In modern industrial systems, components have complex interactions with each other, which makes it become a challenging task to identify the operational conditions of industrial systems. Considering that an industrial system, the embedded components and their interactions can be expressed as nodes and edges in a graph, respectively. Therefore, graph representation algorithms are powerful tools for fault diagnosis of industrial systems. As one of the most commonly used graph representation algorithms, graph neural networks (GNN) mainly follow the law of “learning to attend.” GNN extract training data features learn the statistical correlations between features and labels, resulting in the attended graph favoring for accessing noncausal features as a shortcut for prediction. This shortcut feature is unstable and depends on the data distribution characteristics in the training dataset, which reduces the generalization ability of the classifier. By performing the causal analysis of GNN modeling for graph representation, the results show that shortcut features act as confounding factors between causal features and predictions, causing classifiers to learn wrong correlations. Therefore, to discover patterns of causality and weaken the confounding effects of shortcut features, a causal-trivial attention graph neural network strategy is proposed. First, node and edge representations are given by estimating soft masks. Second, through disentanglement, both causal features and shortcut features are obtained from the graph. Third, the backdoor adjustment of the causal theory is parameterized to combine each causal feature with a variety of shortcut features. Finally, comparative experiments on the three-phase flow facility dataset illustrate the effectiveness of the proposed method.

The research of intelligent fault diagnosis method has made great progress. The prerequisite for the effectiveness of most intelligent diagnosis models is to have abundant labeled data, which is difficult to satisfy in practice. Fortunately, we can obtain a large amount of rolling bearing failure data under laboratory conditions. Inspired by the idea of transfer learning, we propose a deep dynamic adaptive transfer network (DDATN) for intelligent fault diagnosis of rolling bearings. In addition to performing transfer diagnosis under different working conditions and failure degrees of the same type of bearing, it is also able to accomplish the task of cross-machine fault diagnosis from bearings under laboratory conditions to the bearings in practical applications. In the DDATN, the marginal probability distribution and conditional probability distribution of the data are aligned by dynamic domain adaptation using weight factor. First, the original vibration signal of the bearing is first processed to establish the source and target domains. Then, pseudo-label learning on target domain unlabeled data is performed and the transferable features between domains are extracted through the deep parameter-shared neural networks. Next, by performing dynamic adaptation on the extracted transferable features and optimizing the intelligent fault diagnosis model through backpropagation, the complete transfer diagnosis task in the target domain is accomplished. The effectiveness of the proposed DDATN method is demonstrated through variable working conditions and cross-machine transfer fault diagnosis tasks. Compared with other intelligent fault diagnosis methods, the proposed method shows clear advantages.

Researchers have been trying to develop lower-limb exoskeletons to reduce the metabolic cost for human augmentation. The research on (quasi-)passive exoskeletons has gained more widespread attention since the first passive ankle exoskeleton was demonstrated to reduce the metabolic cost of human walking in 2015. Here, we reviewed studies on lower-limb (quasi-)passive exoskeletons for human performance augmentation in the past decade and highlighted key innovations and techniques to enable some of these exoskeletons to achieve the goal of reducing metabolic cost. We reviewed the (quasi-)passive exoskeleton research from three aspects including biological fundamentals for exoskeleton design, assistive principle and mechanical design of exoskeleton, which are the primary considerations of designing and evaluating (quasi-)passive exoskeletons. Lastly, we underlined some practical challenges and emerging trends of (quasi-)passive exoskeleton technology for further enhancing human mobility performance in the future.

Fouling in heat exchanger tubes is a common problem in the operation of condensers. The deposition of fouling can affect the thermal efficiency and safety of the condenser. Therefore, it is necessary to predict the impact of fouling on time and carry out scientific treatment. Firstly, fault prognosis methods require a significant amount of historical fault data, which is often lacking in practical applications. This paper proposes a method based on dynamically adjusting parameters of the fouling thermal resistance empirical equation to establish a fouling thermal resistance digital twin model. It is combined with simulation tools to rapidly generate a large amount of fault data for the research of prognosis methods. Secondly, in the research of fault prognosis methods, prognosis accuracy relies on establishing a reliable and accurate model that describes the behavior of faults. The uncertainty in the modeling process significantly affects the results. Classic modeling methods do not effectively quantify uncertainty. Therefore, this paper proposes a method that applies differential modeling to predict fouling faults in condensers, automatically obtaining uncertain parameters while establishing a reliable model. By calculating the performance evaluation indicator, the accuracy error indicator of the differential modeling-based prognosis method is further reduced to 0.35. The results demonstrate that this method can provide effective reference opinions for handling fouling faults in condensers.