Non-contact ripple noise measurement method and system based on industrial mechanical arm

Abstract

The invention discloses a non-contact ripple noise measurement method and system based on an industrial mechanical arm, and belongs to the technical field of industrial precision measurement and electrical performance detection. According to the method, firstly, a unified global working coordinate system is established, measurement probe calibration and mechanical arm precision verification are completed, and system basic parameters are output; secondly, generating a three-dimensional scanning path in combination with the surface features of the measured object, and determining measurement execution parameters; then, synchronously acquiring a ripple noise original signal and spatial position data through hardware triggering, and completing signal filtering processing; and finally, through signal analysis and feature extraction, constructing a three-dimensional space-noise intensity mapping model, and outputting a whole-region measurement result. According to the invention, non-contact full-coverage measurement of the ripple noise of the to-be-measured area is realized, accurate correspondence between a measurement signal and a spatial position is ensured, the effectiveness and integrity of measurement data are improved, and reliable support is provided for performance evaluation of the measured equipment.

Description

Technical Field

[0001] This invention relates to the field of industrial precision measurement and electrical performance testing technology, and in particular to a non-contact ripple noise measurement method and system based on an industrial robotic arm. Background Technology

[0002] With the rapid development of high-end equipment manufacturing, power electronic devices, and precision industrial testing, ripple noise, as a core indicator for measuring the operating performance and electrical stability of electrical equipment and precision components, directly impacts equipment quality control, performance optimization, and fault diagnosis. In the field of industrial measurement, non-contact measurement technology, with its advantages of no contact damage, adaptability to complex surface morphologies, and ability to achieve dynamic continuous measurement, is gradually replacing traditional contact measurement methods and becoming the mainstream development direction in precision ripple noise measurement. Meanwhile, six-axis industrial robotic arms, with their multi-degree-of-freedom motion, high repeatability, and ability to achieve automated continuous operation, are deeply integrated with non-contact sensing technology, laser tracking positioning technology, and multi-channel synchronous data acquisition technology, and are widely used in various industrial automation precision measurement scenarios. Currently, the industry has gradually formed a ripple noise measurement technology system based on automated execution equipment, non-contact sensing as the core, and signal processing and spatial positioning technologies as support. Related technical solutions are continuously iterating and optimizing to adapt to complex industrial environments, diverse measured object shapes, and high-precision measurement requirements.

[0003] In practical applications of existing ripple noise measurement technologies, several limitations remain, failing to fully meet the demands for comprehensive, high-precision, and highly synchronous measurement in industrial settings. Existing measurement schemes struggle to achieve unified spatial references across multiple devices and stages, easily leading to misalignment between acquired ripple noise signals and corresponding spatial location data, thus failing to guarantee accurate correspondence between measurement data and the measured location. Furthermore, existing schemes struggle to combine the surface features of the measured object with a unified spatial reference to complete adaptive scanning path planning, failing to achieve comprehensive, blind-spot-free measurement of the test area, and prone to data loss. In addition, the synchronization between robotic arm motion control and data acquisition in existing schemes is insufficient, failing to achieve synchronous acquisition and accurate matching of ripple noise signals and spatial location data. Moreover, the interference filtering effect on the original acquired signals is limited, making it difficult to effectively extract valid ripple noise signals. Moreover, existing schemes primarily output single-point measurement results, failing to establish a correlation mapping between spatial location and noise intensity, making it difficult to completely and continuously characterize the ripple noise distribution of the test area, and thus unable to provide complete comprehensive measurement data support for the performance evaluation of the tested equipment. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a non-contact ripple noise measurement method and system based on an industrial robotic arm.

[0005] The objective of this invention is achieved through the following technical solution: A non-contact ripple noise measurement method based on an industrial robotic arm is provided, the method comprising the following steps: S1. Establish a global working coordinate system, which is a three-dimensional rectangular coordinate system that serves as a unified spatial position reference in the field of industrial measurement. It includes the spatial coordinate reference system corresponding to the measurement base station and the tracking target ball. Complete the zero-point calibration of the non-contact capacitance measurement probe, which is a general-purpose sensing device for non-contact electrical signal measurement, including a capacitance sensing unit and a signal output unit. Complete the repeatability accuracy verification of the six-axis industrial robotic arm and output the basic parameters of the calibrated measurement system. S2. Combining the global working coordinate system and the calibrated basic parameters of the measurement system, collect the surface topology data of the object under test, identify the surface features and measurement boundaries of the object under test, generate a three-dimensional scanning path, adjust the motion posture and measurement distance of the non-contact capacitive measurement probe, and determine the measurement execution parameters. S3. According to the three-dimensional scanning path and measurement execution parameters, execute the real-time motion control of the six-axis industrial robot arm. Through hardware triggering, synchronously collect the original ripple noise signal of the measured object and the real-time spatial position data of the six-axis industrial robot arm. Perform hardware low-pass filtering on the original ripple noise signal and output the filtered ripple noise signal. S4. Perform noise separation and spectrum analysis on the filtered ripple noise signal, extract ripple noise characteristic parameters, match the ripple noise characteristic parameters with the real-time spatial location data one by one, construct a three-dimensional space-noise intensity mapping model, complete the ripple noise distribution measurement of the test area of ​​the object under test based on the three-dimensional space-noise intensity mapping model, and output the full area measurement results of ripple noise.

[0006] Furthermore, step S1 includes the following sub-steps: S1.1. Deploy measurement base stations, set up tracking target balls at the end of the six-axis industrial robotic arm, collect spatial position data of the tracking target balls, and establish a global working coordinate system; S1.2. Connect to a standard signal source, set the bandwidth limit for the non-contact capacitance measurement probe, complete the zero-point calibration of the non-contact capacitance measurement probe, and output the calibration parameters of the non-contact capacitance measurement probe. S1.3. Drive the six-axis industrial robot arm to complete the repetitive reciprocating motion of the preset point, collect the actual motion position data of the six-axis industrial robot arm, complete the verification of the repeatability of the six-axis industrial robot arm, and output the motion control reference parameters of the six-axis industrial robot arm. S1.4. Integrate the calibration parameters of the non-contact capacitance measurement probe with the motion control reference parameters of the six-axis industrial robotic arm to form the basic parameters of the calibrated measurement system.

[0007] Furthermore, step S2 includes the following sub-steps: S2.1. Combining the global working coordinate system and the calibrated basic parameters of the measurement system, collect the surface point cloud data of the object under test, generate the surface topology data of the object under test, and identify the surface features, measurement boundaries and the area to be measured of the object under test. S2.2. Combining the global working coordinate system, the calibration parameters of the non-contact capacitance measurement probe and the motion control reference parameters of the six-axis industrial robotic arm, a three-dimensional scanning path is generated based on the surface features of the object being measured and the area to be measured, and the motion posture of the non-contact capacitance measurement probe at each point on the three-dimensional scanning path is planned. S2.3. By combining the surface topology data of the object under test with the three-dimensional scanning path, adjust the measurement distance between the non-contact capacitance measurement probe and the surface of the object under test, determine the scanning speed of the six-axis industrial robotic arm and the trigger parameters for data acquisition, and integrate them to form the measurement execution parameters.

[0008] Furthermore, step S3 includes the following sub-steps: S3.1. Real-time motion control of the six-axis industrial robotic arm is achieved through the industrial Ethernet bus, which is a common real-time communication bus in the field of industrial control; the six-axis industrial robotic arm is driven to move the non-contact capacitance measurement probe along the area to be measured according to the scanning speed in the three-dimensional scanning path and measurement execution parameters. S3.2. According to the data acquisition trigger parameters in the measurement execution parameters, the raw ripple noise signal output by the non-contact capacitance measurement probe and the real-time spatial position data of the six-axis industrial robot arm are synchronously acquired through hardware triggering. S3.3. Perform hardware low-pass filtering on the original ripple noise signal. Hardware low-pass filtering is a common signal processing method in the field of signal conditioning. Filter out the high-frequency interference components in the original ripple noise signal and output the filtered ripple noise signal.

[0009] Furthermore, step S4 includes the following sub-steps: S4.1. Perform wavelet denoising on the filtered ripple noise signal. Wavelet denoising is a common noise separation method in the field of signal processing. Complete the separation of the effective ripple noise signal from the background interference noise, and output the denoised effective ripple noise signal. S4.2. Perform windowed Fast Fourier Transform (FSFT) processing on the denoised ripple noise effective signal. Windowed FFT is a common signal processing method in the field of spectrum analysis. Complete the spectrum analysis of the ripple noise effective signal and output the spectrum data of the ripple noise. S4.3. Extract ripple noise feature parameters from the spectral data based on ripple noise, match the ripple noise feature parameters with the real-time spatial location data at the corresponding acquisition time, and construct a three-dimensional space-noise intensity mapping model. The three-dimensional space-noise intensity mapping model is a mathematical model for the association between spatial data and signal features. S4.4. Combining the three-dimensional space-noise intensity mapping model with the test area of ​​the object under test, the ripple noise distribution of the test area of ​​the object under test is measured, and the full-area ripple noise measurement results are output.

[0010] Furthermore, in step S2, a 3D scanning path planning module is constructed, which includes a coordinate matching unit, a path generation unit, and a posture planning unit. The input of the coordinate matching unit is connected to the global working coordinate system, the output of the coordinate matching unit is connected to the input of the path generation unit, and the output of the path generation unit is connected to the input of the posture planning unit. The coordinate matching unit matches the surface features of the object under test with the area to be tested to the global working coordinate system, generating global coordinate distribution data of the area to be tested. Based on the global coordinate distribution data of the area to be tested and the calibration parameters of the non-contact capacitance measurement probe, the path generation unit generates a continuous 3D scanning path covering the entire area to be tested. Based on the 3D scanning path and the motion control reference parameters of the six-axis industrial robotic arm, the posture planning unit plans the motion posture of the non-contact capacitance measurement probe at each point on the 3D scanning path.

[0011] Furthermore, in step S3, a multi-channel synchronous data acquisition module is constructed. This module is a general-purpose functional module in the field of data acquisition, comprising a hardware trigger unit, three measurement signal acquisition channels, one reference signal acquisition channel, and a position data acquisition channel. The output of the hardware trigger unit is connected to the inputs of the three measurement signal acquisition channels, the one reference signal acquisition channel, and the position data acquisition channel, respectively. The hardware trigger unit receives the data acquisition trigger parameters from the measurement execution parameters and generates a synchronous trigger signal. The synchronous trigger signal is synchronously distributed to the three measurement signal acquisition channels, the one reference signal acquisition channel, and the position data acquisition channel. The raw ripple noise signal output by the non-contact capacitance measurement probe is synchronously acquired through the three measurement signal acquisition channels, the reference reference signal of the measurement system is synchronously acquired through the one reference signal acquisition channel, and the real-time spatial position data of the six-axis industrial robotic arm is synchronously acquired through the position data acquisition channel.

[0012] Furthermore, in step S4.1, a wavelet denoising module is constructed. This module is a general-purpose functional module in the field of signal processing, comprising a signal decomposition unit, a thresholding unit, and a signal reconstruction unit. The input of the signal decomposition unit is connected to the filtered ripple noise signal, and the output of the signal decomposition unit is connected to the input of the thresholding unit. The output of the thresholding unit is also connected to the input of the signal reconstruction unit. The signal decomposition unit uses the Daubechies wavelet basis to perform multi-level wavelet decomposition on the filtered ripple noise signal, generating multi-level wavelet decomposition coefficients. The thresholding unit uses a fixed threshold to perform thresholding on the high-frequency coefficients in the multi-level wavelet decomposition coefficients, filtering out interference noise components in the high-frequency coefficients. The signal reconstruction unit reconstructs the wavelet signal based on the processed high-frequency coefficients and the unprocessed low-frequency coefficients, outputting the denoised effective ripple noise signal.

[0013] Furthermore, in step 4.3, a three-dimensional space-noise intensity mapping model construction module is constructed. This module includes a data matching unit, a mesh generation unit, an interpolation fitting unit, and a model generation unit. The input of the data matching unit is connected to the ripple noise characteristic parameters and the real-time spatial location data, respectively. The output of the data matching unit is connected to the input of the mesh generation unit, the output of the mesh generation unit is connected to the input of the interpolation fitting unit, and the output of the interpolation fitting unit is connected to the input of the model generation unit. The data matching unit binds the ripple noise characteristic parameters to the real-time spatial location data at the corresponding acquisition time, generating a ripple noise data point set with spatial coordinates. The mesh generation unit performs three-dimensional structured mesh generation on the area to be measured based on the global working coordinate system, generating a three-dimensional mesh matrix of the area to be measured. The interpolation fitting unit matches the ripple noise data point set with spatial coordinates to the three-dimensional mesh matrix and uses Kriging interpolation to complete the noise data fitting of the entire mesh. The model generation unit constructs a three-dimensional space-noise intensity mapping model based on the fitted three-dimensional mesh matrix.

[0014] A non-contact ripple noise measurement system based on an industrial robotic arm is provided. This system includes a mechanical motion subsystem, a measurement sensing unit, a data acquisition module, and a signal processing module. The mechanical motion subsystem includes a six-axis industrial robotic arm, a laser tracking device, and a motion control module. The motion control module achieves real-time motion control of the six-axis industrial robotic arm via an industrial Ethernet bus. The measurement sensing unit includes a non-contact capacitance measurement probe, a grounding ring structure, and a signal conditioning module. The grounding ring structure is an anti-interference structure in the electromagnetic compatibility field, comprising a low-impedance conductive ring surrounding the sensing end of the non-contact capacitance measurement probe. The grounding ring structure is configured in conjunction with the non-contact capacitance measurement probe. The data acquisition module is electrically connected to the measurement sensing unit and the mechanical motion subsystem to achieve synchronous acquisition of ripple noise signals and spatial position data. The signal processing module is electrically connected to the data acquisition module to complete ripple noise signal processing and construct a three-dimensional space-noise intensity mapping model, outputting full-area ripple noise measurement results.

[0015] The beneficial effects of this invention are: (1) Through the unified spatial reference system calibration, scanning path planning, synchronous data acquisition and signal processing, the non-contact full-range measurement of the target ripple signal in the area to be measured is realized, ensuring the accurate correspondence between the acquired signal and the spatial location data; (2) By combining the multi-channel synchronous acquisition mechanism with hardware pre-filtering, the timing misalignment and high-frequency interference problems in the signal acquisition process are eliminated, and the signal-to-noise ratio of the original measurement signal and the synchronization of data acquisition are improved. (3) By extracting ripple signal features and constructing a three-dimensional space-noise intensity mapping model, the continuous characterization of ripple noise distribution in the area to be measured is completed, providing complete data support for the performance evaluation of the measured object. Attached Figure Description

[0016] Figure 1 A flowchart of a non-contact ripple noise measurement method based on an industrial robotic arm; Figure 2 The following is a flowchart illustrating the specific steps of a non-contact ripple noise measurement method based on an industrial robotic arm, as provided in the embodiments. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1 See Figure 1 This embodiment provides a non-contact ripple noise measurement method based on an industrial robotic arm, which includes the following steps: S1. Establish a global working coordinate system, which is a three-dimensional rectangular coordinate system that serves as a unified spatial position reference in the field of industrial measurement. It includes the spatial coordinate reference system corresponding to the measurement base station and the tracking target ball. Complete the zero-point calibration of the non-contact capacitance measurement probe, which is a general-purpose sensing device for non-contact electrical signal measurement, including a capacitance sensing unit and a signal output unit. Complete the repeatability accuracy verification of the six-axis industrial robotic arm and output the basic parameters of the calibrated measurement system. S2. Combining the global working coordinate system and the calibrated basic parameters of the measurement system, collect the surface topology data of the object under test, identify the surface features and measurement boundaries of the object under test, generate a three-dimensional scanning path, adjust the motion posture and measurement distance of the non-contact capacitive measurement probe, and determine the measurement execution parameters. S3. According to the three-dimensional scanning path and measurement execution parameters, execute the real-time motion control of the six-axis industrial robot arm. Through hardware triggering, synchronously collect the original ripple noise signal of the measured object and the real-time spatial position data of the six-axis industrial robot arm. Perform hardware low-pass filtering on the original ripple noise signal and output the filtered ripple noise signal. S4. Perform noise separation and spectrum analysis on the filtered ripple noise signal, extract ripple noise characteristic parameters, match the ripple noise characteristic parameters with the real-time spatial location data one by one, construct a three-dimensional space-noise intensity mapping model, complete the ripple noise distribution measurement of the test area of ​​the object under test based on the three-dimensional space-noise intensity mapping model, and output the full area measurement results of ripple noise.

[0019] In some specific implementations, step S1 includes the following sub-steps: S1.1. Deploy measurement base stations, set up tracking target balls at the end of the six-axis industrial robotic arm, collect spatial position data of the tracking target balls, and establish a global working coordinate system; S1.2. Connect to a standard signal source, set the bandwidth limit for the non-contact capacitance measurement probe, complete the zero-point calibration of the non-contact capacitance measurement probe, and output the calibration parameters of the non-contact capacitance measurement probe. S1.3. Drive the six-axis industrial robot arm to complete the repetitive reciprocating motion of the preset point, collect the actual motion position data of the six-axis industrial robot arm, complete the verification of the repeatability of the six-axis industrial robot arm, and output the motion control reference parameters of the six-axis industrial robot arm. S1.4. Integrate the calibration parameters of the non-contact capacitance measurement probe with the motion control reference parameters of the six-axis industrial robotic arm to form the basic parameters of the calibrated measurement system.

[0020] In some specific implementations, step S2 includes the following sub-steps: S2.1. Combining the global working coordinate system and the calibrated basic parameters of the measurement system, collect the surface point cloud data of the object under test, generate the surface topology data of the object under test, and identify the surface features, measurement boundaries and the area to be measured of the object under test. S2.2. Combining the global working coordinate system, the calibration parameters of the non-contact capacitance measurement probe and the motion control reference parameters of the six-axis industrial robotic arm, a three-dimensional scanning path is generated based on the surface features of the object being measured and the area to be measured, and the motion posture of the non-contact capacitance measurement probe at each point on the three-dimensional scanning path is planned. S2.3. By combining the surface topology data of the object under test with the three-dimensional scanning path, adjust the measurement distance between the non-contact capacitance measurement probe and the surface of the object under test, determine the scanning speed of the six-axis industrial robotic arm and the trigger parameters for data acquisition, and integrate them to form the measurement execution parameters.

[0021] In some specific implementations, step S3 includes the following sub-steps: S3.1. Real-time motion control of the six-axis industrial robotic arm is achieved through the industrial Ethernet bus, which is a common real-time communication bus in the field of industrial control; the six-axis industrial robotic arm is driven to move the non-contact capacitance measurement probe along the area to be measured according to the scanning speed in the three-dimensional scanning path and measurement execution parameters. S3.2. According to the data acquisition trigger parameters in the measurement execution parameters, the raw ripple noise signal output by the non-contact capacitance measurement probe and the real-time spatial position data of the six-axis industrial robot arm are synchronously acquired through hardware triggering. S3.3. Perform hardware low-pass filtering on the original ripple noise signal. Hardware low-pass filtering is a common signal processing method in the field of signal conditioning. Filter out the high-frequency interference components in the original ripple noise signal and output the filtered ripple noise signal.

[0022] In some specific implementations, step S4 includes the following sub-steps: S4.1. Perform wavelet denoising on the filtered ripple noise signal. Wavelet denoising is a common noise separation method in the field of signal processing. Complete the separation of the effective ripple noise signal from the background interference noise, and output the denoised effective ripple noise signal. S4.2. Perform windowed Fast Fourier Transform (FSFT) processing on the denoised ripple noise effective signal. Windowed FFT is a common signal processing method in the field of spectrum analysis. Complete the spectrum analysis of the ripple noise effective signal and output the spectrum data of the ripple noise. S4.3. Extract ripple noise feature parameters from the spectral data based on ripple noise, match the ripple noise feature parameters with the real-time spatial location data at the corresponding acquisition time, and construct a three-dimensional space-noise intensity mapping model. The three-dimensional space-noise intensity mapping model is a mathematical model for the association between spatial data and signal features. S4.4. Combining the three-dimensional space-noise intensity mapping model with the test area of ​​the object under test, the ripple noise distribution of the test area of ​​the object under test is measured, and the full-area ripple noise measurement results are output.

[0023] In some specific implementations, a 3D scanning path planning module is constructed, which includes a coordinate matching unit, a path generation unit, and a posture planning unit. The input of the coordinate matching unit is connected to the global working coordinate system, and the output of the coordinate matching unit is connected to the input of the path generation unit, which in turn is connected to the input of the posture planning unit. The coordinate matching unit matches the surface features of the object under test with the area to be measured to the global working coordinate system, generating global coordinate distribution data for the area to be measured. Based on the global coordinate distribution data of the area to be measured and the calibration parameters of the non-contact capacitance measurement probe, the path generation unit generates a continuous 3D scanning path covering the entire area to be measured. Based on the 3D scanning path and the motion control reference parameters of the six-axis industrial robotic arm, the posture planning unit plans the motion posture of the non-contact capacitance measurement probe at each point along the 3D scanning path.

[0024] In some specific implementations, a multi-channel synchronous data acquisition module is constructed. This module is a general-purpose functional module in the field of data acquisition, comprising a hardware trigger unit, three measurement signal acquisition channels, one reference signal acquisition channel, and a position data acquisition channel. The output of the hardware trigger unit is connected to the inputs of the three measurement signal acquisition channels, the one reference signal acquisition channel, and the position data acquisition channel, respectively. The hardware trigger unit receives the data acquisition trigger parameters from the measurement execution parameters and generates a synchronous trigger signal. The synchronous trigger signal is then synchronously distributed to the three measurement signal acquisition channels, the one reference signal acquisition channel, and the position data acquisition channel. The raw ripple noise signal output by the non-contact capacitance measurement probe is synchronously acquired through the three measurement signal acquisition channels, the reference signal of the measurement system is synchronously acquired through the one reference signal acquisition channel, and the real-time spatial position data of the six-axis industrial robotic arm is synchronously acquired through the position data acquisition channel.

[0025] In some specific implementations, a wavelet denoising module is constructed. This module is a general-purpose functional module in the field of signal processing, comprising a signal decomposition unit, a thresholding unit, and a signal reconstruction unit. The input of the signal decomposition unit is connected to the filtered ripple noise signal, and the output of the signal decomposition unit is connected to the input of the thresholding unit, which in turn is connected to the input of the signal reconstruction unit. The signal decomposition unit uses the Daubechies wavelet basis to perform multi-level wavelet decomposition on the filtered ripple noise signal, generating multi-level wavelet decomposition coefficients. The thresholding unit uses a fixed threshold to perform thresholding on the high-frequency coefficients in the multi-level wavelet decomposition coefficients, filtering out interference noise components in the high-frequency coefficients. The signal reconstruction unit reconstructs the wavelet signal based on the processed high-frequency coefficients and the unprocessed low-frequency coefficients, outputting the denoised effective ripple noise signal.

[0026] In some specific implementations, a three-dimensional space-noise intensity mapping model construction module is constructed. This module includes a data matching unit, a mesh generation unit, an interpolation fitting unit, and a model generation unit. The input of the data matching unit is connected to the ripple noise characteristic parameters and real-time spatial location data, respectively. The output of the data matching unit is connected to the input of the mesh generation unit, the output of the mesh generation unit is connected to the input of the interpolation fitting unit, and the output of the interpolation fitting unit is connected to the input of the model generation unit. The data matching unit binds the ripple noise characteristic parameters to the real-time spatial location data at the corresponding acquisition time, generating a ripple noise data point set with spatial coordinates. The mesh generation unit performs three-dimensional structured mesh generation on the area to be measured based on the global working coordinate system, generating a three-dimensional mesh matrix of the area to be measured. The interpolation fitting unit matches the ripple noise data point set with spatial coordinates to the three-dimensional mesh matrix and uses Kriging interpolation to complete the noise data fitting of the entire mesh. The model generation unit constructs a three-dimensional space-noise intensity mapping model based on the fitted three-dimensional mesh matrix.

[0027] A non-contact ripple noise measurement system based on an industrial robotic arm is provided. This system includes a mechanical motion subsystem, a measurement sensing unit, a data acquisition module, and a signal processing module. The mechanical motion subsystem includes a six-axis industrial robotic arm, a laser tracking device, and a motion control module. The motion control module achieves real-time motion control of the six-axis industrial robotic arm via an industrial Ethernet bus. The measurement sensing unit includes a non-contact capacitance measurement probe, a grounding ring structure, and a signal conditioning module. The grounding ring structure is an anti-interference structure in the electromagnetic compatibility field, comprising a low-impedance conductive ring surrounding the sensing end of the non-contact capacitance measurement probe. The grounding ring structure is configured in conjunction with the non-contact capacitance measurement probe. The data acquisition module is electrically connected to the measurement sensing unit and the mechanical motion subsystem to achieve synchronous acquisition of ripple noise signals and spatial position data. The signal processing module is electrically connected to the data acquisition module to complete ripple noise signal processing and construct a three-dimensional space-noise intensity mapping model, outputting full-area ripple noise measurement results.

[0028] Example 2 This embodiment provides a specific implementation process for a non-contact ripple noise measurement method based on an industrial robotic arm. The method described in this embodiment is used to achieve non-contact, full-area measurement of ripple noise on the surface of the object being measured, ensuring accurate correspondence between the ripple noise signal and spatial position data. Figure 2 As shown, the implementation process consists of the following four main steps: S1. Coordinate system establishment and system calibration: S1.1. Establishment of the global working coordinate system: The global working coordinate system is a three-dimensional Cartesian coordinate system used in industrial measurement to unify spatial position references. It includes the spatial coordinate reference system corresponding to the measurement base station and the tracking target ball. In this embodiment, the global working coordinate system is used to unify the spatial position references of the six-axis industrial robot arm, the non-contact capacitance measurement probe, and the object being measured, ensuring that the spatial coordinates of all collected data are consistent and avoiding measurement errors caused by position deviations. The specific implementation process of this step is as follows: deploy the measurement base station, set up the tracking target ball at the end of the six-axis industrial robot arm, collect the spatial position data of the tracking target ball, and establish the global working coordinate system.

[0029] S1.2. Calibration of non-contact capacitance measurement probe: The non-contact capacitance measurement probe is a general-purpose sensing device in the field of non-contact electrical signal measurement. It includes a capacitance sensing unit and a signal output unit. In this embodiment, the non-contact capacitance measurement probe is used to acquire the raw ripple noise signal of the object under test. The specific implementation process of this step is as follows: connect a standard signal source, set the bandwidth limitation of the non-contact capacitance measurement probe, complete the zero-point calibration of the non-contact capacitance measurement probe, and output the calibration parameters of the non-contact capacitance measurement probe.

[0030] S1.3. Precision Verification of a Six-Axis Industrial Robotic Arm: A six-axis industrial robotic arm is a general-purpose actuator with six degrees of freedom in the field of industrial automation. In this embodiment, the six-axis industrial robotic arm is used to drive a non-contact capacitance measurement probe to complete a preset path of motion. The specific implementation process of this step is as follows: driving the six-axis industrial robotic arm to complete repetitive back-and-forth motion at preset points, collecting the actual motion position data of the six-axis industrial robotic arm, verifying the repeatability accuracy of the six-axis industrial robotic arm, and outputting the motion control reference parameters of the six-axis industrial robotic arm.

[0031] S1.4. Integration of basic parameters of the measurement system: The measurement system's basic parameters are a set of foundational calibration data used to guide subsequent measurement processes. In this embodiment, the measurement system's basic parameters provide a unified benchmark for subsequent path planning, motion control, and data acquisition. Specifically, this step involves integrating the calibration parameters of the non-contact capacitance measurement probe with the motion control benchmark parameters of the six-axis industrial robotic arm to form the calibrated measurement system's basic parameters.

[0032] In some specific implementations, addressing the issues of misalignment between measurement positions and actual acquired signals caused by inconsistent spatial references among multiple devices and insufficient system calibration accuracy in industrial measurement sites, a standardized implementation with numerical constraints is implemented for the entire process of establishing a global working coordinate system, calibrating non-contact capacitance measurement probes, and verifying the accuracy of a six-axis industrial robotic arm. First, the measurement base stations are deployed. Two laser tracking measurement base stations are set up around the object being measured, with a spacing of 3m to 5m between them. The line-of-sight of the two measurement base stations covers the entire area to be measured and the full range of motion of the six-axis industrial robotic arm, avoiding the problem of tracking target ball obstruction during the measurement process. A tracking target ball is fixedly installed at the end flange position of the six-axis industrial robot arm. The relative position deviation between the installation position of the tracking target ball and the center of the sensing end of the non-contact capacitance measurement probe is controlled within ±0.02mm. The coordinate data of the tracking target ball at 12 preset spatial points are collected synchronously through two measurement base stations. The 12 preset spatial points cover the 8 vertices and 4 face center points of the entire motion space of the six-axis industrial robot arm. Based on the collected coordinate data, the global working coordinate system is established. The spatial positioning error of the established global working coordinate system is controlled within 0.05mm.

[0033] Subsequently, the non-contact capacitance measurement probe was calibrated. A standard sine wave signal source with a peak-to-peak value of 1V and a frequency of 1kHz was connected. The bandwidth of the non-contact capacitance measurement probe was limited to 10Hz to 1MHz, and the zero-point calibration of the probe was completed. The zero-point drift of the calibrated probe was controlled within ±0.5mV, and the calibration parameters of the non-contact capacitance measurement probe were output. Finally, the repeatability accuracy of the six-axis industrial robot was verified. The six-axis industrial robot was driven to complete 10 repetitive back-and-forth movements at 6 preset measurement points. The positioning dwell time for each back-and-forth movement was set to 2s. The actual movement position data of the six-axis industrial robot at each preset point was collected, and the repeatability accuracy of the six-axis industrial robot was calculated to be controlled within ±0.03mm. The motion control reference parameters of the six-axis industrial robot were output. Finally, the calibration parameters and motion control reference parameters were integrated to form the basic parameters of the calibrated measurement system, providing a unified high-precision spatial reference for subsequent path planning and motion control, reducing the measurement error caused by position deviation from the source.

[0034] S2. Scan path generation and measurement parameter determination: S2.1. Identification of surface features of the object being measured: Surface point cloud data is a set of discrete coordinate data used in the field of 3D measurement to characterize the spatial morphology of an object's surface. In this embodiment, the surface point cloud data is used to reconstruct the surface morphology of the object under test, providing a basis for scanning path planning. Surface topology data is structured data characterizing the relative positions and connections of points on the object's surface. In this embodiment, the surface topology data is used to determine the movable range and attitude adjustment boundaries of the non-contact capacitance measurement probe. The specific implementation process of this step is as follows: combining the global working coordinate system and the calibrated basic parameters of the measurement system, the surface point cloud data of the object under test is collected, the surface topology data of the object under test is generated, and the surface features, measurement boundaries, and measurement areas of the object under test are identified.

[0035] S2.2. 3D scanning path planning: The 3D scanning path planning module is a functional module used in industrial measurement to generate interference-free measurement paths based on the characteristics of the object being measured and the spatial coordinate system. The module includes a coordinate matching unit, a path generation unit, and a posture planning unit. The input of the coordinate matching unit is connected to the global working coordinate system, and its output is connected to the input of the path generation unit. The output of the path generation unit is also connected to the input of the posture planning unit. Specifically, the coordinate matching unit matches the surface features of the object being measured with the area to be measured to the global working coordinate system, generating global coordinate distribution data for the area to be measured. The path generation unit, based on the global coordinate distribution data of the area to be measured and the calibration parameters of the non-contact capacitance measurement probe, generates a continuous 3D scanning path covering the entire area to be measured. The posture planning unit, based on the 3D scanning path and the motion control reference parameters of the six-axis industrial robotic arm, plans the motion posture of the non-contact capacitance measurement probe at each point along the 3D scanning path.

[0036] In some embodiments, a spiral scanning path can be used instead of a continuous linear scanning path to adapt to the test area of ​​curved objects and ensure that the measurement distance between the non-contact capacitance measurement probe and the surface of the object remains stable.

[0037] S2.3. Determination of measurement execution parameters: The measurement execution parameters are a set of all control parameters used to guide the subsequent movement and data acquisition actions of the six-axis industrial robot arm. In this embodiment, the measurement execution parameters provide a unified execution benchmark for subsequent motion control and synchronous acquisition. The specific implementation process of this step is as follows: combining the surface topology data of the object under test and the 3D scanning path, adjusting the measurement distance between the non-contact capacitance measurement probe and the surface of the object under test, determining the scanning speed of the six-axis industrial robot arm and the trigger parameters for data acquisition, and integrating them to form the measurement execution parameters.

[0038] In some specific implementations, for objects with complex curved surface structures, the measured area presents specific problems such as large curvature variations, numerous concave and convex structures, inability to achieve full coverage by conventional scanning paths, measurement signal distortion due to the non-perpendicularity of the non-contact capacitance measurement probe to the measured surface, and excessive measurement distance deviation. Therefore, a refined implementation with numerical constraints is carried out on the process of 3D scanning path planning and measurement execution parameter determination. First, combining the global working coordinate system and the calibrated basic parameters of the measurement system, a laser tracking device is used to collect point cloud data of the object's surface. The sampling interval for point cloud acquisition is set to 0.2 mm. After acquisition, the surface point cloud data is denoised and simplified to generate surface topology data of the object. Based on the surface topology data, the surface features, measurement boundaries, and the measured area of ​​the object are identified. The deviation between the identified edge contour of the measured area and the actual measured area is controlled within 0.1 mm.

[0039] Subsequently, the 3D scanning path planning module completes path generation and posture planning. The coordinate matching unit matches the identified surface features of the object under test with the area to be tested to the global working coordinate system, generating global coordinate distribution data for the area to be tested. Based on the global coordinate distribution data of the area to be tested and the calibration parameters of the non-contact capacitance measurement probe, the path generation unit generates a continuous 3D scanning path covering the entire area to be tested. The row spacing of the scanning path is set to 0.5mm, and the inflection point transition radius of the path is set to 2mm to avoid sudden speed changes during the movement of the six-axis industrial robot. Based on the 3D scanning path and the motion control reference parameters of the six-axis industrial robot, the posture planning unit plans the motion posture of the non-contact capacitance measurement probe at each point on the 3D scanning path, ensuring that the angle between the central axis of the probe's sensing end and the normal of the surface of the object under test is controlled within ±1°. At the same time, the interference verification of the path is completed, and a safety distance threshold of 10mm is set to avoid the risk of mechanical interference between the six-axis industrial robot and the object under test and surrounding fixed equipment.

[0040] Finally, by combining the surface topology data of the object under test with the 3D scanning path, the measurement distance between the non-contact capacitance measurement probe and the surface of the object under test was adjusted to keep the measurement distance stable within the range of 1mm ± 0.05mm. The scanning speed of the six-axis industrial robotic arm was set to 5mm / s, and the triggering parameters for data acquisition were set to equal-interval triggering with a trigger interval of 0.1mm. These parameters were integrated to form the measurement execution parameters, ensuring that the measurement posture and measurement distance of the probe remain stable during the scanning process. This achieves full coverage measurement of the area under test without blind spots, avoiding measurement data loss and signal distortion caused by incomplete path coverage and posture deviation.

[0041] S3. Motion control and synchronous data acquisition: S3.1. Real-time motion control of a six-axis industrial robotic arm: The Industrial Ethernet bus is a general-purpose communication bus used in the industrial control field to realize periodic real-time data exchange between industrial devices. In this embodiment, the Industrial Ethernet bus is used to realize the real-time issuance of motion control commands and the real-time feedback of the motion status of the six-axis industrial robot, ensuring the consistency of the six-axis industrial robot's motion with the preset three-dimensional scanning path. The specific implementation process of this step is as follows: the real-time motion control of the six-axis industrial robot is realized through the Industrial Ethernet bus. According to the three-dimensional scanning path and the scanning speed in the measurement execution parameters, the six-axis industrial robot is driven to move the non-contact capacitance measurement probe along the area to be measured.

[0042] S3.2. Multi-channel synchronous data acquisition control: The multi-channel synchronous data acquisition module is a general-purpose functional module in the field of data acquisition used to achieve synchronous triggering and acquisition of multiple signals. The module includes a hardware trigger unit, three measurement signal acquisition channels, one reference signal acquisition channel, and a position data acquisition channel. The output of the hardware trigger unit is connected to the inputs of the three measurement signal acquisition channels, the reference signal acquisition channel, and the position data acquisition channel, respectively. The specific implementation process is as follows: the hardware trigger unit receives the data acquisition trigger parameters from the measurement execution parameters and generates a synchronous trigger signal; this synchronous trigger signal is then synchronously distributed to the three measurement signal acquisition channels, the reference signal acquisition channel, and the position data acquisition channel; the three measurement signal acquisition channels synchronously acquire the raw ripple noise signal output from the non-contact capacitance measurement probe; the reference signal acquisition channel synchronously acquires the reference signal of the measurement system; and the position data acquisition channel synchronously acquires the real-time spatial position data of the six-axis industrial robotic arm.

[0043] S3.3. Hardware filtering of the original signal: Hardware low-pass filtering is a common signal processing method in the field of signal conditioning, which uses hardware filtering circuits to remove interference components in the input signal that are higher than a set cutoff frequency. In this embodiment, hardware low-pass filtering is used to remove high-frequency electromagnetic interference components from the original ripple noise signal, retaining the effective ripple noise signal. The specific implementation process of this step is as follows: hardware low-pass filtering is performed on the original ripple noise signal to remove high-frequency interference components, and the filtered ripple noise signal is output.

[0044] In some embodiments, hardware bandpass filtering can be added on top of hardware low-pass filtering to limit the effective frequency band for signal acquisition based on the frequency range of the ripple signal of the object under test, thereby further reducing the impact of out-of-band interference.

[0045] In some specific implementations, to address the specific problems of asynchronous timing between ripple noise signal acquisition and spatial position data acquisition during the continuous motion of a six-axis industrial robotic arm, timing misalignment of multi-channel acquired signals, and low signal-to-noise ratio of the original signal due to high-frequency electromagnetic interference in the industrial environment, a synchronization implementation with numerical constraints is carried out on the entire process of real-time motion control of the six-axis industrial robotic arm, multi-channel synchronous data acquisition, and hardware filtering of the original signal.

[0046] First, real-time motion control of the six-axis industrial robot is achieved through an industrial Ethernet bus. The communication cycle of the industrial Ethernet bus is set to 1ms. The issuance of motion control commands and the feedback of the motion status of the six-axis industrial robot are completed within the same communication cycle. According to the 3D scanning path and the scanning speed of 5mm / s in the measurement execution parameters, the six-axis industrial robot is driven to move the non-contact capacitance measurement probe continuously and uniformly along the area to be measured. The speed fluctuation during the movement is controlled within ±0.1mm / s to avoid the problem of uneven acquisition spacing caused by speed fluctuation.

[0047] Subsequently, synchronous acquisition control is achieved through a multi-channel synchronous data acquisition module. The hardware trigger unit receives the equally spaced trigger parameters from the measurement execution parameters and generates a synchronous trigger signal. The timing accuracy of the trigger signal is controlled within ±100ns. The synchronous trigger signal is synchronously distributed to three measurement signal acquisition channels, one reference signal acquisition channel, and one position data acquisition channel to ensure that the acquisition actions of all channels are triggered at the same time. The three measurement signal acquisition channels correspond to the three sets of differential sensing acquisition ends of the non-contact capacitance measurement probe, synchronously acquiring the raw ripple noise signal output by the non-contact capacitance measurement probe. One reference signal acquisition channel synchronously acquires the 0V reference signal of the measurement system. The position data acquisition channel synchronously acquires the real-time spatial position data of the end effector of the six-axis industrial robotic arm. The sampling rate of all acquisition channels is set to 10MS / s, the sampling bit depth is set to 16 bits, and the synchronous acquisition timing deviation between channels is controlled within ±200ns, completely avoiding the timing misalignment problem between signal acquisition and position acquisition.

[0048] Finally, the acquired raw ripple noise signal is subjected to hardware low-pass filtering. The hardware low-pass filter circuit adopts an 8th-order Butterworth low-pass filter structure, and the cutoff frequency of the low-pass filter is set to 2MHz to filter out high-frequency electromagnetic interference components above 2MHz in the raw ripple noise signal. The filtered ripple noise signal is output, and the in-band fluctuation of the filtered signal is controlled within ±0.5dB, which effectively improves the signal-to-noise ratio of the raw signal and provides high-quality basic data for subsequent signal processing.

[0049] S4. Signal Processing and Measurement Result Output: S4.1. Ripple noise signal denoising processing: Wavelet denoising is a common noise separation method in signal processing that uses wavelet transform to achieve time-frequency decomposition of a signal, thereby separating the effective signal from background noise. In this embodiment, wavelet denoising is used to remove background interference noise from the filtered ripple noise signal, improving the signal-to-noise ratio of the effective ripple signal. The wavelet denoising module is a general functional module in signal processing used to achieve signal-to-noise separation. The wavelet denoising module includes a signal decomposition unit, a thresholding unit, and a signal reconstruction unit. The input of the signal decomposition unit is connected to the filtered ripple noise signal, and the output of the signal decomposition unit is connected to the input of the thresholding unit. The output of the thresholding unit is connected to the input of the signal reconstruction unit. The specific implementation process of this step is as follows: the signal decomposition unit uses the Daubechies wavelet basis to perform multi-level wavelet decomposition on the filtered ripple noise signal, generating multi-level wavelet decomposition coefficients; the thresholding unit uses a fixed threshold to perform thresholding on the high-frequency coefficients in the multi-level wavelet decomposition coefficients, filtering out interference noise components in the high-frequency coefficients; the signal reconstruction unit performs wavelet signal reconstruction based on the processed high-frequency coefficients and the unprocessed low-frequency coefficients, outputting the denoised effective ripple noise signal.

[0050] S4.2. Ripple noise signal spectrum analysis: Windowed Fast Fourier Transform (FST) is a common signal processing method in the field of spectrum analysis that applies a window function to a time-domain signal and then performs a Fast Fourier Transform to obtain the signal's spectral distribution data. In this embodiment, windowed FFT is used to convert the effective ripple noise signal in the time domain into frequency domain data and extract the signal's frequency distribution characteristics. Specifically, this step involves performing windowed FFT on the denoised ripple noise signal to complete the spectral analysis of the effective ripple noise signal and outputting the ripple noise's spectral data.

[0051] In some embodiments, a Hanning window can be used instead of a rectangular window for windowing to reduce spectral leakage during spectral analysis and improve the accuracy of spectral data.

[0052] S4.3. Construction of the 3D Space-Noise Intensity Mapping Model: The three-dimensional space-noise intensity mapping model is a mathematical model that associates spatial data with signal features. It includes three-dimensional spatial coordinate units, ripple noise feature parameter units corresponding to the coordinates, and spatial interpolation fitting units. In this embodiment, the three-dimensional space-noise intensity mapping model is used to characterize the ripple noise distribution at different spatial locations within the test area. The three-dimensional space-noise intensity mapping model construction module is a functional module used to realize the association and matching between spatial location data and ripple noise feature parameters. The three-dimensional space-noise intensity mapping model construction module includes a data matching unit, a mesh generation unit, an interpolation fitting unit, and a model generation unit. The input terminals of the data matching unit are respectively connected to the ripple noise feature parameters and real-time spatial location data. The output terminal of the data matching unit is connected to the input terminal of the mesh generation unit. The output terminal of the mesh generation unit is connected to the input terminal of the interpolation fitting unit. The output terminal of the interpolation fitting unit is connected to the input terminal of the model generation unit. The specific implementation process of this step is as follows: Ripple noise feature parameters are extracted from the spectral data of ripple noise. These feature parameters include peak-to-peak value, effective value, and noise spectral distribution data. The data matching unit binds the ripple noise feature parameters to the real-time spatial location data at the corresponding acquisition time, generating a set of ripple noise data points with spatial coordinates. The mesh generation unit performs three-dimensional structured mesh generation on the area to be measured based on the global working coordinate system, generating a three-dimensional mesh matrix for the area to be measured. The interpolation fitting unit matches the set of ripple noise data points with spatial coordinates to the three-dimensional mesh matrix and uses Kriging interpolation to complete the noise data fitting of the entire mesh. The model generation unit constructs a three-dimensional space-noise intensity mapping model based on the fitted three-dimensional mesh matrix.

[0053] In some embodiments, inverse distance weighted interpolation can be used instead of Kriging interpolation to fit noisy data across the entire grid, adapting to the test area with uniformly distributed data points and reducing the computational load of data processing.

[0054] S4.4. Ripple noise measurement result output: The full-area ripple noise measurement result is a complete set of measurement data including the spatial coordinates of each point within the test area, the corresponding ripple noise characteristic parameters, and the noise distribution. In this embodiment, the full-area ripple noise measurement result provides complete data support for the ripple noise performance evaluation of the tested object. The specific implementation process of this step is as follows: combining the three-dimensional space-noise intensity mapping model with the test area of ​​the tested object, the ripple noise distribution of the test area of ​​the tested object is measured, and the full-area ripple noise measurement result is output.

[0055] In some embodiments, a heat map of ripple noise distribution in the area to be measured can be generated based on the full-area measurement results of ripple noise, which can intuitively present the distribution of ripple noise in the area to be measured.

[0056] In some specific implementations, to address the specific problems of discretely acquired ripple noise data being unable to continuously characterize the ripple noise distribution across the entire range of the area under test, loss of effective ripple signals during wavelet denoising, inaccurate feature parameter extraction, and insufficient spatial interpolation fitting accuracy, a high-precision implementation with numerical constraints is carried out on the entire process of ripple noise signal denoising, spectrum analysis, three-dimensional space-noise intensity mapping model construction, and measurement result output.

[0057] First, the ripple noise signal is denoised using a wavelet denoising module. The signal decomposition unit uses the Daubechies 6 wavelet basis to perform 6-level wavelet decomposition on the filtered ripple noise signal, generating 6 levels of high-frequency wavelet decomposition coefficients and 1 level of low-frequency wavelet decomposition coefficients. The thresholding unit uses a fixed threshold based on Stein's unbiased risk estimation to perform soft thresholding on the 6 levels of high-frequency wavelet decomposition coefficients, filtering out background interference noise components in the high-frequency coefficients while retaining the high-frequency characteristics of the effective ripple signal. The signal reconstruction unit reconstructs the wavelet signal based on the processed 6 levels of high-frequency coefficients and the unprocessed 1 level of low-frequency coefficients, outputting the denoised effective ripple noise signal. The signal-to-noise ratio of the denoised signal is improved by more than 15dB, while effectively avoiding the loss of the effective ripple signal. Subsequently, the effective signal of the denoised ripple noise is subjected to windowed Fast Fourier Transform (FFT). A Hamming window is used to window the time-domain signal, and the number of points of the FFT is set to 1024. The spectrum analysis of the effective signal of the ripple noise is completed, and the spectrum data of the ripple noise is output. The frequency resolution is controlled within 100Hz.

[0058] Based on the spectral data of ripple noise, three types of ripple noise characteristic parameters are extracted from the 10Hz to 1MHz frequency band: peak-to-peak value of ripple, RMS value of ripple, and noise spectral distribution data. A three-dimensional space-noise intensity mapping model construction module completes the model construction. The data matching unit binds the ripple noise characteristic parameters one-to-one with the real-time spatial location data at the corresponding acquisition time, generating a ripple noise data point set with spatial coordinates. The mesh generation unit performs three-dimensional structured mesh generation on the test area based on the global working coordinate system, with the mesh cell size set to 0.2mm × 0.2mm, generating a three-dimensional mesh matrix for the test area. The interpolation fitting unit matches the ripple noise data point set with spatial coordinates to the three-dimensional mesh matrix, using ordinary kriging interpolation, setting a search range of 12 neighboring data points to complete the noise data fitting of the entire mesh. The deviation between the fitted mesh data and the measured data is controlled within 5%. The model generation unit constructs a three-dimensional space-noise intensity mapping model based on the fitted three-dimensional mesh matrix. Finally, by combining the three-dimensional space-noise intensity mapping model with the test area of ​​the object under test, the ripple noise distribution of the test area of ​​the object under test is measured, and the full-area measurement results of ripple noise are output. This enables continuous characterization of the ripple noise characteristic parameters of each grid point in the test area, providing high-precision and full-range measurement data support for the ripple noise performance evaluation of the object under test.

[0059] This embodiment establishes a unified global working coordinate system, unifying the spatial position reference of the six-axis industrial robotic arm, the non-contact capacitance measurement probe, and the object under test. This effectively reduces the impact of positional deviations between different devices on the measurement results, ensuring accurate correspondence between ripple noise signals and spatial position data. The non-contact measurement method avoids contact damage to the surface of the object under test during measurement, adapting to various surface morphologies. Multi-channel synchronous acquisition enables simultaneous acquisition and matching of ripple noise signals and spatial position data, effectively reducing measurement errors caused by signal-position misalignment. A combination of hardware filtering and software denoising effectively improves the signal-to-noise ratio of the effective ripple noise signal, ensuring the validity of the measurement data. The construction of a three-dimensional space-noise intensity mapping model enables full-range ripple noise distribution characterization of the measured area, providing complete and continuous data support for evaluating the ripple noise performance of the object under test, improving the integrity of the measurement process and the usability of the measurement results.

[0060] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A non-contact ripple noise measurement method based on an industrial robotic arm, characterized in that, Includes the following steps: S1. Establish a global working coordinate system, which is a three-dimensional rectangular coordinate system that serves as a unified spatial position reference in the field of industrial measurement. It includes the spatial coordinate reference system corresponding to the measurement base station and the tracking target ball. Complete the zero-point calibration of the non-contact capacitance measurement probe, which is a general-purpose sensing device for non-contact electrical signal measurement, including a capacitance sensing unit and a signal output unit. Complete the repeatability accuracy verification of the six-axis industrial robotic arm and output the basic parameters of the calibrated measurement system. S2. Combining the global working coordinate system and the calibrated basic parameters of the measurement system, collect the surface topology data of the object under test, identify the surface features and measurement boundaries of the object under test, generate a three-dimensional scanning path, adjust the motion posture and measurement distance of the non-contact capacitive measurement probe, and determine the measurement execution parameters. S3. According to the three-dimensional scanning path and measurement execution parameters, execute the real-time motion control of the six-axis industrial robot arm. Through hardware triggering, synchronously collect the original ripple noise signal of the measured object and the real-time spatial position data of the six-axis industrial robot arm. Perform hardware low-pass filtering on the original ripple noise signal and output the filtered ripple noise signal. S4. Perform noise separation and spectrum analysis on the filtered ripple noise signal, extract ripple noise characteristic parameters, match the ripple noise characteristic parameters with the real-time spatial location data one by one, construct a three-dimensional space-noise intensity mapping model, complete the ripple noise distribution measurement of the test area of ​​the object under test based on the three-dimensional space-noise intensity mapping model, and output the full area measurement results of ripple noise.

2. The method according to claim 1, characterized in that, Step S1 includes the following sub-steps: S1.

1. Deploy measurement base stations, set up tracking target balls at the end of the six-axis industrial robotic arm, collect spatial position data of the tracking target balls, and establish a global working coordinate system; S1.

2. Connect to a standard signal source, set the bandwidth limit for the non-contact capacitance measurement probe, complete the zero-point calibration of the non-contact capacitance measurement probe, and output the calibration parameters of the non-contact capacitance measurement probe. S1.

3. Drive the six-axis industrial robot arm to complete the repetitive reciprocating motion of the preset point, collect the actual motion position data of the six-axis industrial robot arm, complete the verification of the repeatability of the six-axis industrial robot arm, and output the motion control reference parameters of the six-axis industrial robot arm. S1.

4. Integrate the calibration parameters of the non-contact capacitance measurement probe with the motion control reference parameters of the six-axis industrial robotic arm to form the basic parameters of the calibrated measurement system.

3. The method according to claim 1, characterized in that, Step S2 includes the following sub-steps: S2.

1. Combining the global working coordinate system and the calibrated basic parameters of the measurement system, collect the surface point cloud data of the object under test, generate the surface topology data of the object under test, and identify the surface features, measurement boundaries and the area to be measured of the object under test. S2.

2. Combining the global working coordinate system, the calibration parameters of the non-contact capacitance measurement probe and the motion control reference parameters of the six-axis industrial robotic arm, a three-dimensional scanning path is generated based on the surface features of the object being measured and the area to be measured, and the motion posture of the non-contact capacitance measurement probe at each point on the three-dimensional scanning path is planned. S2.

3. By combining the surface topology data of the object under test with the three-dimensional scanning path, adjust the measurement distance between the non-contact capacitance measurement probe and the surface of the object under test, determine the scanning speed of the six-axis industrial robotic arm and the trigger parameters for data acquisition, and integrate them to form the measurement execution parameters.

4. The method according to claim 1, characterized in that, Step S3 includes the following sub-steps: S3.

1. Real-time motion control of the six-axis industrial robotic arm is achieved through the industrial Ethernet bus, which is a common real-time communication bus in the field of industrial control; the six-axis industrial robotic arm is driven to move the non-contact capacitance measurement probe along the area to be measured according to the scanning speed in the three-dimensional scanning path and measurement execution parameters. S3.

2. According to the data acquisition trigger parameters in the measurement execution parameters, the raw ripple noise signal output by the non-contact capacitance measurement probe and the real-time spatial position data of the six-axis industrial robot arm are synchronously acquired through hardware triggering. S3.

3. Perform hardware low-pass filtering on the original ripple noise signal. Hardware low-pass filtering is a common signal processing method in the field of signal conditioning. Filter out the high-frequency interference components in the original ripple noise signal and output the filtered ripple noise signal.

5. The method according to claim 1, characterized in that, Step S4 includes the following sub-steps: S4.

1. Perform wavelet denoising on the filtered ripple noise signal. Wavelet denoising is a common noise separation method in the field of signal processing. Complete the separation of the effective ripple noise signal from the background interference noise, and output the denoised effective ripple noise signal. S4.

2. Perform windowed fast Fourier transform processing on the effective signal of denoised ripple noise. Windowed fast Fourier transform processing is a common signal processing method in the field of spectrum analysis. Perform spectral analysis of the effective signal of ripple noise and output the spectral data of ripple noise; S4.

3. Extract ripple noise feature parameters from the spectral data based on ripple noise, match the ripple noise feature parameters with the real-time spatial location data at the corresponding acquisition time, and construct a three-dimensional space-noise intensity mapping model. The three-dimensional space-noise intensity mapping model is a mathematical model for the association between spatial data and signal features. S4.

4. Combining the three-dimensional space-noise intensity mapping model with the test area of ​​the object under test, the ripple noise distribution of the test area of ​​the object under test is measured, and the full-area ripple noise measurement results are output.

6. The method according to claim 3, characterized in that, Step S2 further includes constructing a 3D scanning path planning module, which includes a coordinate matching unit, a path generation unit, and an attitude planning unit. The input of the coordinate matching unit is connected to the global working coordinate system, the output of the coordinate matching unit is connected to the input of the path generation unit, and the output of the path generation unit is connected to the input of the attitude planning unit. The coordinate matching unit matches the surface features of the object under test with the area to be tested to the global working coordinate system, generating global coordinate distribution data of the area to be tested. The path generation unit generates a continuous 3D scanning path covering the entire area to be tested based on the global coordinate distribution data of the area to be tested and the calibration parameters of the non-contact capacitance measurement probe. The attitude planning unit plans the motion attitude of the non-contact capacitance measurement probe at each point on the three-dimensional scanning path based on the motion control reference parameters of the three-dimensional scanning path and the six-axis industrial robotic arm.

7. The method according to claim 4, characterized in that, Step S3 also includes constructing a multi-channel synchronous data acquisition module. The multi-channel synchronous data acquisition module is a general functional module in the field of data acquisition, which includes a hardware triggering unit, three measurement signal acquisition channels, one reference signal acquisition channel, and a position data acquisition channel. The output of the hardware trigger unit is connected to the input terminals of three measurement signal acquisition channels, one reference signal acquisition channel, and one position data acquisition channel, respectively. The hardware trigger unit receives the data acquisition trigger parameters from the measurement execution parameters and generates a synchronous trigger signal. The synchronous trigger signal is then synchronously distributed to the three measurement signal acquisition channels, one reference signal acquisition channel, and the position data acquisition channel. The raw ripple noise signal output by the non-contact capacitance measurement probe is acquired synchronously through three measurement signal acquisition channels, the reference signal of the measurement system is acquired synchronously through one reference signal acquisition channel, and the real-time spatial position data of the six-axis industrial robot is acquired synchronously through the position data acquisition channel.

8. The method according to claim 5, characterized in that, Step S4.1 further includes constructing a wavelet denoising module. This module is a general-purpose functional module in the field of signal processing, comprising a signal decomposition unit, a thresholding unit, and a signal reconstruction unit. The input of the signal decomposition unit is connected to the filtered ripple noise signal, and the output of the signal decomposition unit is connected to the input of the thresholding unit. The output of the thresholding unit is also connected to the input of the signal reconstruction unit. The signal decomposition unit uses the Daubechies wavelet basis to perform multi-level wavelet decomposition on the filtered ripple noise signal, generating multi-level wavelet decomposition coefficients. The thresholding unit uses a fixed threshold to perform thresholding on the high-frequency coefficients in the multi-level wavelet decomposition coefficients, filtering out interference noise components in the high-frequency coefficients. The signal reconstruction unit reconstructs the wavelet signal based on the processed high-frequency coefficients and the unprocessed low-frequency coefficients, outputting the denoised effective ripple noise signal.

9. The method according to claim 5, characterized in that, Step S4.3 further includes a module for constructing a three-dimensional space-noise intensity mapping model. This module comprises a data matching unit, a mesh generation unit, an interpolation fitting unit, and a model generation unit. The input of the data matching unit is connected to the ripple noise characteristic parameters and the real-time spatial location data, respectively. The output of the data matching unit is connected to the input of the mesh generation unit, the output of the mesh generation unit is connected to the input of the interpolation fitting unit, and the output of the interpolation fitting unit is connected to the input of the model generation unit. The data matching unit binds the ripple noise characteristic parameters to the real-time spatial location data at the corresponding acquisition time, generating a set of ripple noise data points with spatial coordinates. The mesh generation unit performs three-dimensional structured mesh generation on the test area based on the global working coordinate system, generating a three-dimensional mesh matrix for the test area; the interpolation and fitting unit matches the ripple noise data point set with spatial coordinates to the three-dimensional mesh matrix, and uses the Kriging interpolation method to complete the noise data fitting of the entire mesh; the model generation unit constructs a three-dimensional space-noise intensity mapping model based on the fitted three-dimensional mesh matrix.

10. A non-contact ripple noise measurement system based on an industrial robotic arm, used to perform the method as described in any one of claims 1-9, characterized in that, The system comprises a mechanical motion subsystem, a measurement and sensing unit, a data acquisition module, and a signal processing module. The mechanical motion subsystem includes a six-axis industrial robotic arm, a laser tracking device, and a motion control module. The motion control module enables real-time motion control of the six-axis industrial robotic arm via an industrial Ethernet bus. The measurement and sensing unit includes a non-contact capacitance measurement probe, a grounding ring structure, and a signal conditioning module. The grounding ring structure is an anti-interference structure for electromagnetic compatibility, containing a low-impedance conductive ring surrounding the sensing end of the non-contact capacitance measurement probe. The grounding ring structure is designed to work in conjunction with the non-contact capacitance measurement probe. The data acquisition module is electrically connected to the measurement and sensing unit and the mechanical motion subsystem, enabling synchronous acquisition of ripple noise signals and spatial position data. The signal processing module is electrically connected to the data acquisition module, completing ripple noise signal processing and constructing a three-dimensional space-noise intensity mapping model, outputting full-area ripple noise measurement results.

Images