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Adversarial data splitting for domain generalization

Science China(Information Sciences) 2024年第5期67卷 28-42页

作者： Xiang GU Jian SUN Zongben XU School of Mathematics and Statistics Xi'an Jiaotong University

domain generalization aims to learn a model that is generalizable to an unseen target domain,which is a fundamental and challenging task in machine learning for out-of-distribution generalization. This paper proposes a novel domain generalization approach that enforces the learned model to be able to generalize well over the train/val subset splitting of the training dataset. This idea is modeled herein as an adversarial data splitting framework, formulated as a min-max optimization problem inspired by the metalearning approach. The min-max optimization problem is solved by iteratively splitting the training dataset into the training and val subsets to maximize the domain shift measured by the objective function and updating the model parameters to enable the model to generalize well from the training subset to the val subset by minimizing the objective function. This adversarial training approach does not assume the known domain labels of the training data; instead, it automatically investigates the “hard” splitting of the train/val subsets to learn the generalizable model. Extensive experimental results using three benchmark datasets demonstrate the superiority of this approach. In addition, we derive a generalization error bound for the theoretical understanding of our proposed approach.

关键词： domain generalization adversarial learning data splitting meta-learning out of distribution

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Dynamic Vision-Based Machinery Fault Diagnosis With Cross-Modality Feature Alignment

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2068-2081页

作者： Xiang Li Shupeng Yu Yaguo Lei Naipeng Li Bin Yang Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China IEEE

Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades,and the vibration acceleration data collected by contact accelerometers have been widely *** many industrial scenarios,contactless sensors are more *** event camera is an emerging bio-inspired technology for vision sensing,which asynchronously records per-pixel brightness change polarity with high temporal resolution and low *** offers a promising tool for contactless machine vibration sensing and fault ***,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,***,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault *** at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this *** is motivated to explore the abundant vibration acceleration data for enhancing the dynamic vision-based model performance.A crossmodality feature alignment method is thus proposed with deep adversarial neural networks to achieve fault diagnosis knowledge *** event erasing method is further proposed for improving model robustness against *** proposed method can effectively identify unseen fault mode with dynamic vision *** on two rotating machine monitoring datasets are carried out for validations,and the results suggest the proposed method is promising for generalized contactless machinery fault diagnosis.

关键词： Condition monitoring domain generalization eventbased camera fault diagnosis machine vision

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Condition transfer between prestressed bridges using structural state translation for structural health monitoring

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AI in Civil Engineering 2023年第1期2卷 23-37页

作者： Furkan Luleci F.Necati Catbas Department of Civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFL 32816USA

Implementing Structural Health Monitoring(SHM)systems with extensive sensing layouts on all civil structures is obviously expensive and ***,estimating the state(condition)of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential *** this purpose,Structural State Translation(SST)has been recently proposed to predict the response data of civil structures based on the information acquired from a dissimilar *** study uses the SST methodology to translate the state of one bridge(Bridge#1)to a new state based on the knowledge acquired from a structurally dissimilar bridge(Bridge#2).Specifically,the domain-Generalized Cycle-Generative(DGCG)model is trained in the domain generalization learning approach on two distinct data domains obtained from Bridge#1;the bridges have two different conditions:State-H and ***,the model is used to generalize and transfer the knowledge on Bridge#1 to Bridge#*** doing so,DGCG translates the state of Bridge#2 to the state that the model has learned after being *** one scenario,Bridge#2’s State-H is translated to State-D;in another scenario,Bridge#2’s State-D is translated to *** translated bridge states are then compared with the real ones via modal identifiers and mean magnitude-squared coherence(MMSC),showing that the translated states are remarkably similar to the real *** instance,the modes of the translated and real bridge states are similar,with the maximum frequency difference of 1.12%and the minimum correlation of 0.923 in Modal Assurance Criterion values,as well as the minimum of 0.947 in Average MMSC *** conclusion,this study demonstrates that SST is a promising methodology for research with data scarcity and population-based structural health monitoring(PBSHM).In addition,a critical discussion about the methodology adopted in this study is also offered to address some related concerns.

关键词： Structural state translation Structural health monitoring domain generalization Population-based structural health monitoring Generative adversarial networks

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