Abstract:Abstract:Aiming at the problems that current multidimensional force sensors can hardly simultaneously take into account electromagnetic interference, complicated wiring, large dimensional coupling and low sensitivity, this paper presents a twodimensional force sensor based on fiber Bragg grating. Firstly, based on the theories of fiber Bragg grating sensing and bending deformation of simply supported beam, a sensor elastic body with cross beam structure is designed, and the mapping relationship between the fiber grating sensor wavelength drift and the force is derived. Then, the finite element analysis method is used to study the strain distribution characteristics of the elastic body and determine the optimal sticking position of the fiber Bragg grating. Finally, an experiment calibration platform was established to conduct experiment research on the sensitivity, linearity, interdimensional coupling error and repeatability error of the sensor. The experiment results show that the wavelength sensitivities of the sensor in the x and y directions are approximately 7464 and 7520 pm/N, the linearities are approximately 3% and 2%, the interdimensional coupling errors are approximately 101% and 0218% and the repeatability errors are approximately 4898% and 5828%, respectively. The sensor can be applied to twodimensional force measurement of the wrist of the robot and has great significance for improving the highprecision control feedback of the robot joint.

Abstract:Abstract:In order to solve the problems of force coupling effect and measurement error of torque sensor caused by overturning torque, axial force and radial force, a novel capacitive type torque sensor is designed based on the principle of structural decoupling. The novel capacitive type torque sensor uses the builtin crossed roller bearing to achieve structural decoupling, thereby the influence of force coupling effect on measurement accuracy is eliminated. The measurement section adopts the principle of vertical plate capacitance effect and uses double differential measurement mode to improve the sensitivity and stability of the sensor. A commercial torque speed sensor was used as the standard torque meter to calibrate this novel capacitive type torque sensor and conduct decoupling verification experiment. The calibration and experiment results are as follows: the linearity of the novel capacitive type torque sensor is 104%, the resolution is 005 N·m and the maximum measurement error is about 207%, which indicates that the designed sensor can effectively reduce the influence of force coupling effect and possesses the features of structural decoupling, high sensitivity and high accuracy.

Abstract:Abstract:“Joule balance method” is a Planck constant measurement method based on mechanical electromagnetic energy balance. Due to the existence of a “feature vector alignment energy error” caused by the nonalignment state of the coil feature vector, the measurement uncertainty of the Planck constant can hardly be reduced down to 1×10-8. In order to realize the accurate compensation of the alignment energy error, aiming at the precise measurement issue of the residual horizontal displacement during the vertical movement process of the energy balance coil, a differential linesurface displacement sensing measurement method based on the electromagnetic selfdamping direct vertical measurement reference is proposed in this paper. In order to avoid the introduction of the system error in the adjustment process of indirect measurement reference, which leads to the reducing of the measurement accuracy, the linesurface displacement sensor uses a thin wire electrode as the direct vertical measurement reference. The differential capacitance structure of three electrodes is designed to inhabit the nonlinearity of the singleend capacitor. The surface shape of the other two electrodes is designed as mutual opposite outer cylinder, which uses the geometric shape isotropy of the cylinder surface in the cross section to offset the influence of attitude tilt on the measurement results, so as to improve the adaptability of the sensor to complex measurement conditions. And an electromagnetic damper is used to stabilize the thin wire electrode and ensure the accuracy of the measurement results. The experiment results show that the synthetic extended uncertainty of the differential selfdamping displacement sensor is about 281 μm within the normal working range.

Abstract:Abstract:Tactile information is one of the important ways for robots to perceive the working environment and a key factor of ensuring safety and comfortability in humanrobot collaboration. However, compared with the development of visual, auditory and olfactory sensors, the application and industrialization of robotic tactile sensors still relatively lag behind. This paper proposes a robot flexible tactile sensor based on the principle of the electrical doublelayer capacitor. The pressure sensor has the advantages of simple structure, high sensitivity, large measurement range, high flexibility, high signal to noise ratio, low fabrication and use cost, etc. The sensor consists of upper and lower two layers of electrodes and a fiber layer of intermediate ion gel. When external pressure is applied to the sensor, the ion fiber layer is compressed, and the contact area between the electrodes and the ion fiber layer is increased, which results in the increasing of the sensor capacitance. The experiment data shows that the pressure and capacitance have a good linear relationship, and the sensitivity of the sensor is high, which can reach 397 nF/kPa, the pressure measurement range is wide, which is up to 500 kPa, the maximum hysteresis error is 597%, and the dynamic response can properly follow the external pressure stimulus. The proposed tactile sensor could be widely applied to the situations such as robotic sensing, humanrobot interaction, wearable equipment, and so on, and has great academic and industrial value.

Abstract:Abstract:In order to realize the robot kinematic parameter calibration, a method is proposed to acquire its real time terminal attitude information using inertial measurement unit (IMU). However, during the robot dynamic attitude measurement with IMU, there exist some problems that affect the accuracy of measurement, such as the superposition of harmful accelerations (other acceleration information except gravity acceleration signal) in the accelerometer signal, the difficulty in obtaining the statistical characteristic parameters of noise and the drift of gyroscope signal with time. Aiming at these problems, an improved algorithm of adaptive extended kalman filter (EKF) for attitude measurement is designed. Based on the EKF model, this algorithm firstly constructs the first level measurement noise variance matrix, sets up weighted factors and reduces the influence of harmful acceleration signals on measurement result. Secondly, the idea of fading memory factor is introduced in SageHusa adaptive filter algorithm to track the measurement noise of sampling data in real time and construct the second level measurement noise variance matrix. Finally, the quaternion algorithm of attitude updating is adopted to conduct data fusion and correct the error caused by gyroscope signal drift. The experiment results show that compared with the SageHusa adaptive filter algorithm, the improved selfadaptive EKF algorithm can effectively improve the attitude measurement accuracy; the mean absolute errors of pitch and roll at peak height are reduced by 50% and 3643%, respectively, and the mean absolute errors of pitch and roll at peak valley are reduced by 1428% and 1944%, respectively.

Abstract:Abstract:Aiming at the flexible tactile and sliding composite sensing problem of bionic skin, a twolayer distributed sensing unit based on fiber Bragg grating is proposed. Firstly, the sensing principle of fiber Bragg grating is studied and the temperature compensation principle based on reference grating is deduced. Then, polymer elastic material is used for the protective and sensitized encapsulation of the fiber Bragg grating sensing array. The doublelayer “cross” distributed sensing array unit is designed. The pressure sensitivity of the sensing array unit is studied and analyzed in simulation. Finally, an experiment system platform was established. The temperature, tactile and sliding sensing experiments on the flexible composite sensor were conducted. The results of simulation and experiment show that the flexible tactile and sliding sensor has good linearity and response speed. The temperature sensitivity of the sensor is 13 pm/℃, which is 131 times of that of the bare fiber Bragg grating. The positive pressure sensitivity is 8294 nm/MPa. The x positive shear force sensitivity and x negative shear force sensitivity are 0060 2 and 0063 6 nm/N, respectively. The sliding information of object can be obtained from the characteristic signal of sliding sensing. The sensor is expected to be surface mounted or embedded in the bionic hand to realize the perception of tactile and sliding information of the bionic hand, which has some application value.

Abstract:Abstract:Drive shaft status monitoring is an important part of the health management of power machinery equipment. The realtime acquisition of speed and torque is its primary step and data foundation. Aiming at the problems of sensor installation, signal transmission, realtime and continuity in status monitoring, this paper proposes a batteryfree and leadless realtime measurement method based on roundcapacitive grid. The structure and principle of the round capacitive grid are described. The principle of capacitive coupling signal transmission is analyzed. The differentialcombinationconfiguration and installation method of the round capacitive grid are given.The optimal number of round capacitive grid electrodes is determined by simulation as 20. Finally, the speed and torque measurement of the drive shaft is carried on the turbo pump hydraulic test bench. The speed waveforms at 300 and 600 r/min and the waveforms corresponding to three different torque values at 300 r/min are obtained. Five different time points are selected during the experiment, the speed and torque datas collected at each time point by the round capacitive grid and the strain gauge torque sensor are compared, and the data consistency is good. The experimental results show that the round capacitive grid can be used to measuring the torque of the drive shaft.

Abstract:Abstract:Outofround of wheel profile is an important factor affecting the highspeed running of rail locomotives. Combing with the design of latemodel underfloor wheelset lathe, this study focuses on the online turning and measuring profile of wheel tread. An onmachine measurement and evaluation method for the outofround of the wheel profile is studied, and an embedded onmachine measurement system based on the numerical control system is developed. In this work, the measurement model of the wheel tread, data processing, wheel center positioning, and evaluation methods are discussed. The dynamic extraction and separation method of the wheel profile is also proposed to strengthen the onmachine measuring accuracy of measurement device. In addition, the order refinement analysis method is used to solve the physical resolution and measurement accuracy of the loworder polygon, which can improve the evaluation accuracy of the outofround of the wheel profile. Finally, experiments verify that the repeatability of the onmachine measuring has reached micron level. This work can effectively improve the measurement and evaluation of the locomotive wheel profile, and it can also promote the work performance of the underfloor wheelset lathe.

Abstract:Abstract:To improve the spatial resolution of confocal Raman system, a radialpolarized pupilfilter confocal Raman microscope system (RPCRS) is developed. The radial polarized light generator (Splate) is used to convert the linearly polarized light from the laser into radially polarized light. Then, a pupil filter is utilized to modulate the radially polarized light to realize the compression of the Raman excited spot. Finally, the spatial resolution of confocal Raman microscope system is improved by a pinhole to eliminate the Raman scattering light outside the focus. This system has high spatial resolution and lateral sensitivity. The periodic scribes and the standard steps are used as samples. Experimental results show that the spatial resolution of RPCRS can reach 240 nm, the lateral sensitivity is 465% higher than that of common confocal Raman microscope system, and the uncertainty of transverse measurement standard is 1887 nm. This system can realize high spatial resolution imaging of confocal Raman microscope system.

Abstract:Abstract:Atomic force microscopy (AFM) is an important tool for observing and manipulating samples at micronanometer scale. Compared with traditional contacting mode and tapping mode AFMs, the nonresonant tapping mode AFM has got wide applications due to its high accuracy control force and the ability to acquire multiple mechanical characteristics simultaneously. In this paper, adopting the method based on combining the background subtraction and synchronization algorithm, a homebuilt nonresonant tapping mode AFM is built. A general noise simulation model is established for the position detection circuit, the position detection circuit noise is optimized, so that the precision of the minimum controllable force is improved, which makes the minimum controllable force smaller than 50 pN. The morphology characterization of the standard silicon lattice was performed to verify the imaging performance of the system. And the characterization of various mechanical properties of composite materials, such as morphology, adhesion force and deformation was carried out to verify the effectiveness of the system and the imaging method.

Abstract:Abstract:Aiming at the online testing needs of optical elements with various optical aperture sizes during processing stage, a compact transient interferometric measurement system used for online testing is proposed. A polarization camera is applied to realize the transient phaseshifting interferometric measurement of wavefront and reduce the effect of environmental disturbance. Combining the subaperture stitching technique based on configuration optimization algorithm, the accuracy requirement for the motion scanning platform is lowered and the fullaperture surface testing of large aperture optical elements can be achieved. To verify the feasibility of the proposed system, the online testing and subaperture stitching measurement of the centering tool of diamond turning machine and a large aperture spherical mirror were carried out, respectively. The results show that compared with the detection results of ZYGO interferometer, the absolute deviations of the corresponding root mean square values for the two cases are 0003 and 0007 μm, respectively. The system features compact layout and insensitivity to external environmental disturbance. The proposed system can nicely meet the application requirements of online installation and testing in complex environmental condition, and has wide application in online adjustment of centering tool of diamond turning machine and online testing of the optical components with various aperture diameters.

Abstract:Abstract:The application of programmable Josephson quantum voltage standard (PJVS) in electrical measurement completes the transition from physical reference standard to natural reference standard of voltage unit volt, which has the characteristics of high accuracy and high stability. Based on the double channel quantum voltage signals, the ac ratio with quantum precision can be constructed to realize the highprecision quantum reproduction of arbitrary ac voltage ratio, which can replace the traditional ac ratio technology. Through driving the SNS type double channel Josephson junction array, double quantum voltages with precise amplitude and adjustable phase can be synthesized simultaneously. The twochannel differential sampling technique is adopted to reduce the influence of step transition process on the amplitude recovery of quantum voltage, and combining the channel armchanging technique the highprecision measurement of quantum voltage ratio is realized. The standard uncertainty of the ac quantum voltage ratio measurement is 009 μV/V, and the ac quantum voltage ratio output accuracy is better than 005 μV/V. The accuracy of the ac quantum voltage ratio was verified through comparison measurement with the conventional inductive voltage divider (IVD). Finally, the applications of the ac quantum voltage ratio technology in two electromagnetic metrology fields of power measurement and ac impedance bridge are discussed.

Abstract:Abstract:Aiming at the problems of excessive sampling time interval and being subject to environmental disturbance existing in the pointdiffraction interferometric threedimensional measurement system based on a PZT phase shift, a pointdiffraction interferometric system used in transient threedimensional (3D) measurement is proposed. Through light beam polarization modulation and introducing polarization camera in the pointdiffraction interferometric system for 3D measurement, the transient phase shift interferometric measurement of 3D space coordinate is achieved. Aiming at the field of view (FOV) error existing in polarization camera and its influence on 3D measurement accuracy, the field of view (FOV) error calibration method based on phase interpolation is studied. In order to verify the feasibility of the system scheme, both numerical simulation and 3D online measurement comparison experiments were carried out, respectively. The experiment results show that with the proposed system, the transient threedimensional space measurement accuracy of the order of submicron can be achieved and the measurement repeatability is less than 5%. The proposed transient pointdiffraction interferometric system has the features of being not sensitive to environmental disturbance, high repeat measurement precision and rapid measurement, which provides a feasible method for the transient measurement of threedimensional displacement and dimension without guide rail.

Abstract:Abstract:As the exploration and development focus of the oil and gas industry shifts from conventional reservoirs to unconventional reservoirs, comprehensive understanding of petrophysical properties and rock microstructure of unconventional reservoirs is of great significance to implement efficient measures for construction, enhancement and stabilization of production. Facing to the problems of diversified oil and gas reservoir space and sophisticated mechanism of fluid seepage in rock porous media, traditional petrophysical property test and rock static microstructure description fail to meet the research needs of oil and gas seepage and reservoir production enhancement. NMR technology has the characteristics of nondestructive and harmless, which directly reflects the pore fluid distribution and indirectly reflects the pore structure change through MR signal, and possesses significant advantages in the aspects of petrophysical test, pore structure characterization and pore fluid identification. Starting from the principle of NMR method, this paper emphatically introduces the application progress of the NMR technology in the fields of petrophysics and pore structure characterization, summarizes the existing problems in application and looks into the future development direction of this technology.

Abstract:Keywords:plate heat exchanger corrugated plate; corrugation depth; laser displacement sensor; noncontact measurement; moment; multithread

Abstract:Abstract:Outlier detection is an important part of data processing in thermal process, and is also the basis for system modeling, optimization and control. Aiming at the problem that the operational condition of the thermal process changes frequently, which causes the difficulty of outlier detection, this paper proposes a thermal process outlier detection method combining signal decomposition method and densitybased detection method. Firstly, the empirical wavelet transform method is used to extract the operational trend of the thermal process time series. After removing the sequence operational trend, the local outlier factor method is used to obtain the local outlier values for the data points. Finally, the box plot method is used to determine the sequence outlier points. The load data of the 1000MW unit in a certain power plant was used as the experiment data, five errors of 05%, 1%, 2%, 5% and 10% were set respectively to verify the effectiveness of the method. The experiment results show that besides having applicability to both dynamic and steady state processes, the outlier detection method proposed in this paper achieves high detection accuracy under the above five error conditions.

Abstract:Abstract:In order to improve the imaging quality and efficiency of synthetic aperture focusing technique (SAFT), a frequency domain SAFT ultrasonic imaging algorithm based on phase circular statistics vector (PCSV) is proposed. Firstly, the Hilbert transform and Euler′s formula are used to extract the instantaneous phase of the signals in wave field record. Secondly, the real part and imaginary part of the instantaneous phase are regarded as the PCSVs distributed in circular complex plane, and through wave field extrapolation, the phase coherence factor used for frequency domain SAFT weighting is constructed. Finally, the high quality frequency domain SAFTPCSV image is obtained through weighting processing. The imaging results show that the frequencydomain SAFTPCSV imaging algorithm effectively enhances the image resolution and signaltonoise ratio. Under the same test conditions, the operational time and occupied memory space of the frequencydomain SAFTPCSV imaging algorithm are less than 1/46, and 1/47 of those of traditional timedomain SAFT algorithm, respectively.

Abstract:Abstract:The pit on the cylindrical surface is one of the important indicators of cylindrical coated lithium battery circumference surface defect detection. There are two interference factors in the detection of craters on the circumferential surface. The image has uneven brightness along the circumferential and axial directions, and oil contamination on the circumferential surface. To solve these problems, a method for pit defect detection based on machine vision is proposed. Firstly, the gray scale distribution curve is extracted along the axis of the circumference. Then, the gray level difference model is used to extract gray discontinuous points in gray distribution curve, which is not sensitive to light distribution and oil contamination. The extraction threshold of discontinuous points are determined according to the reflective feature of the circumferential surface. In this way, the pit defect detection is achieved. The algorithm is evaluated on the selfbuilt image database SUTBY. Experimental results show that false rejection rate and false accept rate are both 0 percent. The actual test results indicate the uneven brightness has no influence on pit extraction, and there is no false detection caused by oil pollution.

Abstract:Abstract:Aiming at the problems in automobile height regulator production that manual defect detection is laborintensive and timeconsuming, and traditional diagnosis method has poor applicability, an intelligent detection method based on improved convolution network is proposed using deep learning. In this method, convolution network is used to extract features, and residual network structure and separable convolution are added to the network, which improves the accuracy of deep network and reduces the parameter calculation amount. The improved structure mainly uses convolution layer, pooling layer, batch standardization layer and softmax layer, and introduces residual network structure and separable convolution. The experiment results show that the defect detection method for automobile height regulator based on improved convolution network has good recognition accuracy. In the detection experiment on multiple kinds of defects for automobile height regulator, the accuracy is above 99%, which is superior to that of the classical convolution network VGG16.

Abstract:Abstract:The key of monocular vision based 3D displacement measurement is to obtain the camera pose parameters, which can be achieved by solving a perspective n points (PnP) problem. In order to improve the accuracy of PnP algorithm, this paper proposes an improved weighted Iterative EPnP algorithm (WIEPnP). WIEPnP intends to reduce the influence from sign point depth and image noise on the algorithm performance. It is done by setting weight coefficients for sign points and then conducting iterative calculations. MATLAB simulation experiments were carried out for comparative study with 6 PnP algorithms. The results show that the newly proposed WIEPnP can effectively reduce the impacts from the sign point depth and the effect of image Gaussian noise, respectively; and its accuracy and computation time satisfy the field application requirements. Later, the prototype experiments also verify the effectiveness of WIEPnP. In the prototype experiments, measurement errors in x and y directions are convinced to be less than 1 mm. In terms of z direction, the WIEPnP algorithm can effectively reduce the effect of depth changes; thus, the absolute error in z direction is restricted to no more than 3 mm. It can be seen that the WIEPnP algorithm proposed in this paper has good performance in terms of realtime and error; and it can meet the requirements for most realtime 3D displacement measurement.

Abstract:Abstract:Aiming at the nonlinear distortion of the image caused by the multiinterface refraction of underwater imaging, this paper proposes a method for correcting underwater perspective projection images, which converts the underwater images taken by the camera through multiple interfaces into the perspective projection images of the camera in the air. First, under the multilayer plane refraction model, the imaging process of the underwater camera is modeled in the form of a fourdimensional light field parameterization, and the mapping relationship between the underwater image point and the corrected image point is calculated from the direction information of the light field. Then, using the mapping relationship between the two, the images acquired by the underwater camera are converted into perspective projection images in the air. After simulation and optimization, the average error of the perspective projection image with an object distance of 2 m to 4 m is within 1 pixel. The experimental results also show the reliability and accuracy of the method. When the object distance is 2 m, the average error of the corrected perspective projection image is 056 pixel, and the average error of row matching is 04 pixel.

Abstract:Abstract:An automatic dimension measurement method of the scratch images is proposed based on the level set and the anisotropic diffusion. The segmentation and dimension measurement are conducted on scratch images that are obtained by optical microscope. In the segmentation, the distance regularized level set evolution (DRLSE) is used to segment the scratch. The anisotropic diffusion method is utilized instead of Gaussian filtering as the denoising method of the edge indicating function. The initial level set is manually set and evolved to the whole scratch boundaries. The contour coordinates are achieved by the subpixel adjustment. In the dimension measurement, the scratch boundaries are used to measure the width distribution and scratch length. Meanwhile, the average scratch width and scratch area are obtained. The influence of evolution parameters on measurement results are discussed by experiment. Under the optimal parameters of evolution, the relative measurement errors of the average scratch width and scratch area are less than 006% and 01%. Compared with the traditional DRLSE, experimental results show that the proposed method has advantages of precision and accuracy in the average scratch width and scratch area.

Abstract:Abstract:Aircraft sheet metal parts need to be painted after forming. The classification and recognition of a large number of different kinds of sheet metal parts after batch painting has been performed manually, which is a tedious and difficult work. An imagebased crossgrained recognition method for aircraft sheet metal parts is proposed to deal with this tough problem. A specific image collection platform is designed and constructed to take the images of the sheet metal parts in both top and side angles. The images of the sample parts, together with the extracted tendimensional (10D) feature vectors composed of shape factors and invariant moments, are stored in a database for the later use of recognition. According to the features of numerous kinds of sheet metal parts and the existence of groups of sheet metal parts with high similarity, the coarsegrained recognition method is designed. Through traversing and comparing the 10D feature vectors, the 2 candidate targets of the given sheet metal part to be recognized with top similarity are found from the database. Then, the finegrained recognition method, which is a manmachine cooperation process, is used to achieve the finally recognition of the expected sheet metal part. In the experiments on 20 different kinds of aircraft sheet metal parts, the proposed method achieves a recognition accuracy of 960%, and the whole recognition procedure is simple, convenient and efficient.

Abstract:Abstract:The requirement of algorithms for prior knowledge is inconvenient. To solve this problem, an autodetection and autorecognition method is proposed, which needs no prior knowledge. To detect the dial area, a rough detection algorithm is designed based on an ellipse detection algorithm and nonmaximum suppression to detect possible areas of the meter. Then, a line segment detector is enhanced by designing the preprocessing and the method of obtaining gradient values and levelline angles. And a centripetal angle constraint is added to the detector when seed points are selected to detect centripetal lines to filter the right area of the meter based on the number of centripetal lines. In the method of recognizing meters, an ellipse fitting method in which points are randomly selected from several sectors is proposed to fit inner ends of centripetal lines to identify scale lines. The segment fusion conditions are set to detect the pointer. The method based on maximally stable external regions is used to detect regions of interesting (ROIs). After identifying ROIs, neighborhood ROIs are connected to combine scale values, which are linked to their nearest main scale lines′ angles. Finally, the reading of the meter is obtained according to the angle of the pointer and the relation between values and angles. Experiments show that the total running time of the algorithm is 063 s, and the probability of error in one scale and in two scales are 80% and 967%, which can meet the actual requirement.

Abstract:Abstract:To realize online detection of local flatness of workpieces with planar features (e.g., cap of power battery), a flatness detection method based on point cloud is proposed. First, the point cloud is acquired by a 3D laserscanning sensor, and the unit normal vector of the main plane in the workpiece is achieved by a clustering algorithm. The point cloud is rotated to fulfill tilt of the point cloud alignment. Then, the rotated point cloud is transformed into a grayscale image. Template matching is performed with the grayscale image of the standard workpiece to obtain the offset angle and deflection angle between the measured workpiece and the standard workpiece. In this way, the point cloud is calibrated. A point set is intercepted from the aligned point cloud in a given region, and the flatness estimation is implemented using the least square method. The proposed method is applied to the online flatness detection of the automotive battery terminal plate. It is compared with the methods based on the ICP algorithm and the threecoordinate measuring instrument. Experimental results show that the proposed method can save 62% of the time, which also has consistent detection results with two methods. As a simple, efficient and reliable technique, the proposed method has broad application prospects and reference value in the field of precision parts manufacturing.

Abstract:Abstract:Stochastic resonance has the advantages of extracting bearing fault feature frequency and converting noise energy into signal energy. To solve the problem of unsatisfactory performance caused by inadequate selection of models and parameters, a model of asymmetric piecewise linear stochastic resonance system is proposed. The model is assumed to have two steady states. The analytical relationship and the output signaltonoise ratio formula of the model are derived. Meanwhile, the model is compared with the symmetric piecewise linear stochastic resonance system in terms of formula analysis and numerical simulation. The advantages of the system are proved. Then, the model is applied to bearing fault detection, and the adaptive genetic algorithm is used to optimize the parameters of the system. In the inner and outer fault detection for the symmetric system, the amplitude frequency values are 5079 and 1303, respectively. In the inner and outer fault detection for the asymmetric system, the amplitude frequency values are 3918 and 1893, respectively. Results show that the model has excellent detection effect, which proves that the model has great potential in fault detection.

Abstract:Abstract:During the process of drilling, the strong vibration and rapid rotation of bottom drilling tools make the attitude measurement signal contain multifrequency and highamplitude interference. The weak original signal amplitude and low signaltonoise ratio are difficult to be extracted in the field of measurement while drilling. To solve this problem, a Duffing chaotic oscillation detection method for weak signal recognition is proposed in this paper. Firstly, the frequency reconstruction of the measurement signal is realized by scale transformation. In this way, the measured signal can satisfy the restriction of frequency parameters. Then, to solve the influence of the initial phase of the measured signal on the accuracy of the detection model, Duffing oscillator detection model with full phase coverage is achieved by changing the initial phase of the driving signal. Finally, the threshold of Duffing oscillator entering the chaotic state is determined by adjusting the amplitude of the driving signal. The amplitude and phase parameters of the signal are estimated. The test results show that the root mean square error of inclination detected by chaos is 069%, and the relative error of the field-drilling is within ［107%, 208%］, which are higher than the results of original measurement data and standard Kalman filter. The feasibility and effectiveness of the proposed method has been proved.

Abstract:Abstract:Due to the cyclic motion, different working beats and transient speed, it is difficult to effectively describe the running state and assess the health state of industrial robot harmonic reducer. In this study, a method based on integerperiod data and convolutional neural network is proposed to achieve health state assessment of harmonic reducer. Firstly, the phase difference spectrum correctioncross correlation method is used to adaptively segment the original vibration signal and construct the integerperiod data samples to accurately describe the running state information of the harmonic reducer. Secondly, continuous wavelet transform is applied to decompose the integerperiod data sample to obtain the timefrequency map to fully show the transient characteristics of harmonic reducer in the operation cycle. Finally, convolution neural network is utilized to translate and scale the input signals in time and space with high invariability to fully learn the transient characteristics of the harmonic reducer in each operating cycle. In this way, the health state of the harmonic reducer can be evaluated. Experimental results show that the identification accuracy of the proposed method is over 90%. The effectiveness of the proposed method is verified, which has good generalization ability and robustness.

Abstract:Abstract:In the actual onedimensional signal processing, the mathematical morphological filtering method (MMFM) has a large amount of operation. To address this issue, a fast mathematical morphological filtering method (FMMFM) is proposed in this study. First, the erosion operation is modified to realize the realtime signal processing. Then, the data updating process of buffer in the microprocessor system is combined with the theory of MMFM. The iteration of sliding window is used to improve the time consumption of MMFM. In further, the smooth filtering method is also improved to speed up the optimization of the FMMFAM results. The measured pulse signals are used as experimental data. Compared with the MMFM, results show that the FMMFM can effectively reduce the calculation time (speed up over 70 times) and keep the filtering accuracy unchanged. The FMMFM with flat and linear structuring elements have faster filtering speed than those of other elements (speed up over 110 times). The proposed method can still process the signal in realtime (less than 45 s for the signals of 240 s) as the increase of the lengths of structuring element and buffer. It can be employed in pulse signal filtering, segmenting and feature extracting in realtime (less than 45 s). Therefore, the proposed method may be applied in some smart wearable devices with high realtime requirements, such as wristbands and watches.