Review on Lithium-ion Battery PHM from the Perspective of Key PHM Steps

Prognostics and health management(PHM)has gotten considerable attention in the background of Industry 4.0.Battery PHM contributes to the reliable and safe operation of electric devices.Nevertheless,relevant reviews are still continuously updated over time.In this paper,we browsed extensive literature related to battery PHM from 2018to 2023 and summarized advances in battery PHM field,including battery testing and public datasets,fault diagnosis and prediction methods,health status estimation and health management methods.The last topic includes state of health estimation methods,remaining useful life prediction methods and predictive maintenance methods.Each of these categories is introduced and discussed in details.Based on this survey,we accordingly discuss challenges left to battery PHM,and provide future research opportunities.This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.

Sufficient variable selection of high dimensional nonparametric nonlinear systems based on Fourier spectrum of density-weighted derivative

The variable selection of high dimensional nonparametric nonlinear systems aims to select the contributing variables or to eliminate the redundant variables.For a high dimensional nonparametric nonlinear system,however,identifying whether a variable contributes or not is not easy.Therefore,based on the Fourier spectrum of densityweighted derivative,one novel variable selection approach is developed,which does not suffer from the dimensionality curse and improves the identification accuracy.Furthermore,a necessary and sufficient condition for testing a variable whether it contributes or not is provided.The proposed approach does not require strong assumptions on the distribution,such as elliptical distribution.The simulation study verifies the effectiveness of the novel variable selection algorithm.

Targeted energy transfer of a parallel nonlinear energy sink

A parallel nonlinear energy sink(NES) is proposed and analyzed. The parallel NES is composed of a vibro-impact(VI) NES and a cubic NES. The dynamical equation is given, and the essential analytical investigation is carried out to deal with the cubic nonlinearity and impact nonlinearity. Multiple time-scale expansion is introduced, and the zeroth order is derived to give a rough outline of the system. The underlying Hamilton dynamic equation is given, and then the optimal stiffness is expressed. The clearance is regarded as a critical factor for the VI. Based on the periodical impact treatment by analytical investigation, the relationships of the cubic stiffness, the clearance, and the zeroth-order attenuation amplitude of the linear primary oscillator(LPO) are obtained.A cubic NES under the optimal condition is compared with the parallel NES. Harmonic signals, harmonic signals with noises, and the excitation generated by a second-order?lter are considered as the potential excitation forces on the system. The targeted energy transfer(TET) in the designed parallel NES is shown to be more e?cient.

An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis

Deep learning algorithms based on neural networks make remarkable achievements in machine fault diagnosis,while the noise mixed in measured signals harms the prediction accuracy of networks.Existing denoising methods in neural networks,such as using complex network architectures and introducing sparse techniques,always suffer from the difficulty of estimating hyperparameters and the lack of physical interpretability.To address this issue,this paper proposes a novel interpretable denoising layer based on reproducing kernel Hilbert space(RKHS)as the first layer for standard neural networks,with the aim to combine the advantages of both traditional signal processing technology with physical interpretation and network modeling strategy with parameter adaption.By investigating the influencing mechanism of parameters on the regularization procedure in RKHS,the key parameter that dynamically controls the signal smoothness with low computational cost is selected as the only trainable parameter of the proposed layer.Besides,the forward and backward propagation algorithms of the designed layer are formulated to ensure that the selected parameter can be automatically updated together with other parameters in the neural network.Moreover,exponential and piecewise functions are introduced in the weight updating process to keep the trainable weight within a reasonable range and avoid the ill-conditioned problem.Experiment studies verify the effectiveness and compatibility of the proposed layer design method in intelligent fault diagnosis of machinery in noisy environments.

Dynamic Characteristics of Rotor System with a Slant Crack Based on Fractional Damping

The traditional modeling method of rotor system with a slant crack considers only integer-order calculus.However,the model of rotor system based on integer-order calculus can merely describe local characteristics,not historical dependent process.The occur of fractional order calculus just makes up for the deficiency in integer-order calculus.Therefore,a new dynamic model with a slant crack based on fractional damping is proposed.Here,the stiffness of rotor system with a slant crack is solved by zero stress intensity factor method.The proposed model is simulated by Runge-Kutta method and continued fraction Euler method.The influence of the fractional order,rotating speed,and crack depth on the dynamic characteristics of rotor system is discussed.The simulation results show that the amplitude of torsional excitation frequency increases significantly with the increase of the fractional order.With the increase of the rotating speed,the amplitude of first harmonic component becomes gradually larger,the amplitude of the second harmonic becomes smaller,while the amplitude of the other frequency components is almost invariant.The shaft orbit changes gradually from an internal 8-type shape to an ellipse-type shape without overlapping.With the increase of the slant crack depth,the amplitude of the transverse response frequency in the rotor system with a slant crack increases,and the amplitude in the second harmonic component also increases significantly.In addition,the torsional excitation frequency and other coupling frequency components also occur.The proposed model is further verified by the experiment.The valuable conclusion can provide an important guideline for the fault diagnosis of rotor system with a slant crack.

Modal identification of multi-degree-of-freedom structures based on intrinsic chirp component decomposition method

Modal parameter identification is a mature technology.However,there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination.This paper proposes a new time-frequency method based on intrinsic chirp component decomposition(ICCD)to address these issues.In this method,a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction.The effectiveness and accuracy of the proposed method are illustrated using three examples:a cantilever beam structure with intensive noise contamination or environmental interference,a four-degree-of-freedom structure with two closely spaced modes,and an impact test on a cantilever rectangular plate.By comparison with the identification method based on the empirical wavelet transform(EWT),it is shown that the presented method is effective,even in a high-noise environment,and the dynamic characteristics of closely spaced modes are accurately determined.

Uncertainty quantification for stochastic dynamical systems using time-dependent stochastic bases

A novel method based on time-dependent stochastic orthogonal bases for stochastic response surface approximation is proposed to overcome the problem of significant errors in the utilization of the generalized polynomial chaos(GPC) method that approximates the stochastic response by orthogonal polynomials. The accuracy and effectiveness of the method are illustrated by different numerical examples including both linear and nonlinear problems. The results indicate that the proposed method modifies the stochastic bases adaptively, and has a better approximation for the probability density function in contrast to the GPC method.

Investigation on two-phase flow-induced vibrations of a piping structure with an elbow

The dynamic behaviors of a horizontal piping structure with an elbow due to the two-phase flow excitation are experimentally investigated.The effects of flow patterns and superficial velocities on the pressure pulsations and vibration responses are evaluated in detail.A strong partition coupling algorithm is used to calculate the flow-induced vibration(FIV)responses of the pipe,and the theoretical values agree well with the experimental results.It is found that the lateral and axial vibration responses of the bend pipe are related to the momentum flux of the two-phase flow,and the vibration amplitudes of the pipe increase with an increase in the liquid mass flux.The vertical vibration responses are strongly affected by the flow pattern,and the maximum response occurs in the transition region from the slug flow to the bubbly flow.Moreover,the standard deviation(STD)amplitudes of the pipe vibration in three directions increase with an increase in the gas flux for both the slug and bubbly flows.The blockage of liquid slugs at the elbow section is found to strengthen the vibration amplitude of the bend pipe,and the water-blocking phenomenon disappears as the superficial gas velocity increases.

New Sensing Technologies for Monitoring Machinery,Structures,and Manufacturing Processes

Sensing is the fundamental technique for sensor data acquisition in monitoring the operation condition of the machinery,structures,and manufacturing processes.In this paper,we briefly discuss the general idea and advances of various new sensing technologies,including multiphysics sensing,smart materials and metamaterials sensing,microwave sensing,fiber optic sensors,and terahertz sensing,for measuring vibration,deformation,strain,acoustics,temperature,spectroscopic,etc.Based on the observations from the state of the art,we provide comprehensive discussions on the possible opportunities and challenges of these new sensing technologies so as to steer future development.

Power fluctuation and power loss of wind turbines due to wind shear and tower shadow

The magnitude and stability of power output are two key indices of wind turbines. This study investigates the effects of wind shear and tower shadow on power output in terms of power fluctuation and power loss to estimate the capacity and quality of the power generated by a wind turbine. First, wind speed models, particularly the wind shear model and the tower shadow model, are described in detail. The widely accepted tower shadow model is modified in view of the cone-shaped towers of modem large-scale wind turbines. Power fluctuation and power loss due to wind shear and tower shadow are analyzed by performing theoretical calculations and case analysis within the framework of a modified version of blade element momentum theory. Results indicate that power fluctuation is mainly caused by tower shadow, whereas power loss is primarily induced by wind shear. Under steady wind conditions, power loss can be divided into wind farm loss and rotor loss. Wind farm loss is constant at 3a（3a- 1）R^2/（8H^2）. By contrast, rotor loss is strongly influenced by the wind turbine control strategies and wind speed. That is, when the wind speed is measured in a region where a variable-speed controller works, the rotor loss stabilizes around zero, but when the wind speed is measured in a region where the blade pitch controller works, the rotor loss increases as the wind speed intensifies. The results of this study can serve as a reference for accurate power estimation and strategy development to mitigate the fluctuations in aerodynamic loads and power output due to wind shear and tower shadow.

Parametric identification of time-varying systems from free vibration using intrinsic chirp component decomposition

Time-varying systems are applied extensively in practical applications,and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems.To improve the identification accuracy for time-varying systems,this study puts forward a novel parameter identification approach in the time-frequency domain using intrinsic chirp component decomposition(ICCD).ICCD is a powerful tool for signal decomposition and parameter extraction,with good signal reconstruction capability in a high-noise environment.To maintain good identification effects for the time-varying system in a noisy environment,the proposed method introduces a redundant Fourier model for the non-stationary signal,including instantaneous frequency(IF)and instantaneous amplitude(IA).The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters,as well as an example of a multi-degree-of-freedom system.The effects of different levels of measured noise on the identified results are also discussed in detail.Numerical results show that the proposed method is very effective in tracking the smooth,periodical,and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.

Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine

With the development of large rotary machines for faster and more integrated performance, the condition monitoring and fault diagnosis for them are becoming more challenging. Since the time-frequency （TF） pattem of the vibration signal from the rotary machine often contains condition information and fault feature, the methods based on TF analysis have been widely-used to solve these two problems in the industrial community. This article introduces an effective non-stationary signal analysis method based on the general parameterized tim^fre- quency transform （GPTFT）. The GPTFT is achieved by inserting a rotation operator and a shift operator in the short-time Fourier transform. This method can produce a high-concentrated TF pattern with a general kemel. A multi-component instantaneous frequency （IF） extraction method is proposed based on it. The estimation for the IF of every component is accomplished by defining a spectrum concentration index （SCI）. Moreover, such an IF estimation process is iteratively operated until all the components are extracted. The tests on three simulation examples and a real vibration signal demonstrate the effectiveness and superiority of our method.

Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals

Gearbox fault diagnosis based on vibration sensing has drawn much attention for a long time.For highly integrated complicated mechanical systems,the intercoupling of structure transfer paths results in a great reduction or even change of signal characteristics during the process of original vibration transmission.Therefore,using gearbox housing vibration signal to identify gear meshing excitation signal is of great significance to eliminate the influence of structure transfer paths,but accompanied by huge scientific challenges.This paper establishes an analytical mathematical description of the whole transfer process from gear meshing excitation to housing vibration.The gear meshing stiffness(GMS)identification approach is proposed by using housing vibration signals for two stages of inversion based on the mathematical description.Specifically,the linear system equations of transfer path analysis are first inverted to identify the bearing dynamic forces.Then the dynamic differential equations are inverted to identify the GMS.Numerical simulation and experimental results demonstrate the proposed method can realize gear fault diagnosis better than the original housing vibration signal and has the potential to be generalized to other speeds and loads.Some interesting properties are discovered in the identified GMS spectra,and the results also validate the rationality of using meshing stiffness to describe the actual gear meshing process.The identified GMS has a clear physical meaning and is thus very useful for fault diagnosis of the complicated equipment.

Pneumatic programmable superrepellent surfaces

Morphological transformation of surface structures is widely manifested in nature and highly preferred for many applications such as wetting interaction;however,in situ tuning of artificial morphologies independent of smart responsive materials remains elusive.Here,with the aid of microfluidics,we develop a pneumatic programmable superrepellent surface by tailoring conventional wetting materials(e.g.,polydimethylsiloxane)with embedded flexible chambers connecting a microfluidic system,thus realizing a morphological transformation for enhanced liquid repellency based on a nature‐inspired rigid‐flexible hybrid principle(i.e.,triggering symmetry breaking and oscillator coupling mechanisms).The enhancement degree can be in situ tuned within around 300 ms owing to pneumatically controllable chamber morphologies.We also demonstrate that the surface can be freely programmed to achieve elaborated morphological pathways and gradients for preferred droplet manipulation such as directional rolling and bouncing.Our study highlights the potential of an in situ morphological transformation to realize tunable wettability and provides a programmable level of droplet control by intellectualizing conventional wetting materials.

Rigid–flexible hybrid surfaces for water-repelling and abrasionresisting

Droplets impacting solid superhydrophobic surfaces is appealing not only because of scientific interests but also for technological applications such as water-repelling.Recent studies have designed artificial surfaces in a rigid–flexible hybrid mode to combine asymmetric redistribution and structural oscillation water-repelling principles,resolving strict impacting positioning;however,this is limited by weak mechanical durability.Here we propose a rigid–flexible hybrid surface(RFS)design as a matrix of concave flexible trampolines barred by convex rigid stripes.Such a surface exhibits a 20.1%contact time reduction via the structural oscillation of flexible trampolines,and even to break through the theoretical inertial-capillary limit via the asymmetric redistribution induced by rigid stripes.Moreover,the surface is shown to retain the above water-repelling after 1,000 abrasion cycles against oilstones under a normal load as high as 0.2 N·mm−1.This is the first demonstration of RFSs for synchronous waterproof and wearproof,approaching real-world applications of liquid-repelling.

Time of flight improved thermally grown oxide thickness measurement with terahertz spectroscopy

As a nondestructive testing technique,terahertz time-domain spectroscopy technology is commonly used to measure the thickness of ceramic coat in thermal barrier coatings(TBCs).However,the invisibility of ceramic/thermally grown oxide(TGO)reflective wave leads to the measurement failure of natural growth TGO whose thickness is below 10μm in TBCs.To detect and monitor TGO in the emergence stage,a time of flight(TOF)improved TGO thickness measurement method is proposed.A simulative investigation on propagation characteristics of terahertz shows the linear relationship between TGO thickness and phase shift of feature wave.The accurate TOF increment could be acquired from wavelet soft threshold and cross-correlation function with negative effect reduction of environmental noise and system oscillation.Thus,the TGO thickness could be obtained efficiently from the TOF increment of the monitor area with different heating times.The averaged error of 1.61μm in experimental results demonstrates the highly accurate and robust measurement of the proposed method,making it attractive for condition monitoring and life prediction of TBCs.

Millimeter-Wave Bat for Mapping and Quantifying Micromotions in Full Field of View

Echolocating bats possess remarkable capability of multitarget spatial localization and micromotion sensing in a full field of view(FFOV)even in cluttered environments.Artificial technologies with such capability are highly desirable for various fields.However,current techniques such as visual sensing and laser scanning suffer from numerous fundamental problems.Here,we develop a bioinspired concept of millimeter-wave(mmWave)full-field micromotion sensing,creating a unique mmWave Bat(“mmWBat”),which can map and quantify tiny motions spanning macroscopic toμm length scales of full-field targets simultaneously and accurately.In mmWBat,we show that the micromotions can be measured via the interferometric phase evolution tracking from range-angle joint dimension,integrating with full-field localization and tricky clutter elimination.With our approach,we demonstrate the capacity to solve challenges in three disparate applications:multiperson vital sign monitoring,full-field mechanical vibration measurement,and multiple sound source localization and reconstruction(radiofrequency microphone).Our work could potentially revolutionize full-field micromotion monitoring in a wide spectrum of applications,while may inspiring novel biomimetic wireless sensing systems.

Millimeter-Wave Bat for Mapping and Quantifying Micromotions in Full Field of View

Echolocating bats possess remarkable capability of multitarget spatial localization and micromotion sensing in a full field of view(FFOV)even in cluttered environments.Artificial technologies with such capability are highly desirable for various fields.However,current techniques such as visual sensing and laser scanning suffer from numerous fundamental problems.Here,we develop a bioinspired concept of millimeter-wave(mmWave)full-field micromotion sensing,creating a unique mmWave Bat(“mmWBat”),which can map and quantify tiny motions spanning macroscopic toμm length scales of full-field targets simultaneously and accurately.In mmWBat,we show that the micromotions can be measured via the interferometric phase evolution tracking from range-angle joint dimension,integrating with full-field localization and tricky clutter elimination.With our approach,we demonstrate the capacity to solve challenges in three disparate applications:multiperson vital sign monitoring,full-field mechanical vibration measurement,and multiple sound source localization and reconstruction(radiofrequency microphone).Our work could potentially revolutionize full-field micromotion monitoring in a wide spectrum of applications,while may inspiring novel biomimetic wireless sensing systems.