Dynamic Prognoser Architecture via the Path Classification and Estimation (PACE) Model

Most modern prognostic algorithms are founded on a simple abstraction of device degradation, for an individual device there exists a degradation signal that progresses along a unique path until it crosses a critical failure threshold. While this abstraction has been shown to be valid for well understood failure modes under controlled stress conditions, its viability in "real world" devices being exposed to "real world" stresses is questionable. Because the complexity of degradation should scale in a similar fashion as the complexity of the device, the applicability of a simple abstraction of degradation is increasingly arguable for modern devices. This paper will propose an alternative to the current abstraction of degradation, which is founded on the premise that degradation data should be allowed to speak for itself. In this way, many different forms of information can be incorporated into a prognoser's estimate of a device's remaining useful life (RUL). More specifically, this paper will outline a methodology for implementing a dynamic prognoser that can be incrementally trained to learn general (physical model output, expert opinion, etc.) and specific ("real world" data) degradation trends. This work will demonstrate the viability of the proposed method by applying a particular embodiment, namely the path classification and estimation (PACE) model, to data collected from a deep-well oil exploration drill. To begin, expert opinion will be used to develop a PACE prognoser. Next, data collected from individual drills will be used to incrementally train the prognoser to learn specific degradation trends.