Abstract:With the development of artificial intelligence technology, intelligentization has become one of the development trends of the future aeroengine industry. The intelligent aeroengine has become a research hotspot in recent years. Its development will bring a new round of technology revolution in the aviation industry. The background of the intelligent aeroengine is investigated, and two successful applications of the intelligent aeroengines are discussed. In addition, the research progress of intelligent aeroengine is summarized particularly in terms of intelligent control, intelligent health monitoring, fault diagnosis, etc. The challenge issues of intelligent aeroengine are outlined. Its development trend in the context of industrial big data are discussed, including data management, information exchange, information fusion, diagnosis timeliness, life prediction, etc.

Abstract:In order to solve the difficulty of UAV fault diagnosis and health management caused by the lack of fault data and fault label, this paper proposes a full digital simulation fault data generation method based on the simulation model of flight control system. In the paper, the fault modes and corresponding mathematical models of actuators and sensors are analyzed in detail. Fault injection and fault simulation on the University of Minnesota UAV model are carried out to generate fault data. Meanwhile, the impacts of different fault types, injection nodes and amplitudes on fault data generation are analyzed. A set of fault data generation methods based on simulation model is summarized. The proposed method utilizes the advantages of simple digital simulation operation, flexible fault injection mode and full demonstration of UAV mechanism. The proposed method can easily simulate the randomness of the fault occurrence time and failure mode during the UAV actual flight process, which has great significance to improve the present situation of lacking effective fault data in UAV fault diagnosis field.

Abstract:Spacecraft is a kind of extraordinary complex systems consisting of integrating structure, thermal control, power, attitude, orbit, and so on. Telemetry data is the only basis to judge the onorbit spacecraft performance on ground. The effective anomaly detection is a fundamental element to ensure the reliable operation of the spacecraft. In this paper, aiming at the data anomaly detection problems that the telemetry data are the mixture of continuous and discrete samples, and the sample variation is highly correlated with the instructions, a spacecraft telemetry data anomaly detection method is proposed based on ensemble longshort term memory (LSTM) network. The strong nonlinear modeling ability of LSTM is utilized; with matrix norm, the multiple mode mining of telecontrol instruction is achieved; through the construction and effective ensemble of the multiple LSTM prediction model, the adaptability of the model to the complicated spacecraft operating condition is improved; then, the anomaly in the telemetry data is effectively labeled. The telemetry data of two kinds of spacecraft from NASA are detected in experiment. The result indicates that the anomaly detection rate of the proposed method is promoted obviously compared with the telemetry data anomaly detection method based on LSTM, the proposed method is especially suitable for contextual anomaly discovery. The test results verify the feasibility of the proposed method, and the study provides an effective data interpretation ability for the ground operation and control of spacecraft.

Abstract:As the foundation of equipment health management, the degradation process modeling and prediction is an effective way to reduce running risk and maintenance cost. In order to solve the randomness, nonlinearity and multiphase complexity of the degradation process in practice, an adaptive modeling and prediction method for multiphase degradation process based on functional principal component analysis is proposed. This method treats the degradation measurement values as discrete sample values of continuous function, and thus converts the degradation modeling problem into functional data analysis problem. On this basis, this method uses the function principal component analysis method to reduce the dimensionality of the degraded data, and extracts the common information and individual difference information of the equipment degradation. With Bayesian reasoning, the online monitoring data is used to update the degradation model parameters to realize online realtime prediction of equipment health status. Finally, the proposed method is applied to the accelerated life test data of a cooling fan, and the effectiveness of the method is verified. The results show that the proposed method can well model the multiphase complex random degradation process and thus has potential engineering application value.

Abstract:Electrical current signal possesses the characteristics of being easy to collect and not easily affected by environmental noise, which provides a feasible monitoring and diagnosis idea for the special equipment that is difficult to collect signals with vibration sensor. However, the electrical current signal also has the problems of being difficult to extract fault features. An intelligent fault diagnosis method for mechanical equipment based on electrical current signal is proposed, which combines the improved dynamic statistical filtering and deep convolution neural network (DCNN). In order to improve the accuracy of state recognition, the integrated information quantity index (SIpq) is introduced to optimize the filtering effect, which can maximize the difference of the features among different state signals based on dynamic statistical filtering. Through alternately stacking the convolution layer with invariant size of the feature map and the pooling layer with decreased the size of the feature map layer by layer, the DCNN is constructed to extract the high dimension fault features in the electrical current signal step by step. The featureenhanced image samples after dynamic statistical filtering are input into the DCNN to identify the fault type. In order to verify the effectiveness of the proposed method, take three kinds of faults inclnding unbalance, misalignment and looseness of rotating machinery as objects, the fault type identification was carried out. The analysis results show that the proposed method can effectively identify the fault type. Compared with other methods such as ANN and CNN, the proposed method has better recognition accuracy.

Abstract:Power load prediction can provide reliable decisionmaking basis for power system planning and operation. With the development of smart grid, the amount of data collected by supervisory control and data acquisition increases largely, and the structure of data becomes more complex. Frequent changes of load and regional meteorological factors have influence on the accuracy of load forecasting. A shortterm load forecasting method is proposed in this study, which is based on elastic network (EN) for large data dimension reduction and flower pollination algorithm (FPA) for BP neural network optimization. By adding norms and norms to penalty items, the elastic network has the advantages of least absolute shrinkage and selection operator (LASSO) and ridge regression. It can solve the problem of dimension reduction effect, which is affected by collinearity and group effect in LASSO dimension reduction. Then, FPA is introduced to optimize BP neural network, in which the weights and thresholds are easily affected by initial values, slow convergence speed and easy to fall into local optimum. Compared with particle swarm optimization method, the optimization speed of flower pollination algorithm is faster and the effect is better. The proposed method has been applied for predicting power load. Experimental results show that the prediction accuracy can be effectively improved.

Abstract:To improve the prediction ability of auxiliary equipment fault, a fault warning method for power plant auxiliary equipment is proposed. It is based on improved stacked autoencoder network, which fuses the advantages of unsupervised learning methods in deep learning. The method takes the historical normal data as the training set and utilizes the nonlinear expression ability of the stacked autoencoder (SAE) network to indicate the relationship among the variables of the auxiliary equipment. The batch normalization (BN) algorithm is utilized to optimize network performance. For the input observation vector, SAE network offers the corresponding reconstruction vector. The similarity based on the fusion distance is constructed to represent the deviation between the observation vector and the reconstruction vector. When the auxiliary equipment starts to deviate from the normal state, the difference between the observed value and the reconstructed value increase. The similarity drops to the warning threshold, which indicates that the machine is fault. Normal data and fault data of a medium speed mill of a thermoelectric unit are used to test and verify the proposed method. Experimental results show that SAE network with BN algorithm has lower reconstruction error. The model can provide fault warning before the coal mill trips. Therefore, the method can effectively make fault warning of auxiliary equipment, which has certain engineering application value.

Abstract:The process of zinc coating weight control has the characteristics of nonlinear and multivariable. A new simulation and optimization analysis method is proposed to control the influence factors of zinc coating weight control, which is based on orthogonal experimental design, numerical simulation and response surface method. The hot galvanizing production process with the weight of single surface galvanizing layer between 100 and 400 g/m2 is taken as the research object. Orthogonal experimental and online data collection methods are used to collect sample data. The importance of each factor on the response index is ranked by range analysis. Based on the experimental results, the linear regression formula is derived and the orthogonal experiment with interaction columns is used to analyze the influence of factor interaction on the model. In this way, experimental results can be modified. Results show that the multiple correlation coefficient of the fitted quadratic regression equation model is R2=0997 6 and P<0000 1. According to the experimental results, the factors influencing the weight change of zinc coating is ranked as the order of jet pressure, strip velocity, interaction between jet pressure and strip velocity, nozzle distance, nozzle gap, and interaction between jet pressure and nozzle distance.

Abstract:In the structure of rotating piezoelectric energy harvester, the design of the centrifugal force allows the resonant frequency to passively follow the change of the rotational frequency. In this way, a wide frequency band can be achieved by selftuning. However, the performance of the harvester is very sensitive to the parameters. In this paper, the key parameters of the harvester are analyzed and optimized. The EulerLagrange method is used to establish the electromechanical coupling equation. The concept of tuning factor D is proposed. The design of multiple parameters is concentrated into the tuning factor, which can greatly improve the design efficiency of the harvester. The design of the tuning factor is studied by numerical simulation and experiment. Results show that when D≤1, the peak of bandwidth increase decreases with the increase of D. The performance decreases significantly when D≥1. Compared with the noncentrifugal harvester, when D=09, the peak is reduced by 316%. The 6 dB bandwidth is increased from 06 to 7 Hz, which is an increase of 117 times.

Abstract:With the wide application of robots in highend manufacturing industry, aerospace, medical care and other fields, the requirement of the full pose (position and orientation) accuracy is getting higher and higher. In this paper, laser tracker is used to measure the full pose of the robot endeffector. A robot accuracy improvement method is studied based on robot geometric parameter calibration. Firstly, the modified denavithartenberg (MDH) model of series robot is established. Secondly, an improved crow search algorithm (ICSA), which generates the initial positions of crows based on quasirandom sequences is proposed to optimize and calibrate the robot geometric parameters. The mathematical model of the objective function that is used to calibrate the robot geometric parameters with ICSA is established, and the detailed calibration steps are given. Finally, the Staubli Tx60 industrial robot was calibrated practically, the results prove that the proposed method can quickly calibrate the geometric parameters of the robot. After calibration, the average absolute position and orientation errors of the randomly selected test points in the robot workspace are reduced from 0309 6 mm and 0232 2° before calibration to 0092 6 mm and 0082 9° after calibration, and the accuracy is greatly enhanced. The proposed method is simple and easy to implement, and has high efficiency, strong robustness and good stability. The proposed method is suitable for the applications where the robots with both high position and orientation accuracies are required.

Abstract:In this paper, based on the China′s national time reference system of UTC (NTSC), the application study of BeiDou antenna phase center (APC) correction in precise point positioning (PPP) and high precision time comparison is carried out. Based on the data collected through BeiDou receiver, and the BeiDou precision products of clock, orbit and APC correction file downloaded from International GNSS Service (IGS) analysis center, the data processing of the BeiDou PPP is performed. The results show that the RMS of the PPP errors in X, Y and Z directions are about 0011 0, 0021 2 and 0009 5 m before APC correction, and about 0002 6, 0007 1 and 0003 7 m after antenna phase center correction, respectively. It can be seen that the positioning precision is improved obviously after APC correction. When the APC corrections are applied to the two receivers for the zero baseline common clock difference comparison, the standard deviation of the comparison results is decreased from 0148 2 ns before correction to 0093 0 ns after correction. For the long distance precision time comparison, the standard deviation of the comparison results is decreased from 0302 9 ns before correction to 0266 8 ns after correction. Therefore, with the construction of the BeiDou system and the continuous improvement of related BeiDou precision products from international GNSS service (IGS) analysis center, the service precision of BeiDou will be better and better.

Abstract:Based on the acoustic elastic effect, the utilization of critical refracting longitudinal waves to detect the residual stress of metal materials has a wide application prospect. However, the critical refracting longitudinal wave velocity has a weak acoustic elastic effect on stress, which greatly affects the resolution of material stress detection. To improve the detection accuracy of the critical refracting longitudinal wave velocity, a nonlinear decomposition technique (wavelet synchrosqueezed transform) is proposed to transform and reconstruct the critical refracting longitudinal wave. In this way, the ultrasonic arrival time can be calculated accurately. Taking the 1 060 aluminum plate as the research object, the waveform features of critical refracting longitudinal wave are analyzed based on the finite element simulation software. Based on these features, the ultrasonic signals in the experiment are adjusted to improve the waveform accuracy of the critical refracting longitudinal wave. Experimental results show that the propagation time of the critically refracted longitudinal wave in the 1 060 aluminum plate can be accurately measured, and the wave velocity is calculated with an error of no more than 005%. The validity of the accurate calculation method of critical refracting longitudinal wave velocity is verified.

Abstract:The distributed star sensor can be used as a monitoring platform for space targets. Multiple star sensors are needed to be associated instantly in the star map. The space target in the star map is a small target with no features (e.g., texture and color), which cannot be solved by the classical target association algorithm. Based on the correlation of polar geometry constraints, a space target synchronization association method under the distributed star sensor is proposed. The epipolar geometry constraint can describe the intrinsic projective relationship of multiviews, which has been widely used in image matching and stitching problems. It is applied in the target association field in this study. Based on the measured data, the association experiments of 300 star maps are carried out. The space target association probability can reach 90% and the feasibility of the algorithm is verified. The reasonable reference value of the associated threshold is proposed and various causes of the association error are analyzed. Monte Carlo is adopted to estimate the maximum probability of misjudgment. Experimental results show that the proposed method has an ideal association effect under the influence of jitter, measurement deviation and several interference points in the operation of the star sensor.

Abstract:In this paper a twostage vacuum ejector model is proposed, which is applied in the multieffect distillation with thermal vapor compressor (MEDTVC) system, and can improve the vacuum degree and operating efficiency of the system. A scale ratio of 1∶2 between the key dimensions of the two stage ejectors is proposed in the ejector structure design. In order to verify the structure and investigate the complex flow field inside the twostage ejector, the computational fluid dynamics (CFD) simulation software is adopted to carry out the modeling and simulation analysis of the ejector, and the inner flow field characteristics are presented. In order to investigate the influence of primary flow pressure on the performance of the ejector, the primary flow pressure was increased from 550 kPa to 630 kPa while the suction pressure and back pressure were fixed. It was found that the entrainment ratio of the ejector rises firstly, and then reaches a maximum value of 0073; after the maximum point, it begins to decrease with the increasing of the primary flow pressure. The twostage ejector can achieve a vacuum degree of around 58 kPa under the condition of primary flow pressure of 600kPa and back pressure of 100 kPa, which indicates that the proposed structure can effectively improve the operating efficiency and reduce energy consumption of the MEDTVC system.

Abstract:The infrared laser atmospheric transmittance measurement methods have the problems of high cost and low realtime performance. To address these problems, a method making use of the measurements of sunlight modulation sunphotometer to obtain the atmospheric transmittance of infrared laser is proposed. It is mainly based on measurements and research of analysis on analogy calculation. This method has the advantages of low cost, simple operation, and realtime performance. The infrared sunlight modulation sunphotometer can measure the atmospheric transmittance in wide band. Then, the relationship between the broadband transmittance and the narrowband transmittance can be revised. Finally, the applicable nearmid infrared multiband laser atmospheric transmittance can be obtained. The actual measurement results and comparison experiments are carried out. Compared with the near infrared sunphotometer developed by the laboratory, experimental results show that the statistical error is less than 4%. Compared with POM02, the statistical error is less than 6%. The realtime results obtained by infrared sunlight modulation sunphotometer can be used not only in the correction of laser atmospheric transmission, but also in the evaluation of laser communication and laser transmission.

Abstract:The bubble column is a multiphase flow reactor, which has been widely used in energy and environmental field. The size and concentration of bubbles are of great significance for studying the heat and mass transfer process in the bubble column. A digital image processing technique is proposed to measure the bubble size distribution in the bubble column. However, the dense bubbles are easy to overlap during the bubble recognition process. To solve this problem, the overlapping bubble matching and the circumferential fitting based on the curvature calculation are presented. The segmentation and contour reconstruction algorithm are further used to determine the size distribution of bubbles. Experiments are implemented in a bubble column. Experimental results show that the proposed algorithm can not only extract the clear and complete bubbles from the image, but also accurately segment the overlapping bubbles of the image. The multiscale bubble size distribution can be accurately obtained accordingly. As the gas flow rate increasing, the number of small bubbles increases sharply. At the same time, larger bubbles are also generated. The maximum diameter of the bubble and the average diameter of Sauter increase with the increase of the gas flow rate. But, their ratio remains basically the same. The configuration of the distributor has an influence on the uniformity of the bubble size distribution. The square distributor produces the most uniform bubble, and the gas holdup is higher than those of the other two types of distributors. These results verify the feasibility of the image segmentation and contour reconstruction method for the measurement of bubble parameters in gasliquid twophase flow.

Abstract:The failure frequency and maintenance cost of wind turbine gearbox are relatively high. It is necessary to monitor its operation condition in realtime. Nonlinear state estimation technique (NSET) has the problems of high dependence on memory matrix, low accuracy caused by insufficient data utilization, bad realtime performance, etc. Therefore, a condition monitoring method based on soft fuzzy Cmeans clustering (SFCM) and ensemble NSET is proposed. SFCM is adopted to divide the historical data into different classes with overlapping boundaries to achieve the soft condition division. NSET models under different conditions are constructed as individual learner. The parametric regression method is used as the combiner. Lots of data can be used to train the parameters without affecting the realtime performance and the accuracy can be improved accordingly. The gearbox fault data of a 2 MW wind turbine are taken to evaluate the method. Compared with NSET, experimental results show that the proposed method has better accuracy and realtime performance. Through the means of predicted residual and the health index based on residual, it can reflect the early fault and its development trend of gearbox sensitively and accurately.

Abstract:Aiming at the operation difficulty of disease manual detection on the bottom of the bridge, the working principle of the bridge inspection robot is introduced. Combined with the structural characteristics of the bridge and the constraints of visual inspection shooting parameters, the operation planning and position & pose optimization method of bridge inspection robot are studied. Firstly, a position and pose planning method for shooting operation of bridge inspection robot with the best shooting model constraint is proposed. Based on the optimal shooting planning method, aiming at the folding structure on the bottom of the small box girder bridge and T beam bridge, a shooting position and pose optimization method with the safety shooting model as constraint is studied. The weight function combining the shooting angle and shooting distance is designed. The optimization algorithm formula is derived and the convergence provement is given. Then, through the configuration of different shooting parameters, the simulation of the shooting operation position and pose planning method for hollow slab bridge is carried out. At the same time, on the basis of the simulation results of the position and pose planning, the simulation of the position and pose optimization method is also carried out. Finally, taking the developed bridge inspection robot as the target, field test and verification were carried out. The simulation and experiment results show that the proposed planning and optimization method meets the requirements of bridge shooting detection, and has good robustness and realtime performance.

Abstract:In the general application of bathymetric sidescan sonar, the amplitude information or phase information of the echo data is used alone to obtain a sidescan map or bathymetric map to show the seafloor detailed features. In order to extract the microgeomorphic information inside the sidescan data and realize a higherprecision seafloor terrain detection, a twostep loop iterative algorithm is proposed. Firstly, the primeval bathymetric data and sidescan data are used to optimally fit the scattering model. Secondly, the brightness error correction factor is introduced to the improved shape from shading method and iterate the terrain, which ensures fast and stable convergence in terrain iteration. Finally, through loop iteration, the seafloor sediment parameters and the terrain depth value that has higher precision and stronger correlation coefficient with the real terrain relief are obtained. Meanwhile, the Jackson seafloor scattering model is used to simulate the signal transmission and reception processes of the bathymetric sidescan sonar, and the echo data are used to verify the correctness and effectiveness of the iterative algorithm described in this paper. The results show that the proposed method can effectively correct the terrain, and the higher the received signaltonoise ratio is, the better the terrain correction effect will be. When the signaltonoise ratio is to 20 dB, compared with the original bathymetric result, the corrected correlative coefficient of terrain relief is elevated by 524% and the absolute value of the terrain error is reduced by 37%. At last, the algorithm is applied to the bathymetric sidescan sonar data. The comparison and analysis of the terrain maps before and after correction verifies the feasibility and effectiveness of the proposed algorithm.

Abstract:Intelligent diagnosis and prognosis techniques have been widely applied in modern industrial practice. However, there still exist same limitations as follows: 1) the techniques take the identical type faults with different degradation degree as different individual fault patterns for classification and identification, which is unreasonable in practical industry application; 2) the diagnosis model based on the training with specific data lacks generalization ability under varying working conditions. Aiming at above mentioned problems, a multitask feature sharing neural network is proposed and applied to the intelligent diagnosis and prognosis of bearings. Firstly, the CNN is used to construct an adaptive feature extractor, which extracts deep features from raw vibration signals. Secondly, a multitask feature sharing diagnosis model is constructed for classification and prediction, and the fault classification and fault size prediction are realized. Finally, the proposed method is verified with the benchmark bearing dataset from Case Western Reserve University (CWRU). The experiment results show that the proposed method not only can realize the task of fault type classification and fault size prediction, but also possess strong generalization ability.

Abstract:The existing indoor crowd counting methods face the problems limited scenarios, and low detection accuracy, etc. A crowd counting method based on deep neural networks without carrying equipment is proposed in this study. Multiple wireless fidelity (WiFi) sensor nodes are employed to cover indoor areas. The crossover WiFi link data are obtained by detecting signals among sensor nodes. Deep neural network is utilized to learn and extract the features of the effect of the change of the indoor crowd number on WiFi signals. The crowd counting model is trained for the indoor area, and it can be used to estimate the number of crowd by inputting realtime WiFi signals into the model. Evaluation experiments are implemented in a complex indoor office environment. Results show that the proposed method can realize accurate crowd counting with an accuracy of 8223% and the mean error of 037 people. Compared with other machine learning methods, the deep neural network perception model has higher detection accuracy and can be applied to various application scenarios.

Abstract:The collected vibration signal of hob in engineering site is contaminated with noise. It is difficult to extract features contained in vibration signal. In this study, ensemble empirical mode decomposition (EEMD) is applied to denoise vibration signals. To solve the problem of selecting and processing of intrinsic mode function (IMF) after EEMD decomposition, a denoising method of hob vibration signal based on grey criterion and EEMD is proposed. Firstly, the original signal is decomposed into several IMF components by EEMD. Then, according to the proposed grey criterion, each IMF component is processed by polarity consistency and mean processing. The grey correlation between IMF1 and other IMF components is calculated. All IMF components are arranged in descending order according to the grey correlation degree. The first half of IMF components in the descending order are selected for soft threshold processing. Finally, processed IMF components, unprocessed IMF components and residual components are reconstructed to obtain the denoised signal. The feasibility and validity of the method are verified by the simulation signal with different initial signaltonoise ratios and the vibration signals of the hob in actual machining. Meanwhile, the proposed method is compared with EEMD combined with correlation coefficient and wavelet soft threshold denoising. Experimental results show that this method has better denoising effectiveness.

Abstract:Large leak location errors under the low signaltonoise ratio (SNR) of leakage vibration signal in watersupply pipeline (WSP) are considered in this study. Leak location based on the combination of variational mode decomposition (VMD) and crossspectral analysis is proposed. First, the leak signal is decomposed into several intrinsic mode functions (IMFs) using VMD. The characteristic frequency band can be determined using crossspectral analysis of the water supply pipeline leakage signal. Then, the effective IMF components are determined by using the energy ratio of the IMF component in the characteristic frequency band as a selection criterion. The selected effective IMF components are reconstructed to improve SNR. Finally, the reconstructed signals are used to estimate time delay to find a leakage position. To verify the effectiveness of the proposed method, the leak location based on combination of VMD and crossspectral, crosscorrelation, and combination of VMD and correlation coefficient are compared in simulation and experiment respectively. Results show that the average relative location errors of the above three kinds of leak location algorithms are 253%, 862%, and 1686% respectively.

Abstract:The relative motion between the moving parts and the shell of the checkweigher and the resonance of mechanical natural frequency point under external force result in the vibration disturbance. To address this problem, the variable step size least mean square (LMS) adaptive notch filter based on improved versiera algorithm is proposed. Compared with the traditional antivibration methods, such as movingaverage filter, Butterworth lowpass filter, Butterworth notch filter and fixed step size LMS notch filter, the variable step size LMS adaptive notch filter is proved to be accurate and superior in filtering vibration interference by analyzing the checkweigher′s vibration characteristics. The method combined with movingaverage and asymmetric tailcutting mean filtering is used to finalize the result on the checkweigher at various speeds. Experimental results show that the average error is ≤0171 g and the standard deviation is ≤0240%, which can meet the requirement of category XIII(1).

Abstract:A hydraulic oil metal particle detection sensor based on the dualsolenoid coil with casing structure is proposed. Theoretical analysis shows that the casing structure sensor can couple the magnetic field intensity of external solenoid coil and utilize the mutual inductance between double solenoid coils. In this way, the inductance variation of metal particles through the detection area is increased effectively. Inductance variation, average noise, and signaltonoise ratio are selected as metrics for measuring the detection sensitivity. Comparative experiments show that the casing structure sensor not only does not increase detection noise, but also is superior to single solenoid structure sensor in terms of inductance variation and signaltonoise ratio for the detection of same metal particles. With the increase of iron particle size, the superiority of detection ability is more obvious. When it detects iron particles and copper particles ranging from 110 to 120 μm, the inductance variation increases by 24 times and 17 times respectively. Compared with those of single solenoid structure sensor, the signaltonoise ratio increases by 18 times. The lower limit of particle detection experiments show that the casing structure sensor can detect iron particles with diameter larger than 30 μm and copper particles with diameter larger than 90 μm. The research has reference significance for improving the detection sensitivity of solenoid inductance sensor.

Abstract:The micro gripper requires large stroke and low coupling. To fulfil these requirements, a novel flexurebased micro gripper is designed with threestage amplifier. The bridgetype mechanism and levertype mechanism are also utilized. Based on the principle of flexible mechanics, elastic mechanics and dynamics, the dynamic features of micro gripper are improved by optimizing the fixed position of the bridgetype mechanism. Moreover, the theoretical displacement amplification model, coupling error model and dynamic model of micro gripper are formulated. The output displacement feature and dynamic feature of the micro gripper are obtained. The micro gripper is manufactured by the combination of 3D printing and wire cut electrical discharge machining. The correctness of the theoretical model is verified by comparing the theoretical, simulation and experimental data. The displacement amplification of the proposed micro gripper is 197, and the natural frequency is 223 Hz. All the errors with experimental data fluctuate within 10%. The working stroke of micro gripper is 750 μm while the coupling error is only 035% within the working stroke. The micro gripper has the features of large stroke, high precision, low coupling and good dynamics.

Abstract:Having excellent characteristics of nondestructive testing, noncontact measurement and etc., eddy current sensor is widely used in microdisplacement measurement, conducting medium defect detection and equipment operation condition monitoring in various industrial production fields. However, limited by the technical bottlenecks such as coil structure parameter optimization, detection circuit innovative design and measurement error dynamic compensation, the existing eddy current sensor generally has prominent limitations such as poor sensitivity, low linearity and being urgently improved detection precision under abrupt change temperature field, which directly restricts its popularization and application in the fields of highprecision detection under various extreme environments. Therefore, based on deeply analyzing and summarizing the research and application status of eddy current sensor both at home and abroad, this paper concentrates on the coil structure, detection circuit and error compensation method. The paper emphasizingly discusses the basic principles and key technologies for eddy current sensor core performance optimization, preliminarily outlines and forecasts the development trend of the related research, and hopefully provide effective reference for improving sensor performance in multiple dimensions and promoting its development and application.

Abstract:In order to measure the influence of extracellular Ca2+ concentration on the mechanical properties of human red blood cells, a measurement method of cell membrane shear modulus based on optical tweezers is proposed. An optical tweezers system is integrated on the inverted microscope, the optical tweezers system can utilize the dual traps to transversely stretch and manipulate the human red blood cell attached with microspheres, and analyze the relationship between the cell deformation and the optical trap force, and then the shear modulus of the cell membrane is measured. Through measuring the shear modulus of the red blood cell membrane in the CaCl2 solution with different concentrations, the influence of extracellular Ca2+ concentration on the mechanical properties of red blood cells is analyzed. The results show that compared with normal red blood cells in PBS, the mean shear modulus of red blood cell membrane treated with CaCl2 solutions with concentrations of 100, 200, 300, 400 and 500 μM decreases by 78%, 203%, 344%, 484% and 593%, respectively. In order to verify the measurement results, a mechanical model of red blood cell is established and a uniaxial stretching force is applied to simulate the uniaxial stretching force applied to the cell. The simulation results are consistent with the measured results. This method provides a reference for the measurement of other cellular mechanical properties.

Abstract:In the application of ultrasoundguided minimally invasive interventional surgery, it is difficult to track the surgical needle in real time and accurately only using ultrasound images. In this paper, the electromagnetic positioning system combined with ultrasound image is used to realize the task of puncture navigation for minimally invasive interventional robot. The ultrasound image is used to determine the position of the local suspicious lesion area, while the electromagnetic positioning system is used to perform the real time positioning and tracking of the surgical needle. In order to combine them organically, an error compensation method based on Bernstein polynomial is proposed to compensate the tracking error. The Nline model method is used to calibrate the ultrasound image. On this basis, the registration of the surgical needle and the image space is completed, the concept of virtual needle insertion path is proposed, and then the fusion of electromagnetic positioning data and image data is realized. In order to verify the effectiveness and feasibility of the proposed method, the experimental study on the combination of ultrasonic system and electromagnetic positioning system was carried out. A prostate minimally invasive interventional robot platform was built, and the targeted puncture positioning experiments were carried out from various angles. The experiment results show that under the premise of using nylon as robot body material and titanium alloy surgical needle, using this method, the average precision of prosthesis puncture is 114 mm and the average accuracy is 162 mm, which can meet the clinical accuracy requirement of prostate interventional surgery.