Graphene systems are clean platforms for studying electron–electron (e–e) collisions. Electron transport in graphene constrictions is now found to behave anomalously due to e–e interactions: conductance values exceed the maximum free-electron value. Electron–electron (e–e) collisions can impact transport in a variety of surprising and sometimes counterintuitive ways1,2,3,4,5,6. Despite strong interest, experiments on the subject proved challenging because of the simultaneous presence of different scattering mechanisms that suppress or obscure consequences of e–e scattering7,8,9,10,11. Only recently, sufficiently clean electron systems with transport dominated by e–e collisions have become available, showing behaviour characteristic of highly viscous fluids12,13,14. Here we study electron transport through graphene constrictions and show that their conductance below 150 K increases with increasing temperature, in stark contrast to the metallic character of doped graphene15. Notably, the measured conductance exceeds the maximum conductance possible for free electrons16,17. This anomalous behaviour is attributed to collective movement of interacting electrons, which ‘shields’ individual carriers from momentum loss at sample boundaries18,19. The measurements allow us to identify the conductance contribution arising due to electron viscosity and determine its temperature dependence. Besides fundamental interest, our work shows that viscous effects can facilitate high-mobility transport at elevated temperatures, a potentially useful behaviour for designing graphene-based devices.

Superballistic flow of viscous electron fluid through graphene constrictions

In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

In this paper, a detailed review on the dynamics of axially moving systems is presented. Over the past 60 years, vibration control of axially moving systems has attracted considerable attention owing to the board applications including continuous material processing, roll-to-roll systems, flexible electronics, etc. Depending on the system’s flexibility and geometric parameters, axially moving systems can be categorized into four models: String, beam, belt, and plate models. We first derive a total of 33 partial differential equation (PDE) models for axially moving systems appearing in various fields. The methods to approximate the PDEs to ordinary differential equations (ODEs) are discussed; then, approximated ODE models are summarized. Also, the techniques (analytical, numerical) to solve both the PDE and ODE models are presented. The dynamic analyses including the divergence and flutter instabilities, bifurcation, and chaos are outlined. Lastly, future research directions to enhance the technologies in this field are also proposed. Considering that a continuous manufacturing process of composite and layered materials is more demanding recently, this paper will provide a guideline to select a proper mathematical model and to analyze the dynamics of the process in advance.

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Dynamic models of axially moving systems: A review

Effective machinery prognostics and health management play a crucial role in ensuring the safe and continuous operation of equipment, and satisfactory characteristics’ expression of machine health status plays a key role in the ability to diagnose faults with high accuracy. At present, most methods based on signal processing and the shallow learning model rely on artificial feature extraction to identify the machine fault type. In practical applications, however, meaningful health management requires correct recognition of not only the health type but also the fault degree, if any occurs. Such recognition is useful for determining the priority level of mechanical maintenance and minimizing economic losses. Deep learning techniques, such as deep belief network (DBN), have demonstrated great potential in exploring characteristic information from machine status signals. In this paper, an end-to-end fault diagnosis model based on an adaptive DBN optimized by the Nesterov moment (NM) is proposed to extract deep representative features from rotating machinery and recognize bearing fault types and degrees simultaneously. Frequency-domain signals are inputted into the model for feature learning, and NM is introduced to the training process of the DBN model. Individual adaptive learning rate algorithms are then applied to optimize parameter updating. The performance of the proposed method is validated using a self-made bearing fault test platform, and the model is shown to achieve satisfactory convergence and a testing accuracy higher than those obtained from standard DBN and support vector machine.

An End-to-End Model Based on Improved Adaptive Deep Belief Network and Its Application to Bearing Fault Diagnosis

The reduction of vibration in rotating or translating elastic systems (e.g., shafts, circular saws, belts, handsaws) is an important engineering problem. This paper presents the characteristics of rotating or translating elastic system vibration problems which are significant for the design of active controllers. The effect of the rotation or translation velocity on the controller design, and the effects of observation and control spillover are discussed. Simulation results for two example problems, a rotating cantilever shaft and an axially moving string, are used to illustrate the design and performance of active vibration controllers for rotating or translating elastic systems.

Vibration control in rotating or translating elastic systems.

Towards a fault diagnosis method for rolling bearing with Bi-directional deep belief network

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bea...

Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map:

As a weak signal processing method that utilizes noise enhanced fault signals, stochastic resonance (SR) is widely used in mechanical fault diagnosis. However, the classic bistable SR has a problem with output saturation, which affects its ability to enhance fault characteristics. Moreover, it is difficult to implement SR when the fault frequency is not clear, which limits its application in engineering practice. To solve these problems, this paper proposed an adaptive periodical stochastic resonance (APSR) method based on the grey wolf optimizer (GWO) algorithm for rolling bearing fault diagnosis. The periodical stochastic resonance (PSR) model can independently adjust the system parameters and effectively avoid output saturation. The GWO algorithm is introduced to optimize the PSR model parameters to achieve adaptive detection of the input signal, and the output signal-to-noise ratio (SNR) is used as the objective function of the GWO algorithm. Simulated signals verify the validity of the proposed method. Furthermore, this method is applied to bearing fault diagnosis; experimental analysis demonstrates that the proposed method not only obtains a larger output SNR but also requires less time for the optimization process. The diagnosis results show that the proposed method can effectively enhance the weak fault signal and has strong practical values in engineering.

An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

Nonlinear vibration characteristics of a moving membrane with variable velocity have been examined. The velocity is presumed as harmonic change that takes place over uniform average speed, and the nonlinear vibration equation of the axially moving membrane is inferred according to the D’Alembert principle and the von Karman nonlinear thin plate theory. The Galerkin method is employed for discretizing the vibration partial differential equations. However, the solutions concerning to differential equations are determined through the 4th order Runge–Kutta technique. The results of mean velocity, velocity variation amplitude, and aspect ratio on nonlinear vibration of moving membranes are emphasized. The phase-plane diagrams, time histories, bifurcation graphs, and Poincare maps are obtained; besides that, the stability regions and chaotic regions of membranes are also obtained. This paper gives a theoretical foundation for enhancing the dynamic behavior and stability of moving membranes.

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Nonlinear Parametric Vibration and Chaotic Behaviors of an Axially Accelerating Moving Membrane

The transverse vibration characteristics and stability of a moving membrane with several intermediate elastic supports are investigated considering the translating speed. The motion equations of the vibration system are derived according to the membrane vibration theory and the D'Alembert principle. The constraint of the middle printing roller to the membrane is used as the intermediate support which is replaced by the constraint force. And the vibration characteristic equation is established by using Laplace transformation method and the numerical calculation method. Finally, the relationships between the natural frequency of the moving membrane and the translating speed, tension, supporting position and rigidity modulus are analyzed respectively, and the critical speed when the membrane is in a stable state is obtained. The conclusions provide a theoretical basis and effective approaches for improving the work stability of the printing press.

https://journals.sagepub.com/doi/pdf/10.1260/0263-0923.33.1.65

Jimei Wu

Papers

Towards a fault diagnosis method for rolling bearing with Bi-directional deep belief network

Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map:

An Adaptive Periodical Stochastic Resonance Method Based on the Grey Wolf Optimizer Algorithm and Its Application in Rolling Bearing Fault Diagnosis

Nonlinear Parametric Vibration and Chaotic Behaviors of an Axially Accelerating Moving Membrane

Transverse Vibration Characteristics and Stability of a Moving Membrane with Elastic Supports