Prescriptive maintenance for complex products with digital twin considering production planning and resource constraints

Maintenance is a critical aspect of complex products through entire life cycle, often requiring coordination of production planning and available resources, while previous studies appear to have rarely addressed. With this in mind, this paper presents a prescriptive maintenance framework based on digital twins (DTs) for reducing operational risk and maintenance costs of complex equipment clusters. Virtual entities are firstly constructed for each single asset in multiple dimensions, which use real-time or historical sensing data collected from the physical entities to predict the corresponding remaining useful life (RUL). Then such RUL information is incorporated into a stochastic programming model with chance constraints to enable dynamic decision making. In particular, a risk-based optimization model is formulated to take full account of the physical distances between facilities and production gaps. Further, a dual-sense pyramidal transformer model is proposed to sense important details of data in both time and space while capturing temporal dependencies at different scales. Compared to existing data-driven approaches, the proposed DT-based alternative achieves dynamic real-time interaction between physical and virtual units driven by both models and data, while virtual verification based on high-fidelity models ensures high reliability of maintenance decisions, which has also been validated in an aero-engine maintenance case study.