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

Abstract

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 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. We have demonstrated through a maintenance case of turbofan engines that the proposed scheme significantly lowers total maintenance costs while reducing frequent visits from maintenance personnel.

Measurement Science and Technology’s second century underway

“…This year, the editorial board reviewed the journal scope [25] and decided to broaden it to reflect the increasing role that digitalization, artificial intelligence machine learning and digital twinning will have in the future. There have already been articles covering these themes published in the journal; for example [26][27][28], as well as many papers that describe the use of neural networks.…”