Domain-driven Residual Useful Life Estimation.

Predictive maintenance is widely agreed upon as the superior method for maintenance scheduling over time/condition-based choices. This is where the remaining life cycles of each part/component as well as the system in its entirety are forecast, informing the optimal period to perform a maintenance action. In current literature, this is often done using a value or set of values representing the degradation of the components or system; such a value is called a Health Index (HI), built from a function of the sensors on the machine(s) to be monitored. This paper will introduce and expand upon methods to utilise a flexible and domain-defined (HI), derived from the use of a Digital Risk Twin (DRT). The twin is used to capture domain knowledge directly from subject matter experts to determine the optimal HI at both the component stages in addition to one's representative of the system, thus defining at various levels of indenture. The HI is coupled with state-of-the-art deep learning and machine learning techniques to confidently forecast trends observed in a system or its individual parts to enable prognostics with a high calibre of predictive integrity. Syndrome Diagnostics (SD), a tool to incorporate the research and prototypical work laid out will also be presented. [ABSTRACT FROM AUTHOR]