Exploring impedance spectrum for lithium-ion batteries diagnosis and prognosis:A comprehensive review

Lithium-ion batteries have extensive usage in various energy storage needs,owing to their notable benefits of high energy density and long lifespan.The monitoring of battery states and failure identification are indispensable for guaranteeing the secure and optimal functionality of the batteries.The impedance spectrum has garnered growing interest due to its ability to provide a valuable understanding of material characteristics and electrochemical processes.To inspire further progress in the investigation and application of the battery impedance spectrum,this paper provides a comprehensive review of the determination and utilization of the impedance spectrum.The sources of impedance inaccuracies are systematically analyzed in terms of frequency response characteristics.The applicability of utilizing diverse impedance features for the diagnosis and prognosis of batteries is further elaborated.Finally,challenges and prospects for future research are discussed.

Autism spectrum disorder and personality disorders: Comorbidity and differential diagnosis

BACKGROUND Differential diagnosis,comorbidities and overlaps with other psychiatric disorders are common among adults with autism spectrum disorder(ASD),but clinical assessments often omit screening for personality disorders(PD),which are especially common in individuals with high-functioning ASD where there is less need for support.AIM To summarize the research findings on PD in adults with ASD and without intellectual disability,focusing on comorbidity and differential diagnosis.METHODS PubMed searches were performed using the key words“Asperger’s Syndrome”,“Autism”,“Personality”,“Personality disorder”and“comorbidity”in order to identify relevant articles published in English.Grey literature was identified through searching Google Scholar.The literature reviews and reference sections of selected papers were also examined for additional potential studies.The search was restricted to studies published up to April 2020.This review is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method.RESULTS The search found 22 studies carried out on ASD adults without intellectual disability that met the inclusion criteria:16 evaluated personality profiles or PD in ASD(comorbidity),five compared ASD and PD(differential diagnosis)and one performed both tasks.There were significant differences in the methodological Cluster A and cluster C PD are the most frequent co-occurring PD,but overlapping features should be considered.Data on differential diagnosis were only found with cluster A and cluster B PD.CONCLUSION ASD in high-functioning adults is associated with a distinct personality profile even if variability exists.Further studies are needed to explore the complex relationship between ASD and PD.

SIMULATION OF CRACK DIAGNOSIS OF ROTOR BASED ON MULTI-SCALE SINGUUR-SPECTRUM ANALYSIS

In the diagnosis of rotor crack based on wavelet analysis, it is a painful task to find out an adaptive mother wavelet as many of them can be chosen and the analytic results of different mother wavelets are yet not the same. For this limitation of wavelet analysis, a novel diagnostic approach of rotor crack based on multi-scale singular-spectrum analysis （MS-SSA） is proposed. Firstly, a Jeffcott model of a cracked rotor is developed and the forth-order Runge-Kutta method is used to solve the motion equations of this rotor to obtain its time response （signals）. Secondly, a comparatively simple approach of MS-SSA is presented and the empirical orthogonal functions of different orders in various scales are regarded as analyzing functions. At last, the signals of the cracked rotor and an uncracked rotor are analyzed using the proposed approach of MS-SSA, and the simulative results are compared. The results show that, the data-adaptive analyzing functions can capture many features of signals and the rotor crack can be identified and diagnosed effectively by comparing the analytic results of signals of the cracked rotor with those of the uncracked rotor using the analyzing functions of different orders.

Demodulation Based on Harmonic Wavelet and Its Application into Rotary Machinery Fault Diagnosis

The harmonic wavelet transform（HWT） and its fast realization based on fast Fourier transform（FFT） are introduced. Its ability to maintain the same amplitude-frequency feature is revealed. A new method to construct the time-frequency（TF） spectrum of HWT is proposed, which makes the HWT TF spectrum able to correctly reflect the time-frequency-amplitude distribution of the signal. A new way to calculate the HWT coefficients is proposed. By zero padding the data taken out, the non-decimated coefficients of HWT are obtained. Theoretical analysis shows that the modulus of the coefficients obtained by the new calculation way and living at a certain scale are the envelope of the component in the corresponding frequency band. By taking the cross section of the new TF spectrum, the demodulation for the component at a certain frequency band can be realized. A comparison with the Hilbert demodulation combined with band-pass filtering is done, which indicates for multi-components, the method proposed here is more suitable since it realizes ideal band-pass filtering and avoids pass band selecting. In the end, it is applied to bearing and gearbox fault diagnosis, and the results reflect that it can effectively extract the fault features in the signal.

Diagnosis of Nitrogen Status in Rice Leaves with Canopy Spectral Reflectance

The investigation was made on the relationship of seasonal time-course canopy spectral reflectance and ratio index to total leaf nitrogen accumulation (leaf nitrogen content per unit ground area) in rice under different nitrogen treatments. The results showed there was a close correlation between the canopy spectral reflectance and total leaf nitrogen accumulation. Ratio of near infrared to green band (R810/R560) was linearly related with total leaf nitrogen accumulation. independent of nitrogen levels and development stages. Different datasets were used to test the linear regression equation, with average estimation accuracy of 91. 22%, RMSE of 1.09 and average relative error of 0. 026. Thus, the ratio index R810/R560 of canopy spectral reflectance should be useful for non-destructive monitoring and diagnosis of nitrogen status in rice plants.

HIGHER ORDER SPECTRAL ANALYSIS IN FAULT DIAGNOSIS OF ROTORS

The nonlinear properties of rotating machinery vibration signals are presented. The relationship between faults and quadratic phase coupling is discussed. The mechanism that gives rise to quadratic phase coupling is analyzed, and the coupling models are summarized. As a result, higher order spectra analysis is introduced into fault diagnosis of rotors. A brief review of the properties of higher order spectra is presented. Furthermore, the bicoherence spectrum is employed to extract the features that signify the machinery condition. Experiments show that bicoherence spectrum patterns of different faults are quite different, so it is proposed to identify the faults in rotors.

Simultaneous measurements of global vibrational spectra and dephasing times of molecular vibrational modes by broadband time-resolved coherent anti-Stokes Raman scattering spectrography

In broadband coherent anti-Stokes Raman scattering （CARS） spectroscopy with supercontinuum （SC）, the simultaneously detectable spectral coverage is limited by the spectral continuity and the simultaneity of various spectral components of SC in an enough bandwidth. By numerical simulations, the optimal experimental conditions for improving the SC are obtained. The broadband time-resolved CARS spectrography based on the SC with required temporal and spectral distributions is realised. The global molecular vibrational spectrum with well suppressed nonresonant background noise can be obtained in a single measurement. At the same time, the measurements of dephasing times of various molecular vibrational modes can be conveniently achieved from intensities of a sequence of time-resolved CARS signals. It will be more helpful to provide a complete picture of molecular vibrations, and to exhibit a potential to understand not only both the solvent dynamics and the solute-solvent interactions, but also the mechanisms of chemical reactions in the fields of biology, chemistry and material science.

Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis

Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification.However,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification.In this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD diagnosis.The model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning.Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability.We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out cross-validations.Results showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction performance.We also localized important FC features and brain regions associated with ASD classification.Overall,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.

A Method for Incipient Fault Diagnosis of Roller Bearings Based on the Wavelet Transform Correlation Filter and Hilbert Transform

Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.

Study of Fault Diagnosis in Bend Axis Piston Pump

On the basis of theoretical analysis and experimental rerearck, the vibration characteristics of the ZB1-107 bend axis piston pump that is wldely ed in mining machinery is studied in the paper, and the study provides the basis for pump fault diagnesis. The vibration signals of the rault-rree pump and tbe faulty pump have been compared in frequency domaln and it is round that tbere is obvious differeuce in their vibration frequency spectra. The experimentol results demonstrate that the raults, such as port plate wear and tear and the looseness or ball joint or the conuecting rod, can be effectively detected through vibration analysis.

Evaluation of Diesel Engine Noise Reduction Measures Based on Hierarchy Diagnosis

Effect of different noise reduction measures for diesel engines was evaluated based on hierarchy diagnosis. The hierarchy diagnosis chart and hierarchy judgment matrix were given. Through evaluation of noise reduction measures, the main strategies of noise reduction were found. The result shows that the noise reduction level of different frequency belts varies from measure to measure. The reduction capacity of different measures could not add simply, which relates to the problem of parameter matching.

Ultrafast Decay Dynamics of N-Ethylpyrrole Excited to the S_(1)Electronic State:A Femtosecond Time-Resolved Photoelectron Imaging Study

N-ethylpyrrole is one of ethylsubstituted derivatives of pyrrole and its excited-state decay dynamics has never been explored.In this work,we investigate ultrafast decay dynamics of N-ethylpyrrole excited to the S_(1)electronic state using a femtosecond time-resolved photoelectron imaging method.Two pump wavelengths of 241.9 and 237.7 nm are employed.At 241.9 nm,three time constants,5.0±0.7 ps,66.4±15.6 ps and 1.3±0.1 ns,are derived.For 237.7 nm,two time constants of 2.1±0.1 ps and 13.1±1.2 ps are derived.We assign all these time constants to be associated with different vibrational states in the S_(1)state.The possible decay mechanisms of different S_(1)vibrational states are briefly discussed.

Research on Gear-broken Fault Diagnosis in a Tank Gearbox

A fault diagnosis method of working position gear in a tank gearbox is put forward based on simulating the fault of working position gear in an actual tank,extracting the envelope of vibration signal by Hilbert transformation amplitude demodulation method,and zooming the low-frequency band to envelope signal.

The Application of Cumulants Slices in Fault Diagnosis

Complex third-order cumulant has different definition forms. Different forms have different coupling properties, and the generated complex cumulants slices contain different coupling information of signals. In experiments, using the different definitions, the same coupling method is applied to both specific fault signals and normal signals. Furthermore, complex third-order cumulant slices spectrum is defined, and it is used to analyse the coupling features of normal signals and fault signals. Experiments indicate that the detection accuracy rate on the same fault is not the same with the different coupling method, thus, it provides an alternative method to diagnose the specific fault.

The Application of Coupled Three Order Cumulants’ Differential Feature in Fault Diagnosis

Complex three-order cumulant has different definition forms. Different forms conclude different information. For studying the effection of frequency in the coupled signals to fault diagnosis, the differential method to the three order cumulants of coupled signals is adopted. By using the differential of complex three order cumulants before and after respectively, then their ?dimensional spectrum is calculated, and the results are used to fault diagnosis. The experimental results show that, the increase frequency item in three order cumulants after differentiated impacts on the results of fault diagnosis and the degree of effection is relative to the differential times. And the correct rate of fault diagnosis can be raised by changing the differential times of three order cumulants.

The Application of Three Order Cumulants Coupling Features in Fault Diagnosis

Although mechanical vibration is extremely complicated, each fault signal produced by it has its own inherent features, The distinction may be most prominent between the certain components hidden in those features and the same components of normal signals. Three-order cumulant can reduce the Gaussian background noise automatically and its complex formal includes different coupling information of its signal. In the experiment, through these different coupling modes, the same coupling components are fetched from specific fault signal and normal signal, then these components are used to diagnose that certain fault. The results show that the method can fetch the most prominent distinction between normal signal and the specific fault signal, so the specific fault diagnosis by this method is satisfactory.

A Study of the Equipment Fault Diagnosing Method Based on EMD and Holo-spectrum

We first introduce the empirical mode decomposition （EMD） and holo-spectrum analysis. Then we decompose the vibration signal of the equipment by EMD, re-organize the signal closely associated with the original signal, eliminate the noise signal better, and analyze the holographic re-signal spectrum, which makes the amplitude, frequency and phase combinecompletely, reflecting the equipment vibration morphology fully. Particularly applying a two-dimensional holo-spectrum can obtain the direction of rotation order harmonic, size, shape, and the relation- . ship between order harmonic generation rotor vibrations, and so on. The traditional signal processing methods can not eliminate noise well. We apply this method to the rotor vibration signal of fault diagnosis, eliminate noise and reflect the equipment vibration characteristics well.

Biomarkers for early diagnosis of autism obtained through bioinformatics analysis of human blood sample

Objective:autism spectrum disorder(ASD),a serious disorder that begins early in life,con-tinues throughout the lifespan.Children with ASD who are diagnosed early are more responsive to therapeutic interventions and have less social and language impair-ment than children diagnosed later;however,current di-agnostic measures are mostly applied to children older than one year and lack the appropriate biological markers for early diagnosis of ASD.Using bioinformatic analy-sis,this study explores the molecular information mech-anism of ASD.Method:in this study,we used dataset GSE6575 from Gene Expression Omnibus(GEO)to an-alyze the mRNA expression profile of ASD,including 35 ASD samples and 12 normal control samples looking for different genes and we did enrichment analysis of those genes.We then used the STRING database to construct a protein-protein interaction(PPI)network of differential genes.Finally,Cytoscape plug-in cytoHubba was used to search for hub genes.The diagnostic value of the hub genes was verified by subject operating characteristic curves.Result:we looked for 50 different genes and did an enrichment analysis of those genes.The results of the enrichment analysis showed that these differential genes were mainly concentrated in the response to viruses,the immune regulation of inflammation and energy metabo-lism.Using Cytoscape plug-in cytoHubba,we found ten different genes.We drew ROC curves for all ten genes among which two genes,interleukin 2 receptor subunit beta(IL2Rβ)and perforin 1(PRF1),had good sensitivity and specificity for the early diagnosis of autism.The ar-eas under the ROC curves were 0.855,0.830 for IL2Rβ,PRF1.Conclusion:data analysis using the GEO data-base can provide new insights into the etiology of ASD as well as some possible biomarkers and therapeutic targets for early diagnosis and treatment of ASD.

Time Synchronous Averaging Based on Cross-power Spectrum

Periodic components are of great significance for fault diagnosis and health monitoring of rotating machinery.Time synchronous averaging is an effective and convenient technique for extracting those components.However,the performance of time synchronous averaging is seriously limited when the separate segments are poorly synchronized.This paper proposes a new averaging method capable of extracting periodic components without external reference and an accurate period to solve this problem.With this approach,phase detection and compensation eliminate all segments'phase differences,which enables the segments to be well synchronized.The effectiveness of the proposed method is validated by numerical and experimental signals.