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.
Defect recognition for metal surface in the industry has attracted more and more attention. However, the defect data scarcity brings a huge challenge for the defect recognition in the real industrial scenario. The traditional few-shot defect recognition method can address this problem when the train data and test data are collected from the same or similar metal surface. While the defect data from the similar metal surfaces is difficult to acquire to a certain extent. In this paper, we introduce a novel task setting which can achieve the few-shot defect recognition by transferring the knowledge across domains. This method consists of two levels: image-level and feature level. In the image-level, the meta-augmentation method is proposed to improve the recognition generalization in each meta-task by joint parameter updating from the original domain and augmented domain. In the feature-level, the class covariance guided feature perturbation method is proposed to perturb the feature distribution for enhancing the cross-domain generalization capability. Extension cross-domain experiments from the textured surface to metal surface show the superior performance of the proposed method compared with other mainstream methods.
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