Abstract:To solve the low measurement accuracy and poor resistance to environmental interference in the ultra-large radius measurement field, a laser reflection differential confocal ultra-large radius measurement system is developed. The system adopts reflective measuring optical path, and uses the curve of the differential confocal system to precisely locate the cat􀆳s-eye and reflection positions of the tested device. The displacement information of the tested device is recorded by interferometer, and then combined with the vertex focal length of the objective lens to calculate the ultra-large radius value. Theoretical analysis and experimental results show that the system has high focusing accuracy and large measurement range and high anti-environment interference ability, and its relative measurement error is less than 2×10 -4 , and its measuring repeatability reaches at 1. 2 × 10 -4 for - 15 m ultra-large radius, and the system realizes the highprecision measurement of ultra-large curvature radius.

Abstract:When testing the analog-to-digital converters (ADCs), coherent sampling is required. However, it is difficult to achieve, so spectrum leakage will occur. In order to suppress leakage, the most common method used is windowing. The method is so strict that it is not suitable for the current high-precision ADC test, and the user should be very familiar with the window function. In this paper, a method for selecting the appropriate window function type and the span is proposed. Through the proposed method, accurate test results can still be obtained when the first side lobes of the window function are higher than the noise floor and the application range of common window functions is expanded. This makes common window functions, like Hanning window, can also be applied to 18-bit high-precision ADC test work. The simulation results show that the proposed algorithm can still obtain accurate test results over any level of noncoherency, and the maximum error of each parameter is less than 1 dB. The experimental results of a commercial 18-bit ADC are used to verify the feasibility of the proposed algorithm.

Abstract:In order to meet the high-precision measurement requirements of the ultra-long position parameters of rocket skids, a collaborative network measurement method using a laser tracker and a total station was designed, which can integrate the advantages of the both systems. The coordinate control field is introduced to construct the constraint field and the constraint equation. The weights are used to optimize the weights, and the Levenberg-Marquard optimization algorithm is used to solve the optimal solution of the equations. Tests were performed under field conditions and the accuracy was analyzed and compared with the standard length of the reference ruler. The results show that the measurement error is kept at 3 mm within the range of 3 km, which realizes high-precision and high-efficiency measurement of ultra long distance position information.

Abstract:The monitoring of offshore oil spills is becoming increasingly important, but the various methods available are mostly used in the monitoring of large-scale oil spills. In view of the monitoring of underwater small oil spills and initial oil spills, this paper proposes to use the four pins at the micro USB end of the USB data cable as electrodes to measure the impedance, and the mutation of the impedance indicates the detection of oil droplets, that is, the monitoring of seawater oil spills is realized. The standard deviation for measuring 400 impedances in salt water is 0. 058 Ω. The three types of oil (gasoline, diesel and lubricating oil) and salt water were distinguished, and the impedance change from salt water to oil increased by at least 418% and the response time was less than 0. 19 seconds. This method can realize the high-sensitive and rapid monitoring of oil spills in seawater, and has a broad application prospect in the future.

Abstract:It is difficult to estimate the probability distribution and uncertainty of measurement results when the process of evaluating the uncertainty of coaxiality error measurement is complex and the error distribution is unknown. Based on a small amount of existing data, the probability density of the measured data is calculated by the support vector machine method (SVM), and the numerical integration of the obtained probability density distribution is carried out to calculate its estimation and standard uncertainty. The experimental results show that under the condition of small sample data, the best estimation and the accuracy of variance of the samples obtained by SVM are proved. Finally, the coaxiality error of the machine tool core shaft is taken as the experimental object, and the mentioned results are used as the experimental object. Methods to calculate the measurement uncertainty and the measurement uncertainty representation guide (GUM), Monte Carlo method (MCM) calculation results are compared to verify the simplicity, reliability and accuracy of the method.

Abstract:For the issues of false oil level in the measurement of the transformer oil conservator, the aging of the insulating oil and oil compensating after power failure, the paper proposes an oil level measurement and charged oil compensating device of transformer based on pressure sensor. Through installing dual pressure sensors in the oil storage tank drain pipe and using two-point differential pressure algorithm to accurately measure the oil level, The device can reduce repeating maintenance caused by false oil levels. Then, through the separation of oil flow and pump body, vacuum cycle exhaust system, separation of oil inlet and oil filling process, the oil storage of the transformer oil tank can be realized without power failure. The device can reduce the number of power failure maintenance, and effectively improve the overall operation reliability and user experience of the equipment. Through the transformation and comparison of the existing transformer oil conservator of the State Grid Baoding Power Supply Company, the measurement accuracy and practicability of the device were verified.

Abstract:The dynamic error of CMM measurement process restricts the improvement of measurement efficiency in industrial field. For this purpose, a dynamic error compensation method for high-speed measurement process of CMM is proposed to improve measurement accuracy and efficiency. In order to make the research typical and representative, the mobile bridge CMM widely used in the market is taken as the research object. By establishing an error separation platform to analyze the dynamic characteristics of the high-speed measurement process of the measuring machine, four parameters that can characterize the dynamic errors of the measurement process are determined, namely maximum positioning error (MPE), residual positioning error (RPE), maximum approximation error ( MAE), residual approximation error (RAE). Orthogonal experiment method was used to analyze the influence degree of common influence factors (positioning speed, positioning distance, approaching speed, approaching distance) on dynamic parameters. Training samples and test samples were obtained by measuring standard spheres with coordinate measuring machine. The training samples and test samples are used to model and compensate the dynamic errors of the measuring machine. The results show that the dynamic process errors are reduced by 88. 8%, 80. 2%, 90. 8% and 71. 3% respectively after compensated by the fuzzy neural network model. It proves that the fuzzy neural network model can effectively improve the dynamic measurement accuracy of the measuring machine.

Abstract:Aiming to meet demand of the multi-channel and high-speed eddy current testing equipment used in current industrial production line, a multichannel online eddy current testing system is designed. The testing system is composed of eddy current signal processing circuit, multi-channel data acquisition system, real-time control computer based on Vxworks operating system, data management computer and relative software. The excitation frequency of eddy current signal processing circuit varies from 300 Hz to 3 MHz. The range of adjustable sensitivity is 0 ~ 72 dB. With 200 K sample rate for every channel, multi-channel data acquisition system can acquire the data from four eddy current signal channel simultaneously, which is able to work on the 1 200 m/ min production line with through-type eddy current probe. Transferred through PC104 bus to real-time computer, the data is identified and labeled by procedure. The data management computer displays the impedance plane according to the data. To adapt to different testing condition, it is also responsible for the parameter adjustment. In thin-wall copper pipe testing experiment, the testing system shows multi-channel data acquisition ability, high SNR and good effect for 0. 6 mm minor defect testing.

Abstract:The traditional underwater active laser scanning system uses ordinary camera to receive and record reflected light. The reflected laser point is buried in the background noise and cannot be separated, which affects the image quality. In this paper, the position and direction information of laser reflected light and scattered background stray light are recorded by light field camera in underwater laser active scanning imaging system, providing the possibility to suppress scattering background stray light. In the subsequent processing of light field recording image. Firstly, the recorded light field images are refocused twice. The first is the refocusing of the laser light spot on the object surface and the second is the refocusing of the scattered light in the foreground of the laser light spot. Then, the differential processing is applied to the two refocusing images, and a computer automatic judging process is proposed to make the contrast of laser point on the differential image maximum or large enough. The experimental results show that the above method can effectively suppress the scattering background, and it will be a necessary supplement to the existing space separation scattering background suppression technology of synchronous scanning active laser imaging system.

Abstract:In order to extract the wind turbine bearing fault signal submerged under environmental and structural noise, a recursive variational mode decomposition method is proposed based on the energy tracking method, and the particle swarm optimization algorithm is used to solve the optimal constraint factor to obtain the accurate modal component. Based on the nonlinear fractal, the theory proposes a multifractal spectral feature factor to select the best modal component. Taking the fault degree and the loaded bearing acceleration signal as the object, the optimized recursive variational mode decomposition is used to obtain multiple components. The effective information component is selected by the maximum value of the multifractal spectral feature factor, and the fault classification is performed by the support vector machine. The results show that the optimized recursive variational mode decomposition can accurately decompose the vibration signal to different frequency bands for fault information extraction; the multifractal spectrum feature factor is positively correlated with the signal to noise ratio, and the component selected by its maximum value has more effective information; The BPNN is used to classified the hybrid fault degrees of different states, the test samples are constructed by selected components by IVMD-MFC with eight nonlinear characteristics. The diagnostic accuracy is 97. 5%. There is a good performance in hybrid fault degree of different status classification.

Abstract:The catenary is powered by single-phase industrial-frequency power dividing into multiple sections, the acquisition of power feeder voltage, neutral-section voltage and other electrical signals are the foundation to ensure the train automatic-passing the neutralsection. Aiming at the fault of electrical signal measuring circuit caused by sensor insulation aging, the fault diagnosis and fault-tolerant control method of ground automatic passing neutral-section’s measuring circuit is proposed. Based on the mechanism analysis of different measuring circuits, such as power feeders and neutral-section, this method gives a fault diagnosis method based on the mutual check of circuit information, and constructs the corresponding fault-tolerant control scheme according to the fault characteristics of different measuring circuits. Finally, the results of simulation and practical project show the effectiveness and reliability of the proposed method.

Abstract:Aiming at the problems of inadequate accuracy in the prediction of multi-chip module life using traditional gray model and the significant decrease in prediction accuracy with the increase of time span, a Markov-tail double residual correction method for multi-chip component life prediction based on grey model is proposed. Based on the predicted value of grey GM ( 1,1) model, the residual optimized by Markov method is used as the input value of the tail segment grey residual model to achieve double residual correction. An example is given to predict the life of multi-chip module by using resistance values which influence the life of multi-chip module with a small amount of test data obtained under the condition of accelerated thermal cycle test. The experimental results show that compared with the traditional grey model and neural network prediction methods, the average residuals of the proposed method are reduced by 80. 469% and 68. 53% under the small samples condition, the prediction accuracy is improved, the result is more reliable and the life prediction of multi-chip modules can be more accurate.

Abstract:In order to solve the problem that traditional control method is difficult to achieve high-precision grinding of complex surfaces, this paper designs the impedance control method of complex surface grinding robot based on nonsingular terminal sliding mode in Cartesian coordinate system. Firstly, according to the impedance model parameters of the system, the set grinding trajectory is transformed into the executable impedance trajectory at the end of the robot. Then a control method based on nonsingular terminal sliding mode is designed to track the impedance trajectory. For the chattering problem of sliding mode control, the exponential approach law is used to weaken it. The stability of the system is proved based on Lyapunov theory. Simulation and cylinder grinding experiment results show that the method can achieve convergence in a limited time and avoid the control singularity, and improve the robustness and accuracy of the control system.

Abstract:The orthogonal fundamental mode fluxgate has the advantages of wide frequency band, low noise and small volume compared with the traditional parallel excitation second harmonic fluxgate. In order to meet the application requirements in geomagnetic navigation, electromagnetic tracking and other fields, and further increase the response bandwidth, a digital quadrature fundamental mode fluxgate sensor simulation model was established, and a hardware circuit composed of high-speed ADC, FPGA and high-precision DAC was built. The related FPGA program was developed, the correctness and feasibility of the digital demodulation scheme were verified. After testing, the digital quadrature fundamental mode fluxgate sensor has a bandwidth of 15 kHz, which has obvious advantages, and shows a good prospect in the intelligentization of orthogonal compensation and gain calibration.

Abstract:Printed circuit board (PCB) is sensitive to electrostatic discharge, which affects the normal use of electronic product. In order to obtain the energy coupling law of electrostatic discharge (ESD) with PCB, ESD test was applied to the simplified PCB. The effect of the discharge position, characteristics of the PCB trace and discharge voltage on the coupling of ESD electromagnetic field was studied by measuring the coupling voltage on the PCB trace. The test results show that the discharge position, trace width, length and termination resistor will affect the magnitude and trend of coupling voltage and there is a good linear relationship between the discharge voltage and the coupling voltage. Based on a full wave model that is also established in compilation simulation technology microwave studio, it further nstrates the test results from the standpoint of simulation. The research results have certain guiding significance for PCB electromagnetic compatibility design.

Abstract:The traditional TIADC timing mismatch chopping modulation calibration cannot be extended to multi-channel. The improved timing mismatch chopping modulation calibration algorithm adds the single channel and the adjacent channel output to detect the timing mismatch. It can be applied to the detection of arbitrary channel timing mismatch. Compensation is implemented by first-order Taylor approximation, which avoids the complex filter design in traditional algorithms. However, when the input signal frequency exceeds the Nyquist frequency of sub-channel, the calibration direction of the calibration algorithm will be wrong, resulting in the failure of calibration system. Therefore, this paper proposed a new calibration direction correction algorithm that can meet the effective calibration of the Nyquist frequency range of the whole system. Simulation results show that when applied to a 4-channel, 1 GS / s, 12-bit TIADC and when the input signal frequency is 450 MHz, the signal-to-noise distortion ratio ( SNDR) of the system is increased from 28. 4 to 73. 1 dB, and the spurious-free dynamic range (SFDR) is increased from 30. 7 to 88. 9 dB.

Abstract:Because of the characteristic of generating any signal programmatically, the arbitrary waveform generator is an ideal signal source in the study of signal and system, so it is very important to traceable calibration. In this paper, a calibration system consisting of arbitrary waveform generator, cable, adapter and digital oscilloscope is constructed. The traceable calibration of arbitrary waveform generator is realized by using predistortion algorithm. The predistortion calibration method is based on the idea of compensation. Firstly, a simulated ideal signal is loaded to arbitrary waveform generator, which can be traced by oscilloscope. The measured signal contains the distortion of arbitrary waveform generator and transmission device. This distortion can be calculated by the difference between the measured signal and the ideal signal. According to this distortion, the ideal signal loaded to arbitrary waveform generator is adjusted, and finally it is realized that the traceable calibration of arbitrary waveform generator, and makes the signal input to oscilloscope become an ideal signal. In practice, this is an iterative process. In this paper, the condition of the end of the iteration is designed, that is, until it is close to the measurement accuracy of the digital oscilloscope. In order to verify the validity of the calibration method, this paper loads 50 sine waves of equal amplitude and constant phase into arbitrary waveform generator. After three iterations, the maximum relative error is reduced from 10. 408 9% to 2. 305 3%. This shows that the calibration method studied in this paper is effective. After calibration, the signal source system of arbitrary waveform generator can be used as a standard signal source for the calibration of measuring instruments.

Abstract:The multi-target detection and recognition of optical remote sensing images have always been the hot researching topics in image processing and analysis. The multi-target classification and recognition algorithm based on multiple features and single classifier cannot make good use of the adaptability of features and classifiers, resulting in a problem that the accuracy of recognition is difficult to improve. A multi-feature multi-classifier fusion optical target image recognition algorithm based on D-S evidence theory is proposed. Two features with translation and scaling invariance are extracted. Secondly, three classifiers are introduced to classify the feature. Finally, a two-level fusion algorithm scheme by using D-S evidence theory is proposed, and a confusion matrix that characterizes the performance of the classifier is introduced in the calculation process of confidence function. The proposed algorithm is effectively resolved the classifier output uncertainty problem, and further improves the accuracy of multi-target classification and recognition of optical remote sensing images. The recognition rate of multi-objectives by DS evidence theory fusion strategy reaches 97. 22%. The effectiveness of the algorithm is proved.

Abstract:The particle flow velocity is an important parameter of gas-solid two-phase flow. This paper presents a new method for measuring solid particle velocity based on planar array capacitive sensors. Firstly, the 3D electromagnetic field simulation software Ansoft is used to model the sensor, and the spatial axial sensitivity is obtained. Based on the distribution of the axial sensitivity, the spatial filtering characteristic of planar array capacitive sensors is investigated, and the mathematical expression of the velocity measurement is derived. Then a measurement system based on planar array capacitive sensor for measuring the velocity of solid particles is designed and experimentally evaluated on the gravity transport experimental device. The experimental results indicate that the particle velocity is linear with the spectral width of the sensor output signal when the particle flow velocity is between 1 m/ s and 3 m/ s. The relative error of the velocity measurement is less than 9%, and the repeatability error of the test system is within 11%.

Abstract:A joint infrared video pedestrian method is proposed based on Gauss background modeling and convolution neural network, to address the problems of inability to extract foreground targets and low detection rate in traditional method. Firstly, for continuous sequence infrared images, the foreground targets are extracted by the mixture Gaussian model. Then, for the foreground targets which are occluded by pedestrians, the valley bottom of the luminance curve is used as the segmentation point. While, the single pedestrian target area is separated by directional projection. Finally, the determined region of interest is input into the trained LeNet-7 system. Experiments on three different test sets demonstrate that the proposed method has good detection effect. Compared with the traditional method, the detection rate of the proposed method is over 99%, and the false alarm rate is as low as 0%.

Abstract:In view of the imperfect air quality monitoring network and low level of monitoring refinement, an open air quality monitoring system is proposed. Integrating the advantages of small sensor air quality monitoring equipment and large air quality monitoring equipment, a large data intelligent cloud platform is constructed. Based on mobile location and data correlation, the automatic calibration of small sensor monitoring equipment is realized, and the zero drift problem of small sensor monitoring equipment is solved. An intelligent air quality prediction algorithm is proposed based on information fusion technology. It realizes the real-time control of the air quality of the specific location by ordinary users. The experimental results show that the system has low mean square error and high accuracy, and can effectively and automatically calibrate the monitoring equipment.

Abstract:In order to meet the requirements of the drive system for the accurate estimation of the speed of traction motor in the fields of speed sensorless control, abnormal diagnosis and prediction of speed sensor, etc. , a real-time speed estimation method based on frequency adaptive phase-locked loop is proposed. Firstly, to solve the problem that traditional phase-locked loop ( PLL) algorithm cannot track the frequency in a large range, a second-order generalized integral phase-locked loop algorithm with feed-forward reference frequency self-adaptive adjustment (RFSOGI-PLL) is proposed to track the fundamental frequency of stator current in a large range in real time. Then, using the relation model of speed of traction motor, fundamental frequency of stator current and cogging harmonic, together with the structural parameters of traction motor, the traction motor speed of the drive system can be estimated in a large range in real time. Finally, the effectiveness of proposed algorithm is verified on a high-power AC drive system. The simulation and field test results show that, compared with the traditional PLL algorithm, the RFSOGI-PLL algorithm proposed in this paper can effectively improve the adaptability of the algorithm to track frequency change, and meet the requirements of traction motor speed estimation in celerity and estimation error.

Abstract:Analog-to-information converter (AIC) become the next generation of the core technology of Analog to digital due to lower than the Nyquist sampling rate. AIC processes the input signal by random demodulator, thus the AIC system has typical time-varying characteristics which results in the mismatch between the theoretical model and the actual circuit model. Aiming at this problem, based on switched capacitor as the core component of AIC, using the linear periodically time-variant (LPTV) theory analysis periodic timevarying AIC system which is converted into a Linear Time invariant system, pushing the transmission function of the AIC system, the circuit design method of AIC theory model is established. Experimental results show that the circuit design method makes the theoretical system transfer function well-matched with the actual system transfer function, which fully verifies the effectiveness of the design scheme. The reconstitution result proves that the sampling rate of the system can be reduced to 25% of the original Nyquist rate using this circuit structure and the successfully reconstructed signal-to-noise ratio is up to 39. 7 dB.

Abstract:Multi-Hole orifice pressure differential flowmeter has been widely used due to this ability to quickly adjust the flow field and effectively reduce pressure loss, but it still lacks complete structural parameter design and performance optimization design criteria. In this paper, the actual flow test of the proposed symmetric porous orifice differential pressure flowmeter is carried out, and its measurement performance is compared with the numerical simulation results. The results show that the symmetric porous orifice differential pressure flowmeter can effectively improve the measurement accuracy ( ± 0. 5%), reduce pressure loss, have better adaptability. Test results are consistent with numerical simulation results, verify the correctness of numerical simulation and calibrate the new flowmeter, the research results can effectively broaden the porous orifice flowmeter The scope of application.

Abstract:A modified fast empirical wavelet transform (MFEWT) based on compromise threshold function was proposed in order to solve the problem of improper segmentation caused by soft threshold function in fast empirical wavelet transform (FEWT). For this method, the trend spectrum is firstly calculated by Fourier transform and inverse Fourier transform and the result of calculation is optimized by wavelet denoising with compromise threshold function. Then, filter bands are built with optimized trend spectrum and the reconstruction of EWT empirical modes are made according to filter bands. With the fusion of kurtosis and Pearson correlation coefficient, characteristic components are selected. With minimum entropy deconvolution (MED), characteristic frequency of signal reconstructed by characteristic components can be calculated. Fault diagnosis of rolling bearing is finished with the comparison between characteristic frequency in experiment and theory at last. Results of experiment demonstrated that MFEWT performed better than FEWT in signal decomposition. For MFEWT, peaks of characteristic frequency in envelope spectra are clearer. The MFEWT improves the performance of signal decomposition of EWT and the reliability of rolling bearing fault diagnosis.

Abstract:Hyperspectral images have the characteristics of multiple bands and strong correlation among bands, which leads to information redundancy of hyperspectral images, resulting in dimensionality disaster and difficulty in classification. Therefore, a dimensionality reduction classification method of hyperspectral images based on LDA and extreme learning machine is proposed. In this method, hyperspectral data are firstly processed by LDA for dimensionality reduction, so as to overcome the problem of hyperspectral image information redundancy and keep the image feature information as far as possible. After reducing the spectral image dimension, ELM is adopted to classify and identify hyperspectral remote sensing images. The method proposed is applied to Pavia University and Salinas hyperspectral image processing, and the classification accuracy reaches 98. 78% and 99. 94% respectively, which effectively improves the feature classification performance of hyperspectral images and has strong practicability.