CN113884973B

CN113884973B - Non-contact dielectric surface potential detection device and method

Info

Publication number: CN113884973B
Application number: CN202111255001.7A
Authority: CN
Inventors: 满梦华; 魏明; 李鹤
Original assignee: Army Engineering University of PLA
Current assignee: Army Engineering University of PLA
Priority date: 2021-10-27
Filing date: 2021-10-27
Publication date: 2022-10-11
Anticipated expiration: 2041-10-27
Also published as: CN113884973A

Abstract

The invention discloses a non-contact dielectric surface potential detection device and a method, and the device comprises: the computer is used for carrying out digital acquisition, processing and storage on the electrostatic potential sensor, carrying out non-contact test imaging of electrostatic potential three-dimensional spatial distribution, providing visual operation for a user and controlling the operation of the mechanical arm; the mechanical arm is used for calibrating a coordinate system at the front end of an induction electrode of the electrostatic potential sensor before the mechanical arm is initialized and driving the electrostatic potential sensor to move under the control of a computer; the electrostatic potential sensor is used for scanning in a three-dimensional space on the surface of a measured piece under the driving of the mechanical arm, and generating a sensing signal by moving in a space of a potential gradient generated on the surface of the measured piece to perform sampling and processing; the AC/DC excitation signal source is used for applying an AC/DC excitation signal to the tested piece when needed; and the oscilloscope is used for receiving and displaying the induction signal generated by the electrostatic potential sensor.

Description

Non-contact dielectric surface potential detection device and method

Technical Field

The invention relates to the technical field of three-dimensional scanning potential microscope testing, in particular to a non-contact dielectric surface potential detection device and method.

Background

The non-contact measurement of direct current and low-frequency alternating current potentials is based on the electrostatic induction principle, and the potential to be measured is indirectly obtained by measuring the distortion electrostatic field between a charged body and an induction electrode, so that the method has the advantages of no contact, wide application range, non-invasion and the like, and has a great amount of application in scientific research and industrial processes; if the weak electrostatic induction signal is processed by the AC amplifying circuit, the problems of zero drift and leakage attenuation of a DC electrostatic test are solved. The technology is mainly applied to atmospheric electric field monitoring and high-voltage power transmission safety monitoring, an amplifying circuit of the technology is simple, a test result is stable, the mechanical structure design is complex, and the spatial resolution of the test result is not high. In the prior art, the spatial resolution of measuring the surface potential of the carbon fiber reinforced composite material by using a field grinding type sensor is only 1mm.

The direct induction type is to modulate an electrostatic induction signal by using the relative displacement between a charged body and an induction electrode, for example, a sensor mechanically scans the surface of the charged body, and the electrostatic potential on the surface of the charged body is tested by dividing the voltage by the equivalent coupling capacitance between the electrode and the charged body and the input capacitance of the sensor. In order to improve the spatial resolution of the measurement result, the physical size of the sensing electrode needs to be reduced, so that the equivalent coupling capacitance is greatly reduced, even reaching the magnitude of the pico-law to the femto-law, which requires that the electrostatic potential sensor has a very small input capacitance and a very high input resistance at the same time, the low input capacitance is used for matching the equivalent coupling capacitance to improve the sensitivity, the high input resistance is used for reducing the charge leakage to improve the time coefficient of attenuation of the measurement result, and the technical difficulty limits the improvement of the spatial resolution of the direct sensing type measurement result. The novel electrostatic probe for measuring the surface potential distribution of the high-voltage insulator is provided in the prior art, the diameter of the front section of the probe is smaller, the probe can conveniently go deep into a narrow area for measurement, the diameter of the rear section of the probe is large, an I-shaped polytetrafluoroethylene support is used for insulation, the leakage resistance is increased, the ground coupling capacitance is reduced, and the sensitivity of the probe is improved. In the prior art, the insulation design and the leakage-proof high-impedance box of the electrostatic probe are also researched, and a three-dimensional control mechanism for measuring the surface electrostatic potential is designed, so that the spatial resolution of the test is obviously improved. In the prior art, the potential distribution of different insulator surfaces is measured by using an active electrostatic probe and a scanning control platform. In the prior art, a positive feedback circuit design technology is utilized to realize an ultra-high input resistance (approximately equal to 0.1T omega) and an ultra-low input capacitance (approximately equal to 0.3 fF) potential sensor (EPS), and the background noise is only 3.5nV/hz within a test range from 50mHz to 330 MHz. Furthermore, by combining a micro-nano processed flexible film probe and a micro-scale positioning precision triaxial positioning system, a non-contact scanning potential microscope (SEPM) is realized, the spatial resolution of a laboratory test result reaches the millimeter level, a large number of application experiments are carried out in the aspects of electrostatic charge imaging, conductor surface structure analysis and dielectric spectrum characterization, and the maturity of the SEPM technology is effectively promoted.

However, in practical application scenarios such as insulator surface potential testing, spacecraft static electrification law research and the like, the three-dimensional structure of the tested charged body is often irregular, different in shape and volume, and generally fixed in position but not movable, and higher requirements are provided for the three-dimensional testing and field testing capabilities of the SEPM. Therefore, the movable non-contact three-dimensional scanning potential microscope (3D-SEPM) for realizing the field test of the surface potential of the irregular object has wider application prospect.

Disclosure of Invention

The present invention is directed to a non-contact dielectric surface potential detection apparatus and method, which are used to solve the above-mentioned problems in the prior art.

The invention provides a non-contact dielectric surface potential detection device, comprising:

the computer is connected with the oscilloscope and is used for carrying out digital acquisition, processing and storage on induction signals of the electrostatic potential sensor through the oscilloscope, carrying out non-contact type test imaging of electrostatic potential three-dimensional spatial distribution, providing visual operation for a user and controlling the operation of the mechanical arm;

the control cabinet of the mechanical arm is connected to a computer, wherein the control cabinet comprises a demonstrator which is used for calibrating a coordinate system at the front end of an induction electrode of the electrostatic potential sensor, namely a user coordinate system, before the mechanical arm is initialized, and driving the electrostatic potential sensor to move under the control of the computer;

the electrostatic potential sensor is connected to a flange at the tail end of the mechanical arm through the adapter plate and is used for scanning in a three-dimensional space on the surface of a detected piece under the driving of the mechanical arm and generating an induction signal by moving in a space of a potential gradient generated by the action of electrostatic charges or alternating/direct current excitation signals on the surface of the detected piece and sampling and processing the induction signal;

the AC/DC excitation signal source is used for applying an AC/DC excitation signal to the tested piece when needed;

and the oscilloscope is connected with the electrostatic potential sensor and is used for receiving and displaying the induction signal generated by the electrostatic potential sensor.

The invention provides a non-contact dielectric surface potential detection method, which is used for the non-contact dielectric surface potential detection device and comprises the following steps:

controlling the operation of the mechanical arm through a computer;

calibrating a coordinate system, namely a user coordinate system, at the front end of an induction electrode of the electrostatic potential sensor before the initialization of the mechanical arm through a demonstrator of the mechanical arm, and driving the electrostatic potential sensor to move under the control of the computer;

scanning in a three-dimensional space on the surface of a measured piece under the driving of a mechanical arm through an electrostatic potential sensor, and generating an induction signal by moving in a space of a potential gradient generated on the surface of the measured piece under the action of electrostatic charges or an alternating current/direct current excitation signal, and sampling and processing;

applying an AC/DC excitation signal to the tested piece through an AC/DC excitation signal source when needed;

receiving and displaying an induction signal generated by the electrostatic potential sensor through an oscilloscope;

and carrying out digital acquisition, processing and storage on the induction signal of the electrostatic potential sensor of the oscilloscope through a computer, and carrying out non-contact test imaging of the three-dimensional spatial distribution of the electrostatic potential.

By adopting the embodiment of the invention, the resolution ratio of the three-dimensional spatial distribution of the tested electrostatic potential reaches 200 micrometers, the six-axis mechanical arm can be used for various test application scenes such as static charge distribution imaging on the surface of an insulating material, three-dimensional structure imaging on the surface of a conductive material, dielectric parameter distribution imaging of the dielectric material and the like in different working modes, the position and the posture of the sensor can be flexibly controlled, the six-axis mechanical arm is movable, the repeated positioning precision is high, and the six-axis mechanical arm has remarkable advantages in the aspect of testing the tested piece with a complex structure on site.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a schematic view of a non-contact dielectric surface potential detecting device according to an embodiment of the present invention;

FIG. 2 (a) is a schematic view of an equivalent circuit of a noncontact dielectric surface potential detection device of an embodiment of the present invention;

FIGS. 2 (b), 2 (c) and 2 (d) are schematic diagrams of electrostatic charge distribution measurement on the surface of an insulating material, three-dimensional structure measurement on the surface of a conductive material and dielectric constant distribution measurement of the insulating material, respectively, according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the conditioning amplifier circuit principle of an embodiment of the present invention;

FIG. 4 is a schematic diagram of an electrostatic potential sensor according to an embodiment of the invention;

FIG. 5a is a schematic representation of the voltage noise spectral density at the output of a sensor in accordance with an embodiment of the present invention;

FIG. 5b is a schematic diagram of a frequency response curve of a sensor of an embodiment of the present invention;

FIG. 6 is a schematic view of a flange adapter plate of an embodiment of the present invention;

FIG. 7 is a schematic diagram of a spatial localization and data processing algorithm flow according to an embodiment of the present invention;

FIG. 8 is a schematic diagram of an apparatus for calibrating a three-dimensional distribution of electrostatic potentials according to an embodiment of the present invention;

FIG. 9 is a graph showing the results of spatial distribution of electrostatic potential according to an embodiment of the present invention;

FIG. 10a is a schematic diagram of the electrostatic induction raw signal recorded by the sensor scanning along the X-axis and the filtered signal when the Y-position coordinate of the sensing electrode is fixed to 20mm according to the embodiment of the present invention;

FIG. 10b is a schematic diagram of the electrostatic induction raw signal recorded by the sensor scanning along the X-axis and the filtered signal when the Y-position coordinate of the sensing electrode is fixed to 50mm according to the embodiment of the present invention;

FIG. 10c is a two-dimensional schematic diagram of the test results obtained from the stitching of an embodiment of the present invention;

FIG. 10d is a three-dimensional schematic diagram of the test results obtained from the stitching of the embodiments of the present invention;

FIG. 11a is a schematic diagram of the electrostatic induction raw signal recorded by the sensor scanning along the X-axis and the filtered signal when the Y-position coordinate of the sensing electrode is fixed to 25mm according to the embodiment of the present invention;

FIG. 11b is a schematic diagram of the electrostatic induction raw signal and the filtered signal recorded by the sensor scanning along the X-axis when the Y-position coordinate of the sensing electrode is fixed to 60mm according to the embodiment of the present invention;

FIG. 11c is a schematic view of a scan result stitching plane according to an embodiment of the present invention;

FIG. 11d is a three-dimensional schematic view of scan result stitching according to an embodiment of the present invention;

FIG. 12 is a flow chart of a non-contact dielectric surface potential detection method according to an embodiment of the invention.

Detailed Description

In order to solve the problems in the prior art, the embodiment of the invention provides a non-contact type dielectric surface potential detection method through a six-axis mechanical arm and a direct induction type electrostatic potential testing principle, an electrostatic potential sensor with high sensitivity, high precision and low noise is designed based on a positive feedback circuit conditioning technology, a prototype testing system is realized, a calibration device with adjustable electrostatic signals, progressive spatial precision and controllable three-dimensional posture is further designed, the feasibility of the method is verified through numerical simulation and field experiments, and the result shows that the three-dimensional spatial resolution of the system for testing the distribution of direct current and low-frequency alternating current potentials reaches 200 microns.

The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be apparent that the described embodiments are some, but not all, embodiments of the present invention.

System embodiment

According to an embodiment of the present invention, there is provided a non-contact dielectric surface potential detection apparatus, fig. 1 is a schematic diagram of the non-contact dielectric surface potential detection apparatus according to the embodiment of the present invention, as shown in fig. 1, the non-contact dielectric surface potential detection apparatus according to the embodiment of the present invention specifically includes:

the computer is connected with the oscilloscope and is used for carrying out digital acquisition, processing and storage on induction signals of the electrostatic potential sensor through the oscilloscope, carrying out non-contact test imaging on three-dimensional spatial distribution of electrostatic potential, providing visual control operation for a user and controlling the operation of the mechanical arm; the computer is specifically configured to:

the method comprises the steps that the oscilloscope is controlled through Python programming, induction signals of the electrostatic potential sensor are digitally collected, processed and stored through the oscilloscope, visual control operation of a user is provided, a scanning path of the surface of a tested piece is subjected to motion track coordinate solving through an inverse solution function interface of Python SDK of the mechanical arm based on a calibrated user coordinate system, operation of the mechanical arm is controlled, after all track routes are scanned, stored recording data of the oscilloscope are spliced according to the sequence of motion tracks, and after splicing is carried out, filtering and smoothing processing are carried out on the induction signals obtained under the condition of no excitation or direct current excitation; and performing data processing of trend removing, envelope taking and down sampling on the induction signals obtained under alternating current excitation, and realizing non-contact type test imaging of three-dimensional space distribution of electrostatic potential.

The control cabinet of the mechanical arm is connected to the computer, wherein the control cabinet comprises a demonstrator which is used for calibrating a coordinate system at the front end of an induction electrode of the electrostatic potential sensor, namely a user coordinate system, before the mechanical arm is initialized, and driving the electrostatic potential sensor to move under the control of the computer; in the embodiment of the invention, the mechanical arm is a six-axis mechanical arm and supports Python programming interface control.

The electrostatic potential sensor is connected to the flange at the tail end of the mechanical arm through the adapter plate and is used for scanning in a three-dimensional space on the surface of a measured piece under the driving of the mechanical arm and generating an induction signal by moving in a potential gradient space generated on the surface of the measured piece under the action of electrostatic charges or alternating/direct current excitation signals and sampling and processing the induction signal; the electrostatic potential sensor is specifically configured to:

when measuring the distribution of electrostatic charges on the surface of an insulating material, setting mode one: make V be _s (t) variation, d (t) and ε (t) are fixed by V _p (t) Change reaction V _s (t) a positional distribution of (t) wherein V _s (t) is the potential of the point to be measured on the surface of the measured piece, d (t) is the distance between the measured piece and the induction electrode, epsilon (t) is the dielectric constant between the measured piece and the induction electrode, and V _p (t) is a sensing signal;

when measuring the three-dimensional structure of the surface of the conductive material, setting a second mode: d (t) is changed, V _s (t) and ε (t) are fixed by V _p (t) change reflects the position distribution of d (t);

when the dielectric constant distribution test of the insulating material is performed, a third mode is set: changing epsilon (t), V _s (t) and d (t) are fixed by V _p The change in (t) reflects the position distribution of ε (t).

The electrostatic potential sensor specifically includes: the output end of the electrostatic induction signal amplifying circuit is connected with the oscilloscope, wherein,

the induction electrode is used for scanning in a three-dimensional space on the surface of the measured piece with preset space precision and preset speed precision, and generating an induction signal by moving in a space of a potential gradient generated on the surface of the measured piece due to the action of static charges or an alternating current/direct current excitation signal;

and the electrostatic induction signal amplifying circuit is used for sampling and processing the induction signal.

The above technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

FIG. 1 illustrates the principle of operation of a test system, a static induction signal amplification circuit and a 0.29mm diameter induction electrode setThe electrostatic potential sensor is connected to a six-axis high-precision mechanical arm end flange through a switching plate, under the control of a mechanical arm, an induction electrode can scan in a three-dimensional space on the surface of a detected piece with the space precision of 20um and the speed precision of 1mm/s, the surface space of the detected piece generates a potential gradient due to the action of electrostatic charges or an alternating/direct current excitation signal, the induction electrode moves in the potential gradient space to generate an alternating current induction signal, and the signal is sampled and processed by an electrostatic induction signal amplifying circuit to realize non-contact type test imaging of electrostatic potential three-dimensional space distribution. The equivalent circuit of the test principle of the embodiment of the invention is shown in fig. 2 (a), and the potential of the point to be tested on the surface of the tested piece is V _s (t), the distance from the sensing electrode is d (t), the dielectric constant between the two is epsilon (t), the radius of the sensing electrode is r, and therefore the equivalent coupling capacitance C between the sensing electrode and the test point _e (t) is:

the equivalent input resistance of the sensor signal conditioning amplifying circuit is R _in Input capacitance of C _in The induction signal is V _p (t), the analysis circuit may obtain:

according to the above formula, when the mechanical arm controls the movement of the electrostatic potential sensor, the potential V to be measured _s (t) and a coupling capacitance C _e (t) all the gradient changes influence the induction signal V of the electrostatic potential sensor _p (t), and the change in coupling capacitance is in turn influenced by the distance d (t) and the dielectric constant ε (t). In the scanning process of the mechanical arm control sensor, when any two of the three parameters are fixed or periodically changed, the change of the output signal of the electrostatic potential sensor reflects the position distribution of the changed parameter of the measured object. Therefore, the three-dimensional distribution of electrostatic potential non-contact test system has three test modes. When V is _s (t) change, d (t) andwhen ε (t) is fixed, V _p (t) Change reaction V _s (t), which is a pattern generally used to measure the distribution of electrostatic charges on the surface of an insulating material, as shown in fig. 2 (b). When d (t) changes, V _s When (t) and ε (t) are fixed, V _p The change in (t) reflects the position distribution of d (t), which is commonly used for the measurement of the three-dimensional structure of the surface of the conductive material, as shown in FIG. 2 (c). When ε (t) changes, V _s When (t) and d (t) are fixed, V _p The change in (t) reflects the position distribution of ε (t), which is commonly used in dielectric constant distribution testing of insulating materials, as shown in FIG. 2 (d).

The test modes are classified into a passive mode and an active mode according to whether an external excitation signal is required. The passive mode is that no excitation signal is applied to the tested piece during testing, and the space potential gradient is generated by using the static charge inside or on the surface of the tested piece, and the active mode as the testing object is that an excitation signal is applied to the tested piece by using an alternating/direct current signal source during testing, and the space potential gradient is generated by using the surface topography change or the dielectric constant distribution difference of the tested piece and is used as the testing object.

The electrostatic potential sensor design will be described in detail below.

The conditioning and amplifying circuit principle is as follows: the sensing electrode of the electrostatic potential sensor is not in contact with the tested sample in electrical and mechanical modes, so the testing mode is classified as capacitance measurement, and the method has the advantages of non-invasive sensing technology. However, due to the small equivalent coupling capacitance between the sensing electrode and the tested sample, the low frequency and the fast attenuation of the electrostatic signal of the tested piece, and the existence of the high-level power frequency interference, etc., the source impedance of the equivalent circuit of the testing system is very high, and the input impedance of the conditioning amplifying circuit is required to be kept at a very high level, so as to avoid any significant interference to the tested electric field. Therefore, the development of an electrostatic induction signal conditioning and amplifying circuit with high sensitivity, high precision, high stability and low noise has become one of the core research subjects in the field. A typical positive feedback electronic technology such as neutralization, bootstrap, protection and the like is utilized, and a simulation front-end conditioning amplifying circuit is designed by combining shielding, power frequency trap and program control amplifying technologies, so that a high-quality electrostatic potential signal is obtained. The circuit principle is shown in fig. 3, in which,

the electrostatic induction signal amplifying circuit comprises input capacitors C respectively connected with the positive input ends of the first operational amplifiers _in And a stray capacitor C _x And an input resistance R _in (ii) a The positive input end of the first operational amplifier is also connected with an equivalent coupling capacitor C _e (ii) a Wherein, the equivalent coupling capacitance C _e The size of the sensing electrode is determined by the diameter of the sensing electrode and the distance between the sensing electrode and the measured piece.

An equivalent coupling capacitor C in the circuit _e An input capacitor C _in And stray capacitance C _x Forming a capacitive divider network with an equivalent coupling capacitance C _e And an input resistor R _in Forming a first order high pass filter, both of which reduce circuit sensitivity; therefore, the input impedance is improved by arranging the direct current bias and bootstrap feedback module 10 connected between the positive input end and the negative input end of the first operational amplifier and the output end, so that the circuit sensitivity is improved; high sensitivity is important to improve spatial resolution because the strength of the sensing signal is proportional to the diameter of the sensing electrode, and the thinner the sensing electrode, the weaker the sensing signal, and the higher the spatial resolution.

The DC bias and bootstrap feedback module 10 includes a first resistor R connected in series _b1 And a second resistor R _b2 First resistance R _b1 And a second resistor R _b2 The other end of the first resistor is respectively connected with the positive input end of the first operational amplifier and the ground, and the first resistor R _b1 And a second resistor R _b2 The common terminal is connected to the first capacitor C _b Connected with the negative input end and the output end of the first operational amplifier.

In order to further increase the input impedance of the circuit, a forward feedback module 20 is arranged between the forward input end and the output end of the first operational amplifier in the circuit of the invention, and the forward feedback module is composed of a second operational amplifier and a third resistor R _n1 A fourth resistor R _n2 And a second capacitor C _n The output end of the second operational amplifier and the third resistor R _n1 Are all passed through a second capacitor C _n The positive input end of the first operational amplifier is connected; third resistor R _n1 The other end of the resistor is connected with a fourth resistor R _n2 Connected to a second operational amplifierPositive input terminal, fourth resistor R _n2 The other end is grounded; the positive input end of the second operational amplifier is connected with the output end of the first operational amplifier; the second capacitor Cn feeds back the proper proportion of the circuit output signal (i.e. the proportion value is increased as much as possible to improve the sensitivity of the test circuit under the premise of not causing the self-oscillation of the first amplifier) to the circuit input end so as to reduce the equivalent input capacitor C of the circuit _in Thereby further increasing the input impedance of the circuit.

The conditioning amplification module (30) is connected to the output end of the first operational amplifier and is in wireless connection with the computer, and comprises an ADI amplifier and a power frequency wave trap which are packaged in a grounding shielding box; an STM32 microcontroller and a wireless transmission module for wirelessly transmitting signals with a computer are also arranged;

the wireless transmission module can be set as a bluetooth module or a wifi module connected with the computer, and can be, for example, an HC09 bluetooth module.

In the circuit, an active protection module 40 is arranged between the positive input end of the first operational amplifier and the output end of the first operational amplifier, and the unit gain amplifier is used for driving the shielding shell of the induction electrode to reduce the stray capacitance C _x Therefore, the leakage current and power frequency interference of the induction electrode can be reduced to the maximum extent.

The electrostatic potential sensor is realized using well-established commercial discrete component and printed circuit board processes, as shown in fig. 4. The induction electrode is composed of a half steel wire SFT50-1 with the inner diameter of 0.29mm and a three-coaxial connecting seat CBJ 70. The conditioning amplifying circuit board is composed of an ADI amplifier, a power frequency wave trap, an STM32 microcontroller, a Bluetooth module and the like, and is packaged in a grounding shielding box. By properly setting the resistance-capacitance values of the bootstrap and neutralization circuits, the equivalent input impedance of the electrostatic potential sensor can reach 10 at most ¹³ In the order of ohms. The conditioning amplifying circuit can continuously measure and has two data output modes of analog signal output and digital storage Bluetooth transmission.

The noise spectral density of a principle prototype of an electrostatic potential sensor was tested in an unshielded environment using an agilent E4440A spectrum analyzer. When the input terminal is grounded through a 1pF capacitor, the voltage at the output terminal of the electrostatic potential sensorThe noise spectrum density is shown in FIG. 5a, wherein the 1/f corner frequency of the flicker noise is about 30Hz, and the Gaussian white noise of the sensor above the frequency point is

This parameter determines the minimum detectable signal amplitude of the electrostatic potential sensor. Under the same environment, the input and output frequency response characteristics of the electrostatic potential sensor under the condition of 1pF coupling capacitance are analyzed by using a Digilent's Analog Discovery2 multifunctional analyzer, the frequency response curve is shown in figure 5b, the power frequency trap circuit enables the gain of the direct current voltage of the electrostatic potential sensor near 50Hz to be about-30 dB, so that the power frequency interference can be effectively reduced, and in addition, the gain of the direct current voltage in the range of 0.1Hz to 1kHz is 34dB, so that the weak electrostatic induction signal can be effectively amplified.

The electrostatic potential sensor is installed at the tail end of the six-axis mechanical arm through the customized flange adapter plate, the high-precision three-dimensional space positioning of the induction electrode is realized, and the high-precision three-dimensional space positioning is used for testing and scanning the surface potential of a tested piece, wherein the customized flange adapter plate can be as shown in figure 6, a plurality of fixing holes which are different in size and used for being connected with the electrostatic potential sensor are formed in the plate, and the electrostatic potential sensor is fixed on the flange adapter plate through bolts.

The working radius of the six-axis mechanical arm is 886 mm, the space positioning precision is 0.02 mm, the maximum speed of the tail end is 2.8m/s, and the precision is 1mm/s. Therefore, the posture and the distance of the induction electrode can be accurately adjusted according to the shape and the position of the detected piece, the scanning speed of the electrode is adjusted according to the size of the detected electrostatic potential, and the distance and the speed parameters are key parameters influencing the strength of the electrostatic induction signal.

The arm passes through switch board and demonstrator and is connected to the computer, supports Python programming interface control. And the computer simultaneously controls a PicoScope 4824 oscilloscope of Pico through Python programming to realize the operations of digital acquisition, storage, visualization and the like of the analog signals of the sensor. The spatial localization and data processing algorithm flow is shown in fig. 7. Before the mechanical arm is initialized, a coordinate system (namely a user coordinate system) of the front end of the induction electrode needs to be calibrated by using a demonstrator. And then, solving the motion track coordinate of the scanning path on the surface of the tested piece by using an inverse solution function interface of the mechanical arm Python SDK. And after all the track routes are scanned, splicing the mounting motion tracks of the oscilloscope recorded data stored by the computer in sequence. Finally, only filtering and smoothing are needed to be carried out on the static induction signals obtained in the non-excitation or direct-current excitation working mode; for the electrostatic induction signals obtained in the ac excitation working mode, further data processing of trend elimination, envelope extraction and down sampling is required, so as to reduce the artifacts of mechanical vibration and the pick-up of external noise during the scanning process.

In order to quantitatively research the three-dimensional spatial resolution of the electrostatic potential distribution tested by the system, a calibration device which can adjust electrostatic signals, and can realize progressive spatial precision and controllable three-dimensional posture is designed, as shown in fig. 8. Firstly, the electrostatic potential calibration piece similar to an interdigitated capacitor structure is realized by utilizing a printed circuit board process, and is connected with an external signal source through an SMA joint to receive a direct current or alternating current excitation signal. The voltage amplitude of the direct current excitation signal, the voltage and frequency of the alternating current signal and other parameters can be flexibly controlled, and therefore the to-be-detected electrostatic signal can be adjusted. In the calibration piece, an excitation signal and a ground potential are respectively connected with straight wires with the same width, signal wires and ground wires are arranged in parallel at intervals, the distance between the signal wires and the ground wires is as wide as the width of the wires, and the distance parameter is defined as the spatial precision of electrostatic potential distribution. The static potential areas with different space accuracies are designed in the tested piece, 9 space accuracy areas from 1mm to 0.2mm are arranged in a decreasing step length of 0.1 mm, and therefore progressive traversal of static distribution space accuracy is achieved. And finally, the calibration piece is arranged on a precise five-axis translation table, and can be adjusted into any space posture according to experimental requirements, so that flexible control of three-dimensional space distribution of electrostatic potential is realized.

The calibration piece is subjected to three-dimensional modeling and electrostatic simulation by using finite element analysis software, and the spatial distribution result of the electrostatic potential of the calibration piece under the excitation of direct-current voltage is shown in fig. 9. The gradient of the surface electrostatic potential distribution of the calibration piece is rapidly attenuated along with the increase of the normal distance, and is reduced along with the improvement of the space precision, and the gradient is basically attenuated to zero when the normal distance reaches 2 times of the space precision. The normal distance from the induction electrode of the test system to the surface of the piece to be tested is required to be within 2 times of the space precision, the smaller the distance is, the larger the potential distribution gradient is, the stronger the induction signal is, and the more favorable the processing of the rear-end conditioning amplification circuit is. On the other hand, the diameter of the sensing electrode is also a bottleneck for limiting the precision of the testing space, the thicker the electrode is, the larger the area of the sensing space is, the lower the potential gradient is due to the spatial filtering effect, the lower the sensing signal is, and thus the spatial resolution of the testing system is limited. Therefore, the smaller the normal distance is, the thinner the induction electrode is, and the spatial resolution of the test system can be obviously improved. However, due to the existence of the electrostatic "point discharge" phenomenon, the spatial resolution cannot be infinitely improved by the above two parameters, and an appropriate value needs to be selected through experiments to achieve the optimal spatial resolution.

The electrostatic potential three-dimensional distribution non-contact test method provided by the embodiment of the invention has multiple application scenes and working modes, and can be mainly divided into a direct current test and an alternating current test according to different tested signals. The resolution ratio of the system for testing the three-dimensional spatial distribution of the electrostatic potential is researched by respectively utilizing an alternating current signal source and a direct current signal source.

And D, direct current testing: a calibration piece with the thickness of 2mm, the length of 8cm (defined as an X axis) and the width of 8cm (defined as a Y axis) is manufactured by utilizing a printed circuit board process, the calibration piece is fixed to the top end of a precision five-axis translation stage, the translation stage is adjusted to enable the plane (XOY plane) of the calibration piece to form an included angle of 45 degrees with the horizontal plane, a direct current voltage source is connected to an excitation signal SMA interface of the calibration piece, 30V direct current voltage output is set, and a standard electrostatic field to be tested distributed in a three-dimensional space is formed on the surface of the calibration piece. Furthermore, the mechanical arm controls the position and the posture of the electrostatic potential sensor, the surface space of the calibration piece is scanned in a raster mode, the induction electrodes are perpendicular to the surface of the calibration piece, the distance is 0.3mm, the induction electrodes are stepped along the Y axis in 1mm step length, the induction electrodes continuously move along the X axis under each Y axis coordinate value, the acceleration is 1mm/s, and the maximum speed is 3mm/s. Meanwhile, an oscilloscope records an analog signal output by the electrostatic potential sensor at a sampling rate of 1KHz, and performs noise reduction processing on the recorded data by using a low-pass digital filter function lowpass () of MATLAB, wherein the passband frequency is 0.01 pi radians/sample, the steepness is 0.95, and the stopband attenuation is 180dB. Finally, all the recorded data are subjected to joint visualization, and a typical result is shown in fig. 10.

Fig. 10a and 10b show the electrostatic potential sensor scanning the recorded electrostatic induction raw signal and the filtered signal along the X-axis when the Y-position coordinate of the induction electrode is fixed at 20mm and 50mm, respectively. In the scanning process of the mechanical arm control electrostatic potential sensor, the motion of the induction electrode modulates the direct-current electrostatic potential into an alternating-current induction signal, and the amplitude-frequency characteristic of the alternating-current signal is determined by the spatial distribution gradient of the electrostatic potential to be detected and the motion speed of the induction electrode. Part of the noise in the raw signal comes from the noise floor of the electrostatic potential sensor. When the frequency of the alternating current signal falls within 30Hz of the flicker noise corner frequency of the electrostatic potential sensor, the influence of the flicker noise is obviously increased, and when the frequency exceeds 30Hz, the local noise of the electrostatic potential sensor is very low. Another part of the noise in the raw signal comes from the motion noise of the mechanical arm. On one hand, the positioning precision of the mechanical arm is only 20 micrometers, and mechanical vibration inevitably exists in the motion of the motor, so that the normal distance between the induction electrode and the piece to be detected shakes, and an equivalent coupling capacitor C is further caused _e Thereby causing noise in the sensing signal. On the other hand, the spatial position coordinates of the motion track of the induction electrode are obtained by inversely solving the six-axis rotation angle of the mechanical arm, when the electrostatic potential sensor scans along the surface of the measured piece, some singular point coordinates with inaccessible spatial position inevitably exist, and the scanning motion track needs to be finely adjusted in micrometer scale, so that the problem of mismatching between the expected test position and the actual position is caused, and signal noise is introduced.

As shown in fig. 10, the low-pass filtering function used in the embodiment of the present invention can effectively remove the above-mentioned noise. Fig. 10c and 10d show two-dimensional and three-dimensional maps obtained by stitching the test results, respectively, and the test system can distinguish nine spatial precision regions from 1mm to 0.2 mm. In general, the lower the spatial accuracy, the stronger the sensing signal, and the sensing signal strength in the region with spatial accuracy of 0.9mm and 1.0mm is significantly higher than that in the region with spatial accuracy of 0.2mm and 0.3mm, but the signal strength does not monotonically correspond to the spatial accuracy. The strength of the induction signal is determined by the test distance, the potential gradient and the movement speed together, the test distance and the movement speed are fixed in the experiment, the induction signal reflects the time change rate of the potential gradient, namely the positioning gradient is divided by the scanning time, the spatial precision is improved, so that the potential gradient and the scanning time are simultaneously reduced, and the induction signal and the spatial precision are not in a monotonous corresponding relation. Therefore, in order to achieve the highest test accuracy, it is necessary to appropriately select the test distance and the movement speed parameter according to the object to be tested. The result shows that the resolution ratio of the system for testing the three-dimensional space distribution of the direct current electrostatic potential reaches 0.2mm, and the system can be applied to imaging of electrostatic charge distribution on the surface of an insulating material and imaging of a three-dimensional structure on the surface of a conductive material.

And (3) alternating current testing: the same experimental flow of a direct current test experiment is adopted, a signal source is replaced by a 33520B waveform generator of Germany technology, and an excitation signal is set to be a sine wave signal with 2V amplitude and 10kHz frequency. The sampling rate of the oscilloscope was set to 100kHz. And performing trend removing, envelope detection and down-sampling processing on the sampled data by adopting a signal processing function embedded in MATLAB. The method for removing the trend function detrend () selects a constant method, the envelope detection function envelope () selects an effective value method, the window length is set to 5000, and the reduction factor of the down-sampling function decimate () is set to 100 times. All recorded data are also visualized by stitching as shown in fig. 11.

Fig. 11a and 11b show the electrostatic potential sensor scanning the recorded electrostatic induction raw signal and the detection processed signal along the X-axis when the Y position coordinate of the induction electrode is fixed at 25mm and 60mm, respectively. Because the excitation signal is an alternating current signal, the output of the electrostatic potential sensor is a measured signal induced by the equivalent coupling capacitance, and motion modulation is not needed. The amplitude of the induction signal envelope is inversely related to the test distance, and the frequency is positively related to the scanning speed. The signal noise is mainly derived from the robot arm movement noise, such as the abnormal signal values of the vertical column present in fig. 11c and 11 d. Also, in overview, the test system is able to distinguish nine spatial precision regions from 1mm to 0.2 mm. The induced signal intensity in the regions with spatial accuracies of 0.9mm and 1.0mm is significantly higher than in the regions with spatial accuracies of 0.2mm and 0.3mm, but the signal intensity does not have a monotonic correspondence with the spatial accuracies for the same reasons as above. The resolution ratio of the system for testing the three-dimensional space distribution of the alternating current potential can also reach 0.2mm, and the system can be applied to the imaging of the dielectric parameter distribution of the dielectric material.

In summary, the embodiment of the invention provides a basic concept and a testing method of a non-contact three-dimensional scanning potential microscope, the feasibility of the concept is verified through theoretical derivation, numerical simulation and prototype system experiments, and the result shows that the resolution of the system for testing the three-dimensional spatial distribution of the electrostatic potential reaches 200 micrometers. Under different working modes, the method can be used for various test application scenes such as electrostatic charge distribution imaging on the surface of an insulating material, three-dimensional structure imaging on the surface of a conductive material, dielectric parameter distribution imaging of a dielectric material and the like, for example, insulator surface electric field three-dimensional distribution test in the high-voltage field, carbon fiber composite nondestructive test in the fault detection field, and electrostatic deposition detection on the surface of an aircraft in the aerospace field. The six-axis mechanical arm adopted by the embodiment of the invention can flexibly control the position and the posture of the electrostatic potential sensor, can move and has high repeated positioning precision, thereby having remarkable advantages in the aspect of testing a tested piece with a complex structure on site.

Method embodiment

According to an embodiment of the present invention, there is provided a non-contact dielectric surface potential detection method for the above non-contact dielectric surface potential detection apparatus, fig. 12 is a flowchart of the non-contact dielectric surface potential detection method according to the embodiment of the present invention, and as shown in fig. 12, the non-contact dielectric surface potential detection method according to the embodiment of the present invention specifically includes:

step 1101, controlling the operation of the mechanical arm through a computer; the method specifically comprises the following steps:

solving the motion trail coordinate of the scanning path on the surface of the tested piece by using an inverse solution function interface of Python SDK of the mechanical arm based on a calibrated user coordinate system, and controlling the operation of the mechanical arm;

step 1102, calibrating a coordinate system, namely a user coordinate system, at the front end of an induction electrode of the electrostatic potential sensor before initializing the mechanical arm through a demonstrator of the mechanical arm, and driving the electrostatic potential sensor to move under the control of the computer; specifically, the mechanical arm receives control of the computer through a Python programming interface, and drives the electrostatic potential sensor to move under the control of the computer, wherein the Python programming interface specifically includes: inverse solution function interface of Python SDK.

1103, scanning the surface of a measured piece in a three-dimensional space under the driving of a mechanical arm through an electrostatic potential sensor, and generating an induction signal by moving in a space of potential gradient generated on the surface of the measured piece under the action of electrostatic charges or an alternating/direct current excitation signal, and sampling and processing the induction signal; specifically, the induction electrode scans in a three-dimensional space on the surface of the measured piece with preset space precision and preset speed precision, and an induction signal is generated by the spatial motion of a potential gradient generated on the surface of the measured piece due to the action of static charge or an alternating/direct current excitation signal; and sampling and processing the induction signal through an electrostatic induction signal amplifying circuit. Wherein, sampling and processing the induction signal through the electrostatic induction signal amplifying circuit specifically comprises:

when measuring the distribution of electrostatic charges on the surface of an insulating material, setting a first mode: make V _s (t) change, d (t) and ε (t) are fixed by V _p (t) Change reaction V _s (t) a positional distribution of (t) wherein V _s (t) is the potential of the point to be measured on the surface of the measured piece, d (t) is the distance between the measured piece and the induction electrode, epsilon (t) is the dielectric constant between the measured piece and the induction electrode, and V _p (t) is a sensing signal;

1104, applying an AC/DC excitation signal to the surface space of the tested piece through an AC/DC excitation signal source when needed;

step 1105, receiving and displaying the induction signal generated by the electrostatic potential sensor through an oscilloscope;

and step 1106, performing digital acquisition, processing and storage on the sensing signal of the electrostatic potential sensor of the oscilloscope through a computer, and performing non-contact test imaging of three-dimensional spatial distribution of electrostatic potential. The method specifically comprises the following steps:

the method comprises the following steps of controlling an oscilloscope through Python programming, carrying out digital acquisition, processing and storage on induction signals of the electrostatic potential sensor through the oscilloscope, and providing visual operation of a user;

after all track lines are scanned, splicing the stored oscilloscope recorded data according to the sequence of the motion tracks;

after splicing, filtering and smoothing induction signals obtained under the condition of no excitation or direct current excitation; and performing data processing of trend removing, envelope taking and down sampling on the induction signals obtained under the alternating current excitation, and realizing non-contact test imaging of three-dimensional space distribution of electrostatic potential.

The embodiment of the present invention is a method embodiment corresponding to the system embodiment described above, and specific operations of each processing step may be understood with reference to the description of the method embodiment, which is not described herein again.

The foregoing description of specific embodiments has been presented for purposes of illustration and description. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 30 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical blocks. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as ABEL (Advanced Boolean Expression Language), AHDL (alternate Hardware Description Language), traffic, CUPL (core universal Programming Language), HDCal, jhddl (Java Hardware Description Language), lava, lola, HDL, PALASM, rhyd (Hardware Description Language), and vhigh-Language (Hardware Description Language), which is currently used in most popular applications. It will also be apparent to those skilled in the art that hardware circuitry for implementing the logical method flows can be readily obtained by a mere need to program the method flows with some of the hardware description languages described above and into an integrated circuit.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium that stores computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be regarded as a hardware component and the means for performing the various functions included therein may also be regarded as structures within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, respectively. Of course, the functions of the various elements may be implemented in the same one or more pieces of software and/or hardware in practicing embodiments of the present description.

One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The description has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises that element.

One or more embodiments of the specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.

Claims

1. A non-contact dielectric surface potential detecting device, comprising:

the computer is connected with the oscilloscope and is used for carrying out digital acquisition, processing and storage on the sensing signal of the electrostatic potential sensor through the oscilloscope, carrying out non-contact type test imaging of electrostatic potential three-dimensional spatial distribution, providing visual control operation for a user and controlling the operation of the mechanical arm;

the control cabinet of the mechanical arm is connected to the computer, wherein the control cabinet comprises a demonstrator which is used for calibrating a coordinate system at the front end of an induction electrode of the electrostatic potential sensor, namely a user coordinate system, before the mechanical arm is initialized, and driving the electrostatic potential sensor to move under the control of the computer;

the electrostatic potential sensor is connected to the end flange of the mechanical arm through the adapter plate, and is used for scanning in a three-dimensional space on the surface of a detected piece under the driving of the mechanical arm, and generating an induction signal by moving in a potential gradient space generated by the action of electrostatic charges or alternating/direct current excitation signals on the surface of the detected piece, and sampling and processing the induction signal, wherein the electrostatic potential sensor specifically comprises:

when measuring the distribution of electrostatic charges on the surface of an insulating material, setting mode one: make V _s (t) variation, d (t) and ε (t) are fixed by V _p (t) Change reaction V _s (t) a positional distribution of (t) wherein V _s (t) is the potential of the point to be measured on the surface of the measured piece, d (t) is the distance between the measured piece and the induction electrode, epsilon (t) is the dielectric constant between the measured piece and the induction electrode, and V _p (t) is a sensing signal;

when the dielectric constant distribution test of the insulating material is performed, a third mode is set: changing epsilon (t), V _s (t) and d (t) are fixed by V _p (t) changes reflect the positional distribution of ε (t);

2. The apparatus of claim 1, wherein the robotic arm is a six-axis robotic arm supporting Python programming interface control.

3. The device according to claim 1, characterized in that said electrostatic potential sensor comprises in particular: the output end of the electrostatic induction signal amplifying circuit is connected with the oscilloscope, wherein,

the induction electrode is used for scanning in a three-dimensional space on the surface of a measured piece with preset space precision and preset speed precision, and generating an induction signal by moving in a space of a potential gradient generated by the action of static charges or an alternating/direct current excitation signal on the surface of the measured piece;

4. The apparatus of claim 3, wherein the electrostatic induction signal amplifying circuit comprises a first operational amplifier;

the direct current bias and bootstrap feedback module (10) is connected between the positive input end and the negative input end of the first operational amplifier, and the direct current bias and bootstrap feedback module (10) is also connected with the output end of the first operational amplifier;

a neutralization positive feedback module (20) connected between the positive input end and the output end of the first operational amplifier;

and the conditioning amplification module (30) is connected to the output end of the first operational amplifier and is in wireless connection with a computer.

5. The apparatus of claim 4, wherein the DC bias and bootstrap feedback module (10) comprises a first resistor R connected in series _b1 And a second resistor R _b2 The first resistor R _b1 And a second resistor R _b2 The other end of the first resistor R is respectively connected with the positive input end of the first operational amplifier and the ground, and the first resistor R _b1 And a second resistor R _b2 The common terminal is connected to the first capacitor C _b And the negative input end and the output end of the first operational amplifier are connected.

6. The apparatus of claim 4, wherein the forward feedback module (20) is composed of a second operational amplifier and a third resistor R _n1 A fourth resistor R _n2 And a second capacitor C _n Forming;

the output end of the second operational amplifier and the third resistor R _n1 Are all passed through said second capacitance C _n The positive input end of the first operational amplifier is connected, and the third resistor R _n1 The other end of the resistor is connected with the fourth resistor R _n2 The fourth resistor R is connected with the positive input end of the second operational amplifier _n2 The other end is grounded, the second operationThe forward input end of the amplifier is connected with the output end of the first operational amplifier.

7. The apparatus according to claim 4, wherein the conditioning amplification module (30) comprises an amplifier and a power frequency trap packaged in a grounded shield box, a microcontroller and a wireless transmission module for wireless transmission of signals.

8. The apparatus of claim 4, wherein an active protection module (40) is connected between the first operational amplifier positive input terminal and the first operational amplifier output terminal.

9. The apparatus of claim 1, wherein the computer is specifically configured to:

the method comprises the steps that the oscilloscope is controlled through Python programming, induction signals of the electrostatic potential sensor are digitally collected, processed and stored through the oscilloscope, visual control operation of a user is provided, a scanning path of the surface of a tested piece is subjected to motion track coordinate solving through an inverse solution function interface of Python SDK of the mechanical arm based on a calibrated user coordinate system, operation of the mechanical arm is controlled, after all track routes are scanned, stored recording data of the oscilloscope are spliced according to the sequence of motion tracks, and after splicing is carried out, filtering and smoothing processing are carried out on the induction signals obtained under the condition of no excitation or direct current excitation; and performing data processing of trend removing, envelope taking and down sampling on the induction signals obtained under the alternating current excitation, and realizing non-contact test imaging of three-dimensional space distribution of electrostatic potential.

10. A non-contact dielectric surface potential detection method, for use in the non-contact dielectric surface potential detection apparatus of any one of claims 1 to 9, the method comprising:

controlling the operation of the mechanical arm through a computer;

calibrating a coordinate system, namely a user coordinate system, at the front end of an induction electrode of the electrostatic potential sensor through a demonstrator of the mechanical arm before the mechanical arm is initialized, and driving the electrostatic potential sensor to move under the control of the computer;

under the drive of mechanical arm, the electrostatic potential sensor scans in the three-dimensional space of the surface of the measured piece, and the space motion of the potential gradient generated on the surface of the measured piece due to the action of electrostatic charge or AC/DC excitation signals generates induction signals and carries out sampling and processing, and the method specifically comprises the following steps:

applying an AC/DC excitation signal to the surface space of the tested piece through an AC/DC excitation signal source when needed;

receiving and displaying the induction signal generated by the electrostatic potential sensor through an oscilloscope;

11. The method of claim 10, wherein the driving the electrostatic potential sensor to move under the control of the computer specifically comprises:

the mechanical arm receives control of the computer through a Python programming interface, and drives the electrostatic potential sensor to move under the control of the computer, wherein the Python programming interface specifically comprises the following steps: inverse solution function interface of Python SDK.

12. The method according to claim 10, wherein the scanning in the three-dimensional space on the surface of the object to be measured is performed by the electrostatic potential sensor under the driving of the robot arm, and the generating of the sensing signal and the sampling and processing of the sensing signal are performed by the spatial motion of the potential gradient generated on the surface of the object to be measured due to the electrostatic charge or the ac/dc excitation signal, specifically comprising:

scanning the surface of the measured piece in a three-dimensional space by the induction electrode with preset space precision and preset speed precision, and generating an induction signal by moving in the space of potential gradient generated on the surface of the measured piece due to the action of static charge or an alternating/direct current excitation signal;

and sampling and processing the induction signal through an electrostatic induction signal amplifying circuit.

13. The method of claim 10,

the control of the operation of the mechanical arm through the computer specifically comprises:

the method specifically comprises the following steps of digitally acquiring, processing and storing induction signals of an electrostatic potential sensor of the oscilloscope through a computer, and carrying out non-contact test imaging of three-dimensional spatial distribution of electrostatic potential:

after splicing, filtering and smoothing induction signals obtained under the condition of no excitation or direct current excitation; and performing data processing of trend removing, envelope taking and down sampling on the induction signals obtained under alternating current excitation, and realizing non-contact type test imaging of three-dimensional space distribution of electrostatic potential.