With the rapid improvement of Industrial Internet of Things and artificial intelligence, predictive maintenance (PdM) has attracted great attention from both academia and industrial practitioners. When equipment is running, the electrical attributes have intrinsic relations. Meanwhile, they are changing over time. However, existing PdM models are often limited as they lack considering both attribute interactions and temporal dependence of the dynamic working system. To address the problem, in this article, we propose an electrical spatio-temporal graph convolutional network (Electrical-STGCN) for PdM. First, it takes a sequence of electrical records as input. Next, both attribute interactions and temporal dependence are established to extract features. Then, the extracted features are fed into a prediction component. Finally, the output of the Electrical-STGCN (i.e., remaining useful life) can help the workers decide whether to carry out equipment maintenance. The effectiveness of the proposed method is verified in real-world cases. Our method achieves 85.2% Accuracy and 0.9 F1-Score, which are better than the other approaches.

In a paced aircraft final assembly line, some disturbances can be collected timely on the basis of the cyber-physical production system. In order to reduce the execution deviation, some workers need to switch among stations after a fixed period. Thus, a worker allocation problem with the multistage workstation is introduced first. Then, an integer programming formulation is presented to formulate the problem with the objective of shortest workstation cycle and the workload balance of both stations and workers. Moreover, a modified nondominated sorting genetic algorithm (NSGA-IV) is proposed to solve it, which tradeoff the convergence and the population diversity in the decision space. Finally, the NSGA-IV algorithm compares with five multiobjective evolutionary algorithms in a real-world case. Compared with manual allocation, the takt time of an aircraft final assembly line is reduced by 20.86% by using the NSGA-IV algorithm.

Motivated by the importance of service quality in nowadays customer business environment, we focus on inventory optimization under probabilistic service level constraints , namely, the α service level (also known as the ready rate) or the β service level (also known as the fill rate). Under service level constraints, we consider two canonical stochastic inventory models: (i) the classical inventory control model with backlogging and (ii) the remanufacturing inventory control model with random product returns. The random demands could be non‐stationary, evolving and correlated over time. For each model, we first establish the optimality of generalized base‐stock policies, and then propose a new approximation algorithm that admits a worst‐case performance guarantee of 2. The core concept developed in this study is called the delayed forced holding and production cost , which is proven effective in dealing with service level constrained inventory systems. We also provide an efficient heuristic algorithm for the multi‐item inventory system. Our extensive computational experiments show that the proposed algorithms perform within 2% of optimality.

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships in the scene. Recently, tremendous progress has been made in exploring better context relationship representations. Previous work mainly focuses on contextual information aggregation and uses de-biasing strategies on samples to eliminate the preference for head predicates. However, there remain challenges caused by indeterminate feature training. Overlooking the label confusion problem in feature training easily results in a messy feature distribution among the confused categories, thereby affecting the prediction of predicates. To alleviate the aforementioned problem, in this paper, we focus on enhancing predicate representation learning. Firstly, we propose a novel Adaptive Message Passing (AMP) network to dynamically conduct information propagation among neighbors. AMP provides discriminating representations for neighbor nodes under the view of de-noising and adaptive aggregation. Furthermore, we construct a feature-assisted training paradigm alongside the predicate classification branch, guiding predicate feature learning to the corresponding feature space. Moreover, to alleviate biased prediction caused by the long-tailed class distribution and the interference of confused labels, we design a Bi-level Curriculum learning scheme (BiC). The BiC separately considers the training from the feature learning and de-biasing levels, preserving discriminating representations of different predicates while resisting biased predictions. Results on multiple SGG datasets show that our proposed method AMP-BiC has superior comprehensive performance, demonstrating its effectiveness.

This paper uses a distributed approach fault tolerant control to solve the Nash equilibrium (NE) search problem for integral multi-agent systems (MAS) subject to undesirable sensor faults. In partial information games, information exchange between agents plays an essential role in ensuring the successful completion of the NE search. A flexible observer-based control algorithm has been proposed to compensate for the adverse effects of sensor faults. It reduces the influence of sensor faults on group decisions-making and realizing the optimization of group strategy. The distributed design can guarantee that the MAS can gradually approach the NE regardless of sensor faults. Finally, the effectiveness of the technology is illustrated through a simulation example.