Digital-twin-enabled intelligent operation and maintenance for telescope-drive systems under extreme environments

Abstract

Telescope-drive systems are a critical but underdiscussed foundation of astronomical performance because they determine whether commanded motion can be converted into stable pointing, smooth tracking, and long-duration operational availability. This challenge is becoming more acute in next-generation observatories, where direct-drive architectures, extreme environmental exposure, sparse fault data, and restricted maintenance access make conventional reliability assumptions increasingly inadequate. We synthesize the emerging literature on intelligent operation and maintenance (O&M) for telescope-drive systems, with particular emphasis on extreme-environment and unattended scenarios, and organize the field across five linked stages: state perception, fault diagnosis, behavior prediction, safety protection, and intelligent decision-making. We argue that digital twin provides the most coherent architecture for binding these stages into a closed-loop health-management framework; reframe telescope-drive reliability as a telescope-systems problem spanning sensing, control, degradation, and operational decision; place the unanticipated state at the center of telescope intelligent O&M; and evaluate current astronomical evidence across representative observatories and telescope projects. The main conclusion is that near-term progress depends on telescope-specific unanticipated-state definitions, condition-aware twins validated against laboratory and field evidence, sparse-data diagnosis that preserves uncertainty, and graded protection and decision logic suited to remote observatory operation.