Abstract

Current system reliability methods (typically based on fault trees [FTs] or reliability block diagrams) can effectively propagate reliability data from the asset level to the system level in order to identify system-critical points. However, the asset reliability data employed are an approximated integral representation of past industry-wide operational experience, and thus 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 historical performance data and should not be an approximated integral representation of past industry-wide operational experience (as currently performed by system reliability models through Bayesian updating processes). Sensor data, diagnostic assessments, and prognostic assessments are in fact not considered in plant reliability models used to inform system engineers as to which assets are the most critical. In addition, propagation of 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 these two goals by proposing a different approach to reliability modeling—one that relies on asset diagnostic/prognostic assessments and monitoring data to measure asset health. Propagation of health data from the asset level to the system level is performed through FT 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). Per a cause-effect lens, while classical reliability models target the effect associated with asset performance, a margin-based approach focuses on the cause of the undesired asset performance (i.e., its health). Hence, thinking of reliability in terms of margins implies decision-making processes based on causal reasoning. We show how FT models can be solved using a margin-based reliability mindset, and how this process can effectively assist system engineers in identifying which assets are the most critical to system performance.