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

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 investigated.In many industrial scenarios,contactless sensors are more preferred.The 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 latency.It offers a promising tool for contactless machine vibration sensing and fault diagnosis.However,the dynamic vision-based methods suffer from variations of practical factors such as camera position,machine operating condition,etc.Furthermore,as a new sensing technology,the labeled dynamic vision data are limited,which generally cannot cover a wide range of machine fault modes.Aiming at these challenges,a novel dynamic vision-based machinery fault diagnosis method is proposed in this paper.It 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 transfer.An event erasing method is further proposed for improving model robustness against variations.The proposed method can effectively identify unseen fault mode with dynamic vision data.Experiments 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.

Transfer Learning for Prognostics and Health Management:Advances,Challenges,and Opportunities

As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management(PHM)domain,transfer learning provides a fundamental solution to enhance generalization of datadriven methods.In this paper,we briefly discuss general idea and advances of various transfer learning techniques in PHM domain,including domain adaptation,domain generalization,federated learning,and knowledge-driven transfer learning.Based on the observations from state of the art,we provide extensive discussions on possible challenges and opportunities of transfer learning in PHM domain to direct future development.

The Integral Representations and Their Applications on the Analytic Varieties of Bounded Domains in Stein Manifolds

In this paper,we first obtain a unified integral representation on the analytic varieties of the general bounded domain in Stein manifolds(the two types bounded domains in[3]are regarded as its special cases).Secondly we get the integral formulas of the solution of∂-equation.And we use a new and unique method to give a uniform estimate of the solution of∂-equation,which is different from Henkin's method.

THE NAVIER-STOKES EQUATIONS WITH THE KINEMATIC AND VORTICITY BOUNDARY CONDITIONS ON NON-FLAT BOUNDARIES

We study the initial-boundary value problem of the Navier-Stokes equations for incompressible fluids in a general domain in R^n with compact and smooth boundary, subject to the kinematic and vorticity boundary conditions on the non-flat boundary. We observe that, under the nonhomogeneous boundary conditions, the pressure p can be still recovered by solving the Neumann problem for the Poisson equation. Then we establish the well-posedness of the unsteady Stokes equations and employ the solution to reduce our initial-boundary value problem into an initial-boundary value problem with absolute boundary conditions. Based on this, we first establish the well-posedness for an appropriate local linearized problem with the absolute boundary conditions and the initial condition （without the incompressibility condition）, which establishes a velocity mapping. Then we develop apriori estimates for the velocity mapping, especially involving the Sobolev norm for the time-derivative of the mapping to deal with the complicated boundary conditions, which leads to the existence of the fixed point of the mapping and the existence of solutions to our initial-boundary value problem. Finally, we establish that, when the viscosity coefficient tends zero, the strong solutions of the initial-boundary value problem in R^n（n ≥ 3） with nonhomogeneous vorticity boundary condition converge in L^2 to the corresponding Euler equations satisfying the kinematic condition.

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

Implementing Structural Health Monitoring(SHM)systems with extensive sensing layouts on all civil structures is obviously expensive and unfeasible.Thus,estimating the state(condition)of dissimilar civil structures based on the information collected from other structures is regarded as a useful and essential way.For 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 structure.This 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 State-D.Then,the model is used to generalize and transfer the knowledge on Bridge#1 to Bridge#2.In doing so,DGCG translates the state of Bridge#2 to the state that the model has learned after being trained.In one scenario,Bridge#2’s State-H is translated to State-D;in another scenario,Bridge#2’s State-D is translated to State-H.The 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 ones.For 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 values.In 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.

A New Boundary Condition for the Three-Dimensional MHD Equation and the Vanishing Viscosity Limit

In this paper, we consider the viscous incompressible magnetohydrodynamic （MHD） system with a new boundary condition for a general smooth domain in R^3. We obtain the well-posedness of the system and the vanishing viscosity limit result.