Overview of digital twin-driven rotating machinery fault diagnosis: status and trends
Lijuan Zhao, Tong Xie, Yuhong Wei, Y. Liu, Yi Qin
Abstract
Abstract Rotating machinery holds a pivotal role in industrial and daily life. Incorporating robust health monitoring or fault diagnosis methodologies is imperative to safeguard such equipment’s reliability and operational availability. Digital twin (DT) technology, which mirrors the real-time condition of a physical entity through a digital counterpart, has garnered considerable attention, particularly in the realm of fault diagnosis for rotating machinery. Despite its growing significance, there is a notable lack of comprehensive literature reviews pertaining to this domain. Consequently, this paper thoroughly reviews current and emerging DT technology-driven rotating machinery fault diagnosis trends. Initially, the origin and evolution of DT, alongside its application in fault diagnosis across diverse areas, are elucidated. Subsequently, the pivotal technologies and extant solutions within the DT-driven fault diagnosis purview are inspected. Furthermore, the literature on DT-driven rotating machinery for real-time condition monitoring, fault diagnosis, and life prediction is examined through varying tasks and application imperatives. Ultimately, the extant challenges of besetting DT-driven rotating machinery fault diagnosis techniques are defined, and their prospective evolutionary trajectories are appraised.