The functional parts of a machine tool determine its reliability level to a great extent. The failure prediction of the functional part is helpful to prepare the maintenance scheme in time, in order to ensure a stable manufacturing process and the required production quality. Due to the rise of digital twin (DT), which has the characteristics of virtual reality interaction and real-time mapping, a DT-based real-time prediction method of the remaining useful life (RUL) and preventive maintenance scheme is proposed in this study. In this method, a DT model of the manufacturing workshop is established based on real-time perceptual information obtained by the proposed acquisition method. Subsequently, the real-time RUL of the functional part is predicted by establishing an RUL prediction model based on the nonlinear-drifted Brownian motion, which takes the working conditions and measurement errors into consideration. On this basis, the optimal preventive maintenance scheme can be determined and fed back to the manufacturing workshop, in order to guide the maintenance of relevant parts. Finally, an example case study is presented to illustrate the feasibility and effectiveness of the proposed method.

Failure mode and effects analysis (FMEA) is a widely used technique for identifying, evaluating, and eliminating potential failures in production, system, and process. The traditional FMEA ranks the failure modes according to risk priority numbers (RPN), which are obtained by the multiplications of the crisp values of risk factors, such as occurrence (O), severity (S), and detection (D). However, the traditional FMEA is criticized for mishandling uncertain information and calculating RPN unreasonably. To overcome the above deficiencies, this study presents an advanced FMEA method combined with interval 2-tuple linguistic variables (ITLV) and technique for order preference by similarity to ideal solution (TOPSIS). In the proposed method, the evaluations given by different FMEA members based on their different linguistic term sets are represented by ITLVs, which are feasible and valid variables to effectively deal with uncertain information. The TOPSIS method is used to rank the risk priorities of failure modes by comprehensively considering all of risk factors. Finally, an application case is provided to illustrate the validity and robustness of the proposed method.