Integrated diagnostics and time to maintenance estimation for complex engineering systems

Prognostics and Health Management (PHM) [1] is a key enabler of Condition Based Maintenance Plus (CBM+) [2]. In essence, it refers to the “Plus” by providing the ability to predict future health status of a system or component, as well as providing the ability to anticipate faults, problems, potential failures, and required maintenance actions. From the perspective of operation and maintenance (O&M) world, the vital knowledge requirements from PHM are indicators of degraded health condition (alarm, warnings, call for inspection, etc.), estimates of time to onset of such indicators, estimate of time to maintenance, and ahead-of-time diagnostics for identification of the root causes (or sources) that will likely cause these maintenance calls. Such knowledge provides lead time to the operators and system maintainers to prepare for inspection and schedule maintenance opportunistically, so as to minimize downtime and optimize maintenance cost. Qualtech Systems, Inc. (QSI) has developed a domain-neutral capability for tracking and trending sensor observations with considerations to operating mode changes, sensor dropouts, and measurement noise. This capability has been introduced to TEAMS
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(Testability Engineering and Maintenance Systems) [3] - the health management decision-support software suite of QSI. By leveraging the built-in diagnostic features of TEAMS, this capability provides `time-to-alarm' and `time-tomaintenance' estimates along with the list of potential failure sources (subsystems, components, etc.) responsible for the predicted alarms and maintenance calls. A trend fusion capability has also been introduced for accurately estimating the time to maintenance for components monitored by multiple sensors exhibiting differing observation trends. Introduction of these capabilities facilitates utilization of same dependency model (TEAMS model) for reactive diagnosis and proactive identification of the components that require maintenance within a time horizon set by the operator or the maintainer. This allays the need for developing separate diagnostic and prognostic models, which in general are costly and lengthy work - and thereby offers an efficient and economic enabler for the CBM+ paradigm.