A simulation-driven prediction model for state of charge estimation of electric vehicle lithium battery.

Accurately predicting the state of charge (SOC) of lithium-ion batteries in electric vehicles is crucial for ensuring their stable operation. However, the component values related to SOC in the circuit typically require estimation through parameter identification. This paper proposes a three-stage method for estimating the SOC of lithium batteries in electric vehicles. Firstly, the parameters of the constructed second-order RC circuit are identified using the Forgetting Factor Recursive Least Squares (FFRLS) method. Secondly, an innovative approach is employed to construct a battery simulation model using modal-data fusion method. Finally, the predicted values of the simulation model are corrected using the unscented Kalman filter (UKF). Validation through datasets demonstrates the high precision of this method in parameter identification. Moreover, in the comparison of SOC prediction corrections with Particle Filter (PF), Extended Kalman Filter (EKF), and the proposed UKF on simulated prediction data and experimental test data. The proposed method achieves the lowest root mean square error (RMSE) of 0.0025 for simulation prediction data and 0.0186 for experimental test data. It also maintained its error within 5 % on actual data.

Multi-domain wavelet boundary element method for calculating two-dimensional stress intensity factors.

In order to improve the accuracy of stress intensity factors (SIFs) calculated by traditional boundary element methods (BEM), the multi-domain wavelet boundary element method (WBEM) is proposed. Firstly, by adjusting the nodes of the B-spline wavelet element on the interval, crack-tip elements are constructed. Since B-spline wavelet on the interval (BSWI) has excellent compact support characteristics and is particularly suitable for describing solution domains with large gradient changes, the constructed crack-tip can reduce the numerical oscillation effect near the crack tip. Secondly, the crack-tip elements are implemented into WBEM. And the combination of WBEM and multi-domain technology can effectively handle interface cracks. Thirdly, the crack problem solving strategy based on multi-domain WBEM can directly evaluate the SIFs of cracks. Finally, several numerical examples involving homogeneous media and bi-material models are given to verify that the proposed method is simple and highly accurate.

Dielectric-Enhanced, High-Sensitivity, Wide-Bandwidth, and Moisture-Resistant Noncontact Triboelectric Sensor for Vibration Signal Acquisition.

Noncontact triboelectric sensors (TESs) have the potential to enhance self-powered sensing performance by eliminating the need for physical contact. This study demonstrates a strategy to construct noncontact TES that enables self-powered sensing and vibration signal acquisition with high sensitivity and wide bandwidth. The incorporation of carbon nanotubes into nitrocellulose (CNTs/NC) endows the tribopositive layer with larger inner micro/nanocapacitances, consequently augmenting the charge storage capacity. As a result, the contactless sensing performance of CNTs/NC-based TES (CNTs/NC-TES) was enhanced by 146%. Correspondingly, the related theory and working mechanism of noncontact sensing were demonstrated. Furthermore, the CNTs/NC-TES exhibits optimal distance response sensitivity of 57.10 V mm-1, a wide-bandwidth response from 0.1 to 4000 Hz, and relative humidity (RH) stability. This contactless CNTs/NC-TES has the potential for high sensitivity and wide frequency vibration monitoring in a high-RH environment.

An Investigation of the Energy Harvesting Capabilities of a Novel Three-Dimensional Super-Cell Phononic Crystal with a Local Resonance Structure.

Using the piezoelectric (PZT) effect, energy-harvesting has become possible for phononic crystal (PnC). Low-frequency vibration energy harvesting is more of a challenge, which can be solved by local resonance phononic crystals (LRPnCs). A novel three-dimensional (3D) energy harvesting LRPnC is proposed and further analyzed using the finite element method (FEM) software COMSOL. The 3D LRPnC with spiral unit-cell structures is constructed with a low initial frequency and wide band gaps (BGs). According to the large vibration deformation of the elastic beam near the scatterer, a PZT sheet is mounted in the surface of that beam, to harvest the energy of elastic waves using the PZT effect. To further improve the energy-harvesting performance, a 5 × 5 super-cell is numerically constructed. Numerical simulations show that the present 3D super-cell PnC structure can make full use of the advantages of the large vibration deformation and the PZT effect, i.e., the BGs with a frequency range from 28.47 Hz to 194.21 Hz with a bandwidth of 142.7 Hz, and the maximum voltage output is about 29.3 V under effective sound pressure with a peak power of 11.5 µW. The present super-cell phononic crystal structure provides better support for low-frequency vibration energy harvesting, when designing PnCs, than that of the traditional Prague type.

Intelligent Fault Diagnosis of Hydraulic Multi-Way Valve Using the Improved SECNN-GRU Method with mRMR Feature Selection.

Hydraulic multi-way valves as core components are widely applied in engineering machinery, mining machinery, and metallurgical industries. Due to the harsh working environment, faults in hydraulic multi-way valves are prone to occur, and the faults that occur are hidden. Moreover, hydraulic multi-way valves are expensive, and multiple experiments are difficult to replicate to obtain true fault data. Therefore, it is not easy to achieve fault diagnosis of hydraulic multi-way valves. To address this problem, an effective intelligent fault diagnosis method is proposed using an improved Squeeze-Excitation Convolution Neural Network and Gated Recurrent Unit (SECNN-GRU). The effectiveness of the method is verified by designing a simulation model for a hydraulic multi-way valve to generate fault data, as well as the actual data obtained by establishing an experimental platform for a directional valve. In this method, shallow statistical features are first extracted from data containing fault information, and then fault features with high correlation with fault types are selected using the Maximum Relevance Minimum Redundancy algorithm (mRMR). Next, spatial dimension features are extracted through CNN. By adding the Squeeze-Excitation Block, different weights are assigned to features to obtain weighted feature vectors. Finally, the time-dimension features of the weighted feature vectors are extracted and fused through GRU, and the fused features are classified using a classifier. The fault data obtained from the simulation model verifies that the average diagnostic accuracy of this method can reach 98.94%. The average accuracy of this method can reach 92.10% (A1 sensor as an example) through experimental data validation of the directional valve. Compared with other intelligent diagnostic algorithms, the proposed method has better stationarity and higher diagnostic accuracy, providing a feasible solution for fault diagnosis of the hydraulic multi-way valve.

Novel strategy of combined interstitial macrophage depletion with intravenous targeted therapy to ameliorate pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease with poor prognosis and high mortality rate. In the process of IPF, inflammatory dysregulation of macrophages and massive fibroblast aggregation and proliferation destroy alveoli, which cause pulmonary dysfunction, and ultimately lead to death due to respiratory failure. In the treatment of IPF, crossing biological barriers and delivering drugs to lung interstitium are the major challenges. In order to avoid the side effect of macrophages proliferation, we proposed, designed, and evaluated the strategy which combined macrophage depletion by intervaginal space injection and intravenous targeted therapy on bleomycin mouse model. We found that it inhibited pulmonary macrophages, reduced macrophage depletion in non-target organs, improved pulmonary drug targeting, impeded the progression of pulmonary fibrosis, and accelerated the recovery of pulmonary function. This combination therapeutic strategy shows good biosafety and efficacy, induces a targeted response, and is promising as a practical new clinical approach towards the treatment of pulmonary fibrosis.

A Domain Adaption ResNet Model to Detect Faults in Roller Bearings Using Vibro-Acoustic Data.

Intelligent fault diagnosis of roller bearings is facing two important problems, one is that train and test datasets have the same distribution, and the other is the installation positions of accelerometer sensors are limited in industrial environments, and the collected signals are often polluted by background noise. In the recent years, the discrepancy between train and test datasets is decreased by introducing the idea of transfer learning to solve the first issue. In addition, the non-contact sensors will replace the contact sensors. In this paper, a domain adaption residual neural network (DA-ResNet) model using maximum mean discrepancy (MMD) and a residual connection is constructed for cross-domain diagnosis of roller bearings based on acoustic and vibration data. MMD is used to minimize the distribution discrepancy between the source and target domains, thereby improving the transferability of the learned features. Acoustic and vibration signals from three directions are simultaneously sampled to provide more complete bearing information. Two experimental cases are conducted to test the ideas presented. The first is to verify the necessity of multi-source data, and the second is to demonstrate that transfer operation can improve recognition accuracy in fault diagnosis.

Refining the time-frequency characteristic of non-stationary signal for improving time-frequency representation under variable speeds.

Time-frequency ridge not only exhibits the variable process of non-stationary signal with time changing but also provides the information of signal synchronous or non-synchronous components for subsequent detection research. Consequently, the key is to decrease the error between real and estimated ridge in the time-frequency domain for accurate detection. In this article, an adaptive weighted smooth model is presented as a post-processing tool to refine the time-frequency ridge which is based on the coarse estimated time-frequency ridge using newly emerging time-frequency methods. Firstly, the coarse ridge is estimated by using multi-synchrosqueezing transform for vibration signal under variable speed conditions. Secondly, an adaptive weighted method is applied to enhance the large time-frequency energy value location of the estimated ridge. Then, the reasonable smooth regularization parameter associated with the vibration signal is constructed. Thirdly, the majorization-minimization method is developed for solving the adaptive weighted smooth model. Finally, the refined time-frequency characteristic is obtained by utilizing the stop criterion of the optimization model. Simulation and experimental signals are given to validate the performance of the proposed method by average absolute errors. Compared with other methods, the proposed method has the highest performance in refinement accuracy.

Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery.

Strong noise interference or compound fault coupling phenomenon may lead to the failure of fault diagnosis technology. This paper focuses on weak feature extraction and compound faults detection for rotating machinery fault diagnosis and proposes adaptive symplectic geometric mode decomposition (SGMD) using cycle kurtosis entropy. Firstly, an index named cycle kurtosis entropy (CKE) is presented to measure the strength of periodic impulses in a signal. The CKE uses the entropy value of calculating all delay cycle kurtosis (CK) to overcome the shortcomings of the CK in adaptive ability and obtain more stable values. Thirdly, CKE is applied to construct an adaptive slip window with optimal length. This process is called the adaptive window segmentation method, which is mainly used to dig out weak fault features in signals. Finally, CKE is used as the component selection criterion to select the components decomposed by SGMD. The selected components are reconstructed to obtain a de-noised signal. Hilbert envelope analysis is applied to the denoised signal to demodulate the fault characteristic frequency. Numerical simulations and experimental investigations using bearings and gears are performed to testify the property of the presented method. The results indicate that the adaptive slip window can enhance the decomposing ability of SGMD under strong noise condition. Moreover, for the strong periodic impulse identification ability, the cycle kurtosis entropy is suitable to determine the optimal components of SGMD. It is expected that the presented method will be effectively used for fault feature extractions in rotating machinery under stationary running conditions.

Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries.

The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles.

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps.

A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time-frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.

An engineered hydrogel with low-dose antitumor drugs enhances tumor immunotherapy through tumor interstitial wrap.

Stimulating immunogenic cell death (ICD) is the key to tumor immunotherapy. However, traditional chemoradiotherapy has limited effect on stimulating immunity and often requires repeated administration, which greatly reduces the tumor-killing effect. In this article, we created a sodium alginate hydrogel sustained-release system containing low-dose doxorubicin (Dox) and immune adjuvant R837, which were injected into the interstitial space to wrap around the tumor in situ, achieving a sustained release and long-lasting immune response. Cooperating with immune checkpoint blockade, Dox induced ICD, activated dendritic cells (DCs) and converted immunosuppressive M2-type tumor-associated macrophages (TAM) to tumor-killing M1-type TAMs. Simultaneously, it greatly promoted T cell proliferation and infiltration, and reduced tumor immunosuppressive factors, triggering a robust immune response to suppress tumors in vivo. In conclusion, this anti-tumor strategy based on interstitial injection can achieve continuous local immune stimulation by low-dose chemotherapy drugs, providing a potential approach for tumor immunotherapy.

Hydroxysafflor Yellow A Phytosomes Administered via Intervaginal Space Injection Ameliorate Pulmonary Fibrosis in Mice.

Idiopathic pulmonary fibrosis is a fatal interstitial disease characterized by fibroblast proliferation and differentiation and abnormal accumulation of extracellular matrix, with high mortality and an increasing annual incidence. Since few drugs are available for the treatment of pulmonary fibrosis, there is an urgent need for high-efficiency therapeutic drugs and treatment methods to reduce the mortality associated with pulmonary fibrosis. The interstitium, a highly efficient transportation system that pervades the body, plays an important role in the occurrence and development of disease, and can be used as a new route for disease diagnosis and treatment. In this study, we evaluated the administration of hydroxysafflor yellow A phytosomes via intervaginal space injection (ISI) as an anti-pulmonary fibrosis treatment. Our results show that this therapeutic strategy blocked the activation of p38 protein in the MAPK-p38 signaling pathway and inhibited the expression of Smad3 protein in the TGF-ß/Smad signaling pathway, thereby reducing secretion of related inflammatory factors, deposition of collagen in the lungs of mice, and destruction of the alveolar structure. Use of ISI in the treatment of pulmonary fibrosis provides a potential novel therapeutic modality for the disease.

Adaptive MOMEDA based on improved advance-retreat algorithm for fault features extraction of axial piston pump.

The fault information of axial piston pump bearings is inevitably submerged by violent natural periodic impulses. Therefore, an accurate extraction of fault impulses remains a challenging problem. A hybrid method of MOMEDA and TEO is proposed to extract periodic impulses in this study. Firstly, the deconvolution periods of multiple periodic components in the original vibration signals are analysed using Kurtosis. Then, an advance-retreat algorithm is used to optimize the filter length of MOMEDA. After multiple input parameters are determined adaptively, the MOMEDA is used to enhance the various periodic impulses respectively. Finally, TEO demodulation is employed to further obtain fault frequencies. Experimental vibration data is used to verify the advantages of this method for periodic impulses extraction. The results are then compared with traditional deconvolution and decomposition techniques to prove the superior performance of the proposed approach in terms of its better accuracy and reduced processing time.

A hybrid of FEM simulations and generative adversarial networks to classify faults in rotor-bearing systems.

Condition monitoring of rotor-bearing systems using artificial intelligence has great significance to guarantee the reliability and security of mechanical systems. However, in engineering applications, AI model will fail to classify faults with insufficient fault samples owing to complex working condition. A hybrid fault classification approach is presented by combining finite element method (FEM) with generative adversarial networks (GANs) for rotor-bearing systems. Firstly, FEM simulations are employed to calculate simulation fault samples as additional sources of missing fault samples. Secondly, GANs is used to acquire abundant synthetic samples generated from the simulation and measurement samples, which aims to expand fault samples. Finally, the complete fault samples, including simulation, measurement and their corresponding synthetic samples, are utilized as training samples to train typical classifiers, and further to identify unknown faults. High classification accuracies for a rotor-bearing system using different kinds of artificial intelligent (AI) models are obtained, which demonstrates the effective of proposed method. It is noticed that the present idea can be guided to solve insufficient fault samples problem in more complex mechanical system with agreeable fault classification accuracy.

Review of the species of Anastatus (Hymenoptera: Eupelmidae) known from China, with description of two new species with brachypterous females.

Fourteen species of Anastatus (Anastatus) Motschulsky, 1859 (Hymenoptera: Eupelmidae) are treated from China, of which A. (Anastatus) flavaeratus Peng and Tang n. sp. and A. (Anastatus) pariliquadrus Peng and Tang n. sp. are described based on brachypterous females. Two new synonyms are proposed, A. flavipes Sheng and Wang, 1997 under A. japonicus Ashmead, 1904 n. syn., and A. huangi Sheng and Yu, 1998 under A. gastropachae Ashmead, 1904 n. syn. The species previously reported from China under the name A. acherontiae Narayanan, Subba Rao and Ramachandra, 1960 is newly identified as Anastatus echidna (Motschulsky, 1863). Anastatus colemani Crawford, 1912, is excluded from the Chinese mainland fauna, and A. dendrolimus Kim and Pak, 1965, A. kashmirensis Mathur, 1956, and A. tenuipes Bolívar y Pieltain, 1925 are excluded from the Chinese fauna. Two previously recorded extralimital species, A. bifasciatus (Geoffroy, 1785) and A. colemani are treated as possibly present in China even though their presence was not confirmed and the records likely are based on misidentifications. Anastatus ramakrishnai (Mani, 1935), originally described from India, is compared with A. dexingensis Sheng and Wang, 1997 and A. formosanus Crawford, 1913, with the suggestion that the name could be synonymous with one of the latter two names. The males of nine species and females of all Anastatus species recognised from China are keyed, diagnosed, and illustrated. Information on recorded hosts and distribution is summarised for all the species.

TAM and MUSIC Approach for Impact-Source Localization under Deformation Conditions.

As an impact-source-localization technique, Lamb waves are commonly used to detect low-velocity impact in composite structures. However, the performance of Lamb waves is susceptible under deformation conditions. In this paper, a novel approach combined the Toeplitz approximation method (TAM) and multiple-signal classification (MUSIC) (TAM-MUSIC) to improve impact-source-localization (angle and distance in polar coordinates) accuracy under deformation conditions. The method divided a two-dimensional search of direction and distance into two one-dimensional searches. The impact direction was calculated by the TAM, which was introduced into the steering vector of MUSIC to estimate the distance by scanning the whole monitoring area. An epoxy laminate plate experiment showed that the phase and amplitude of uniform linear-array signals had different average plate curvature that led to poor impact-source-localization accuracy using the MUSIC method. TAM provided high-resolution direction-finding capability, suitable for the pretreatment of Lamb waves. Results showed that the present method, with a small amount of computation and low memory requirement, had higher location-estimation accuracy than that of traditional methods under deformation conditions.

Band Gaps and Transmission Characteristics Analysis on a Two-Dimensional Multiple-Scatter Phononic Crystal Structure.

In this paper, a novel wrap-around multi-scattering phononic crystal (PC) structure is proposed. Band gaps (BGs) and transmission characteristics of the present structure are calculated using finite element method (FEM). Through the calculations of single-scattering prototype, three complete BGs which are exhibited at low frequency and the fourth wide BG at high frequency are discovered. The transmission features and resonant spectra represented by frequency response function (FRF) shows that apparent resonance directly cause the four specific BGs. By keeping the total area of scatterers unchanged, 2 × 2, 3 × 3 and 4 × 4 scatterers are designed to obtain the change rule of BGs. Furthermore, the size ratio of 2 × 2 scatterers, the number of connection beams are investigated to obtain the regular pattern of acoustic energy transmission and attenuation. The present investigation of multiple-scatter PC structure will provide a solid support on the future design of acoustical functional materials.

A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine.

Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with acceleration sensors. However, faulty samples of such systems are difficult to obtain. Therefore, machine learning methods, such as support vector machine (SVM), neural network (NNs), etc., fail to obtain agreeable fault detection results through smart sensors. A personalized diagnosis fault method is proposed to activate the smart sensor networks using finite element method (FEM) simulations. The method includes three steps. Firstly, the cosine similarity updated FEM models with faults are constructed to obtain simulation signals (fault samples). Secondly, every simulation signal is separated into sub-signals to solve the time-domain indexes to generate the faulty training samples. Finally, the measured signals of unknown samples (testing samples) are inserted into the trained SVM to classify faults. The personalized diagnosis method is applied to detect bearing faults of a public bearing dataset. The classification accuracy ratios of six types of faults are 90% and 92.5%, 87.5% and 87.5%, 85%, and 82.5%, respectively. It confirms that the present personalized diagnosis method is effectiveness to detect faults in the absence of fault samples.

Design and biological evaluation of tetrahydropyridine derivatives as novel human GPR119 agonists.

A series of novel tetrahydropyridine derivatives were prepared and evaluated using cell-based measurements. Systematic optimization of general structure G-1 led to the identification of compound 35 (EC50 = 4.9 nM) and 37 (EC50 = 8.8 nM) with high GPR119 agonism activity and moderate clog P. Through single and long-term pharmacodynamic experiments, we found that compound35 showed a hypoglycemic effect and may have an effect on improving basal metabolic rate in DIO mice. Both in vitro and in vivo tests indicated that compound 35 was a potential potent GPR119 agonist in allusion to T2DM treatment.