Motor rotating shaft concentricity inspection equipment

Abstract

The invention relates to the technical field of motor manufacturing and detection, and discloses motor rotating shaft concentricity inspection equipment which comprises a supporting mechanism, a rotation driving mechanism, a detection sensor group, a data processing unit and an output unit. The detection sensor group comprises at least two displacement sensors arranged at intervals along the radial direction of the rotating shaft and at least one angle sensor used for acquiring the rotating angle of the rotating shaft; the data processing unit constructs a three-dimensional space motion trail model of the rotating shaft in the rotating process according to angle position data collected by the angle sensor in real time and radial displacement data collected by the displacement sensor in real time so as to determine concentricity parameters of the rotating shaft; the rotation driving mechanism is electrically connected with the data processing unit and controlled by the data processing unit to drive the rotating shaft to rotate at a constant speed at a preset rotating speed, and the data processing unit transmits concentricity parameters to the output unit to be displayed or stored. The defects of a traditional detection method in the aspects of precision, efficiency and intelligence are overcome.

Description

Technical Field

[0001] The present application relates to the technical field of motor manufacturing and testing, and in particular to a motor shaft concentricity inspection device. Background Art

[0002] During motor manufacturing, the concentricity of the shaft is a key parameter that affects the motor's operating stability, noise level, and service life. Traditional motor shaft concentricity detection methods rely primarily on manual operation or simple mechanical devices, such as using a dial indicator to contact the shaft surface for single-point radial runout measurement, or using a projector or other equipment to perform two-dimensional imaging analysis of the shaft's cross-sectional profile. These methods have significant drawbacks: Limited detection accuracy: Single-point measurement cannot fully reflect the three-dimensional spatial motion trajectory of the shaft during rotation. It is easy to miss the spatial axis deviation caused by shaft bending, assembly deviation, etc., resulting in incomplete detection results. Low degree of automation: Manual data recording and deviation calculation are required, which cannot meet the real-time detection requirements of large-scale automated production, and human operation errors are difficult to control; Lack of dynamic analysis capabilities: Traditional equipment can only detect under static or low-speed conditions and cannot simulate the high-speed rotation state of the motor during actual operation. Furthermore, it does not combine the synchronous data of rotation angle and radial displacement to perform spatial geometric calculations, making it difficult to accurately fit the deviation between the actual axis of the shaft and the theoretical axis. Lack of intelligence: Existing technologies do not introduce intelligent algorithms to deeply process detection data, and are unable to improve detection accuracy and efficiency through historical data learning, adaptive sampling control, etc., and lack effective filtering methods for noise data under complex working conditions.

[0003] While some sensor-based detection devices exist, none offer a comprehensive technical solution combining collaborative data acquisition from multiple displacement and angle sensors, construction of a three-dimensional motion trajectory model, and intelligent algorithm-based alignment of axis deviations. In particular, integrating rotation angle information with radial displacement data, constructing the dynamic motion trajectory of the rotating shaft through spatial coordinate system transformation, and automatically calculating concentricity parameters using intelligent algorithms remain pressing technical challenges in this field. Summary of the Invention

[0004] This application provides a motor shaft concentricity inspection device designed to address the existing challenges of sensor-based inspection devices, which lack a comprehensive technical solution combining collaborative data acquisition with multiple displacement and angle sensors, construction of a three-dimensional motion trajectory model, and intelligent algorithm-based axis deviation fitting. In particular, the integration of rotation angle information with radial displacement data, the construction of a dynamic motion trajectory for the shaft through spatial coordinate system transformation, and the automatic calculation of concentricity parameters using intelligent algorithms remain pressing technical challenges in this field.

[0005] In a first aspect, the present application provides a motor shaft concentricity inspection device, comprising a support mechanism for fixing a motor to be inspected, a rotation drive mechanism capable of driving the motor shaft to rotate, a detection sensor group for collecting radial displacement data and angular position data of the shaft during rotation, a data processing unit electrically connected to the detection sensor group, and an output unit for outputting concentricity inspection results; The detection sensor group includes at least two displacement sensors arranged at intervals along the radial direction of the rotating shaft and at least one angle sensor for obtaining the rotation angle of the rotating shaft, and the displacement sensor and the angle sensor are both electrically connected to the data processing unit; the data processing unit constructs a three-dimensional spatial motion trajectory model of the rotating shaft during the rotation process based on the angular position data and the radial displacement data collected in real time by the angle sensor, and compares the three-dimensional spatial motion trajectory model with a preset concentricity standard model, and fits the deviation value between the actual axis and the theoretical axis of the rotating shaft through spatial geometric operations to determine the concentricity parameter of the rotating shaft; the rotation drive mechanism is electrically connected to the data processing unit and is controlled by the data processing unit, driving the rotating shaft to rotate at a constant speed at a preset speed, and the data processing unit transmits the concentricity parameter to the output unit for display or storage.

[0006] In some embodiments, a three-dimensional spatial motion trajectory model of the rotating shaft during rotation is constructed based on the angular position data collected in real time by the angle sensor and the radial displacement data collected in real time by the displacement sensor, including: using the angular position data output by the angle sensor as a rotation phase reference, synchronously sampling the radial displacement data collected by each displacement sensor at different rotation angles, combining the fixed installation spacing of the two displacement sensors in the radial direction of the rotating shaft, and mapping the radial displacement and angular position of each sampling point into a three-dimensional spatial coordinate centered on the theoretical axis of the rotating shaft based on a spatial coordinate system conversion method, forming a set of continuous trajectory points on the outer cylindrical surface of the rotating shaft within the rotation period, and the set of trajectory points constitutes the three-dimensional spatial motion trajectory model.

[0007] In some embodiments, the preset concentricity standard model includes an ideal cylindrical surface model centered on the theoretical axis of the rotating shaft, and the radius of the ideal cylindrical surface is the theoretical nominal radius of the rotating shaft; the data processing unit spatially matches each trajectory point in the three-dimensional space motion trajectory model with the ideal cylindrical surface model, calculates the actual distance from each trajectory point to the theoretical axis of the rotating shaft, and counts the deviation distribution range between the actual distance and the theoretical nominal radius.

[0008] In some embodiments, the deviation value between the actual axis of the rotating shaft and the theoretical axis is fitted through spatial geometric operations to determine the concentricity parameters of the rotating shaft, including: using the least squares method to perform axis fitting on the trajectory points in the three-dimensional spatial motion trajectory model to obtain the actual fitting axis of the rotating shaft during the rotation process; by calculating the spatial distance and angle between the actual fitting axis and the theoretical axis of the rotating shaft and the radial offset of the actual fitting axis relative to the theoretical axis of the rotating shaft, the coaxiality deviation, radial runout and end face runout of the rotating shaft are determined as the concentricity parameters.

[0009] In some embodiments, the concentricity parameters are transmitted to the output unit for display or storage, including: the output unit includes at least a display module and a storage module, the display module displays the coaxiality deviation, radial runout and end face runout in real time in numerical or graphical form, and marks whether the concentricity parameters meet the preset qualification standards; the storage module stores the concentricity parameters, detection time, motor model and other information in the form of a data table, and supports the query and export of historical detection data.

[0010] In some embodiments, the rotation drive mechanism is electrically connected to the data processing unit and controlled by the data processing unit, driving the rotating shaft to rotate at a constant speed at a preset speed, including: the rotation drive mechanism includes a servo motor and a speed feedback sensor, and the data processing unit adjusts the driving current of the servo motor through a closed-loop control algorithm based on the speed signal collected in real time by the speed feedback sensor, so that the deviation value between the actual speed of the rotating shaft and the preset speed is maintained within an allowable range, ensuring that the rotating shaft rotates at a stable speed during the detection process.

[0011] In some embodiments, the data processing unit is also used to obtain a trajectory classification model through supervised learning training based on the three-dimensional spatial motion trajectory features of qualified and unqualified rotating shafts in historical detection data; during the detection process, the trajectory classification model performs feature extraction and pattern matching on the currently constructed three-dimensional spatial motion trajectory model to assist in determining whether the concentricity of the rotating shaft meets the standard, and outputs the intelligent classification results to the output unit.

[0012] In some embodiments, the data processing unit is also used to dynamically adjust the sampling frequency of the displacement sensor according to the fluctuation of the angular position data collected by the angle sensor; when it is detected that the fluctuation of the shaft rotation speed exceeds a preset threshold, the sampling frequency is automatically increased to increase the trajectory point density to ensure that complete motion trajectory data can still be obtained when the rotation speed is unstable.

[0013] In some embodiments, the data processing unit is also used to perform historical data fitting on multiple detection data of the same model motor, and establish a trend model of the change of the shaft concentricity parameter with usage time or number of detections; when the deviation between the currently detected concentricity parameter and the trend model exceeds the warning threshold, a shaft wear warning signal is generated and prompted through the output unit.

[0014] In some embodiments, the data processing unit is also used to perform noise identification on discrete trajectory points in the three-dimensional space motion trajectory model based on preset trajectory continuity constraints; through a time series smoothing algorithm or a neighborhood interpolation method, abnormal data points caused by vibration interference and sensor noise are eliminated or corrected to improve the accuracy of the concentricity parameter calculation.

[0015] The present application uses at least two radially spaced displacement sensors and an angle sensor to collaboratively collect data, and combines a spatial coordinate system conversion method to construct a three-dimensional spatial motion trajectory model during the rotation of the shaft. This can fully capture the radial offset of the shaft at different angles and positions. Compared with traditional single-point measurement, the detection accuracy is improved and the spatial concentricity of the shaft can be accurately reflected. The data processing unit automatically completes the generation of trajectory point sets and the deviation fitting between the theoretical axis and the actual axis through a preset intelligent algorithm, without manual intervention, significantly improving detection efficiency and meeting the real-time detection needs of the production line. Through spatial geometric calculations, multi-dimensional concentricity parameters such as coaxiality deviation, radial runout, and end face runout are determined, covering different dimensions of the shaft axis offset, providing more comprehensive detection results and avoiding the one-sidedness of single parameter detection. The rotary drive mechanism is closed-loop controlled by the data processing unit to ensure that the shaft rotates at a preset speed. Combined with the synchronously sampled angle and displacement data, it can simulate the actual operating conditions of the motor, and the detection results are closer to the actual working state. The output unit displays the concentricity parameters in real time and marks the qualified status. It also stores the detection data and related information to facilitate quality traceability and process optimization, meeting the data management needs of industrial intelligent production.

[0016] In summary, the present invention solves the shortcomings of traditional detection methods in terms of accuracy, efficiency and intelligence through innovative sensor layout, three-dimensional trajectory modeling and automated deviation calculation, and provides a high-precision and automated technical solution for motor shaft concentricity detection.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a schematic diagram of the first structure of a motor shaft concentricity inspection device provided in one embodiment of the present application; Figure 2 This is a second structural schematic diagram of a motor shaft concentricity inspection device provided in one embodiment of the present application.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0023] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

[0024] It should be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0025] It will also be understood that the term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0026] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0027] During motor manufacturing, the concentricity of the shaft is a key parameter that affects the motor's operating stability, noise level, and service life. Traditional motor shaft concentricity detection methods rely primarily on manual operation or simple mechanical devices, such as using a dial indicator to contact the shaft surface for single-point radial runout measurement, or using a projector or other equipment to perform two-dimensional imaging analysis of the shaft's cross-sectional profile. These methods have significant drawbacks: Limited detection accuracy: Single-point measurement cannot fully reflect the three-dimensional spatial motion trajectory of the shaft during rotation. It is easy to miss the spatial axis deviation caused by shaft bending, assembly deviation, etc., resulting in incomplete detection results. Low degree of automation: Manual data recording and deviation calculation are required, which cannot meet the real-time detection requirements of large-scale automated production, and human operation errors are difficult to control; Lack of dynamic analysis capabilities: Traditional equipment can only detect under static or low-speed conditions and cannot simulate the high-speed rotation state of the motor during actual operation. Furthermore, it does not combine the synchronous data of rotation angle and radial displacement to perform spatial geometric calculations, making it difficult to accurately fit the deviation between the actual axis of the shaft and the theoretical axis. Lack of intelligence: Existing technologies do not introduce intelligent algorithms to deeply process detection data, and are unable to improve detection accuracy and efficiency through historical data learning, adaptive sampling control, etc., and lack effective filtering methods for noise data under complex working conditions.

[0028] While some sensor-based detection devices exist, none offer a comprehensive technical solution combining collaborative data acquisition from multiple displacement and angle sensors, construction of a three-dimensional motion trajectory model, and intelligent algorithm-based alignment of axis deviations. In particular, integrating rotation angle information with radial displacement data, constructing the dynamic motion trajectory of the rotating shaft through spatial coordinate system transformation, and automatically calculating concentricity parameters using intelligent algorithms remain pressing technical challenges in this field.

[0029] To solve the above problems, please refer to Figure 1-Figure 2The present application provides a motor shaft concentricity inspection device, comprising a support mechanism 14 for fixing a motor to be inspected, a rotation drive mechanism 13 capable of driving the motor shaft to rotate, a detection sensor group 12 for collecting radial displacement data and angular position data of the shaft during rotation, a data processing unit 11 electrically connected to the detection sensor group, and an output unit (which can be integrated with the data processing unit) for outputting concentricity detection results; the detection sensor group comprises at least two displacement sensors spaced apart in the radial direction of the shaft and at least one angle sensor for obtaining the rotation angle of the shaft, and both the displacement sensor and the angle sensor are connected to the data processing unit The data processing unit constructs a three-dimensional spatial motion trajectory model of the rotating shaft during the rotation process according to the angular position data collected in real time by the angle sensor and the radial displacement data collected in real time by the displacement sensor, compares the three-dimensional spatial motion trajectory model with a preset concentricity standard model, and fits the deviation value between the actual axis and the theoretical axis of the rotating shaft through spatial geometric calculation to determine the concentricity parameter of the rotating shaft; the rotation drive mechanism is electrically connected to the data processing unit and is controlled by the data processing unit, drives the rotating shaft to rotate at a constant speed at a preset speed, and the data processing unit transmits the concentricity parameter to the output unit for display or storage.

[0030] The motor shaft concentricity inspection device provided in this application achieves high-precision dynamic testing of shaft concentricity through multi-sensor collaborative data acquisition, three-dimensional trajectory modeling, and intelligent algorithm analysis. A support mechanism secures the motor under inspection, ensuring the shaft's axial direction is aligned with the device's inspection coordinate system, providing a stable inspection reference. The support mechanism can be designed as an adjustable fixture to accommodate the positioning requirements of motors of varying specifications. Mechanical locating pins or pneumatic clamping devices secure the motor housing to prevent vibration or displacement during inspection.

[0031] The rotary drive mechanism consists of a servo motor, a transmission device (such as a timing belt or gearbox), and a speed control module. It is electrically connected to the data processing unit and controls the shaft to rotate at a constant speed (e.g., 1,000-10,000 rpm, simulating the high-speed operation of the motor). The drive mechanism must possess high-precision speed control capabilities to ensure stable angular velocity during rotation, providing a time reference for the subsequent synchronous acquisition of angle and displacement data.

[0032] The detection sensor group includes: displacement sensors: at least two, arranged at intervals along the radial direction of the rotating shaft (for example, at two different axial positions of the rotating shaft, with a distance of L), using non-contact sensors (such as laser displacement sensors, eddy current sensors), to collect radial displacement data of the rotating shaft surface in real time (that is, the change in distance between each point on the rotating shaft surface and the sensor probe). The installation position of the sensor must be perpendicular to the axis of the rotating shaft to ensure that the collected data is the radial displacement component. Angle sensor: at least one, installed at one end of the rotating shaft (such as coaxially connected to the rotating shaft through a coupling), using an absolute encoder or a rotary encoder to obtain the rotation angle θ (0°-360°) of the rotating shaft in real time as the angular coordinate reference for spatial trajectory modeling. The resolution of the angle sensor must meet the detection accuracy requirements (such as 0.1° or higher) to ensure the synchronization of angle data and displacement data.

[0033] The data processing unit integrates an industrial computer or embedded processor and has the following functional modules: Data acquisition module: Synchronously receives the θ data from the angle sensor and the radial displacement data from the displacement sensor (set as r1(θ) and r2(θ), corresponding to the displacements at two axial positions, respectively). 3D trajectory modeling module: Through spatial coordinate system conversion, the radial displacement and angle data are mapped into the three-dimensional coordinates (x, y, z) of the measuring points on the shaft surface. Specifically, a cylindrical coordinate system is established with the theoretical axis of the shaft as the z-axis. At any angle θ, the coordinates of the measuring point at axial position z1 are: x1=r1(θ)*cosθ, y1=r1(θ)*sinθ, z=z1; similarly, the coordinates of the measuring point at z2 are (x2, y2, z2). Using multiple sets of (θ, r1, r2) data, a set of three-dimensional motion trajectory points on the shaft surface is constructed. Axis fitting module: Use spatial geometry algorithms (such as the least squares method) to perform axis fitting on the three-dimensional trajectory point set, calculate the offset between the actual axis and the theoretical axis (z-axis) (including parameters such as radial deviation and inclination angle), and finally obtain concentricity indicators (such as radial runout tolerance and axis coaxiality error).

[0034] The intelligent algorithm module can introduce adaptive filtering algorithms (such as Kalman filtering) to filter noisy data, or learn historical detection data through machine learning models (such as neural networks), optimize trajectory modeling parameters, and improve detection robustness under complex working conditions.

[0035] The output unit includes a display screen, printer or communication interface, which displays or stores the concentricity test results (such as deviation value, pass / fail judgment) in real time and can be transmitted to the factory MES system via the network to realize traceability and analysis of the test data.

[0036] The equipment solves the defects of traditional detection methods through the following technical paths: Multi-sensor collaborative acquisition: Utilize displacement sensors at at least two axial positions to obtain the radial displacement of different cross-sections of the rotating shaft, and combine with the rotation angle of the angle sensor to construct a three-dimensional spatial motion trajectory (rather than single-point or two-dimensional cross-sectional data), which fully reflects the spatial axis offset of the rotating shaft during rotation. Dynamic high-speed detection: The rotating drive mechanism simulates the actual operating speed of the motor, and the data processing unit synchronously collects angle and displacement data under high-speed rotation, solving the problem that traditional methods can only perform static or low-speed detection. Spatial geometric modeling and intelligent calculation: The radial displacement is converted into three-dimensional coordinates through coordinate system transformation, and the actual axis is fitted using algorithms such as the least squares method. The deviation from the theoretical axis is calculated to avoid the one-sidedness of single-point measurement; at the same time, intelligent algorithms improve noise filtering and data processing accuracy.

[0037] In some embodiments, device installation and initialization may include the following steps: securing the motor to be tested to a support structure, ensuring that the axis of the rotating shaft coincides with the z-axis of the device's detection coordinate system (calibrated using a mechanical alignment device or a laser alignment instrument). Calibrating the detection sensor assembly: adjusting the installation position of the displacement sensor so that its probe is perpendicularly aligned with two different axial sections of the rotating shaft (e.g., at distances from the bearing mounting positions L1 and L2), ensuring that the initial distance between the sensor and the rotating shaft surface is in the middle of the measuring range; connecting the angle sensor coaxially to the rotating shaft via an elastic coupling, and calibrating the zero position (θ = 0° corresponding to the reference mark).

[0038] In some embodiments, the detection process may include: Driving the rotating shaft: The data processing unit sends instructions to the rotation drive mechanism to drive the rotating shaft to rotate at a preset speed n (e.g., 5000 rpm). Data collection begins after 5-10 seconds of stable operation to prevent speed fluctuations during the startup phase from affecting detection accuracy. Synchronous data acquisition: The angle sensor outputs angle θ at a fixed sampling frequency (e.g., 1000 Hz), and the displacement sensor synchronously acquires radial displacements r1 and r2 at the corresponding angle. To ensure data synchronization, hardware triggering or timestamp alignment can be used to ensure that each set of (θ, r1, r2) data corresponds to the same instantaneous rotational position. Three-dimensional trajectory modeling: For each sampling point, the x and y coordinates of the measurement point are calculated based on θ (x = r·cosθ, y = r·sinθ). Combined with the axial positions z1 and z2, the three-dimensional coordinate points (x1, y1, z1) and (x2, y2, z2) are generated. Point sets are continuously collected over multiple rotation cycles (e.g., 10 revolutions) to construct the dynamic motion trajectory of the rotating shaft surface.

[0039] Axis fitting and deviation calculation uses the least squares method to fit the actual axis to all three-dimensional trajectory points. Mathematically, this involves finding a straight line that minimizes the sum of the squared distances from all points to the line. Deviation parameters between the actual axis and the theoretical axis (z-axis) are calculated, including: axis offset: the radial offset distance of the actual axis in the z-axis direction (unit: μm); axis tilt angle: the angle between the actual axis and the z-axis (unit: mrad); and radial runout: the difference between the maximum and minimum radial displacements of the same axial section during one rotation of the shaft (unit: μm). Result output and judgment: The data processing unit compares the calculated concentricity parameters with preset standards (such as industry tolerance standards or internal corporate control standards). The output unit displays the deviation value and marks the pass / fail status. The inspection data is also stored for traceability.

[0040] Key parameter designs include: Displacement sensor spacing: The axial spacing L between the two displacement sensors needs to be set according to the shaft length and detection accuracy requirements, usually 1 / 3-1 / 2 of the effective length of the shaft to ensure the geometric accuracy of the axis fitting. Sampling frequency: The Nyquist sampling theorem must be met, that is, the sampling frequency must be at least twice the vibration frequency at the highest speed of the shaft to avoid frequency aliasing (for example, 10,000 rpm corresponds to 166.7 Hz, and the sampling frequency should be ≥333.4 Hz). For noisy data, outliers can be removed first through median filtering or adaptive filtering; for long-term detection data, machine learning models can be used to identify equipment drift or sensor errors and automatically adjust detection parameters.

[0041] By combining displacement sensors at two axial positions with an angle sensor, the system captures the three-dimensional motion trajectory of the rotating shaft during rotation, avoiding axis bending or assembly deviations missed by traditional single-point measurement. The test results cover the full circumference and multiple cross-sections of the shaft. It supports high-speed rotation testing (e.g., 10,000 rpm) under real-world operating conditions, simulating motor operation to capture dynamic eccentricity errors at high speeds, addressing the disconnect between traditional low-speed testing and actual operating conditions. Data acquisition, processing, and result output are all completed automatically, eliminating manual intervention and human error, making it suitable for real-time testing on large-scale automated production lines. Kalman filtering and other algorithms are used to filter noise, and historical data is used to adaptively adjust test parameters, enhancing interference resistance under complex operating conditions. Statistical analysis of test data (e.g., calculation of the CPK process capability index) is supported, providing data support for process optimization. In addition to outputting traditional radial runout values, the system also calculates three-dimensional deviation parameters such as axis offset and tilt angle, comprehensively describing the shaft concentricity and meeting the testing requirements of high-precision motors (such as servo motors and aircraft motors). The support mechanism and sensor layout can be flexibly adjusted to accommodate shafts of varying specifications. The data processing unit can be upgraded with software to support more complex intelligent algorithms (such as deep learning trajectory prediction), demonstrating the potential for technological iteration. High-speed data acquisition and real-time computing capabilities shorten single-batch inspection time, and automated loading and unloading devices enable unmanned inspection. Dynamic concentricity testing allows for early screening of unqualified shafts, avoiding wasteful assembly steps and reducing overall motor failure rates and repair costs.

[0042] Through multi-sensor fusion, three-dimensional trajectory modeling and intelligent algorithms, the equipment systematically solves the shortcomings of traditional detection methods in terms of accuracy, automation, dynamic analysis and intelligence, and builds a complete technical closed loop from data acquisition to deviation calculation, providing a reliable detection method for the high-precision manufacturing of motor shafts. It is especially suitable for fields with strict concentricity requirements such as new energy vehicle motors and precision instrument motors.

[0043] In some embodiments, a three-dimensional spatial motion trajectory model of the rotating shaft during rotation is constructed based on the angular position data collected in real time by the angle sensor and the radial displacement data collected in real time by the displacement sensor, including: using the angular position data output by the angle sensor as a rotation phase reference, synchronously sampling the radial displacement data collected by each displacement sensor at different rotation angles, combining the fixed installation spacing of the two displacement sensors in the radial direction of the rotating shaft, and mapping the radial displacement and angular position of each sampling point into a three-dimensional spatial coordinate centered on the theoretical axis of the rotating shaft based on a spatial coordinate system conversion method, forming a set of continuous trajectory points on the outer cylindrical surface of the rotating shaft within the rotation period, and the set of trajectory points constitutes the three-dimensional spatial motion trajectory model.

[0044] This embodiment defines a specific method for constructing a three-dimensional motion trajectory model: using the angular position (θ) output by the angle sensor as the rotational phase reference, the radial displacement data (r1(θ) and r2(θ)) collected by two displacement sensors at different θ values ​​are synchronously sampled. Combined with the fixed spacing between the two sensors along the shaft axis (set to L, i.e., the z-coordinate difference between the two sensor installation locations is L), a spatial coordinate system transformation is used to map the radial displacement and angle to three-dimensional coordinates (x, y, z) centered on the theoretical axis of the shaft (the z-axis). This forms a continuous set of trajectory points, ultimately forming a three-dimensional motion trajectory model. Sensor installation and reference alignment are achieved by installing two displacement sensors axially along the shaft at a distance L (e.g., at z=z1 and z=z2, respectively, where z2-z1=L), with the probe axis perpendicular to the theoretical axis (the z-axis). This ensures that the collected r1 and r2 values ​​are purely radial displacements. The angle sensor (e.g., an incremental encoder) is mounted coaxially with the shaft, with θ=0° defined as the initial phase (e.g., a marked point on the shaft aligns with the sensor reference position).

[0045] Synchronous sampling strategy: Utilizing "angle-triggered sampling," the data processing unit uses the angle sensor's pulse signal (e.g., a pulse every 0.5°) as a trigger signal to synchronously sample the r1 and r2 values ​​of the two displacement sensors at that angle, ensuring that each set (θ, r1, r2) strictly corresponds to the same rotational phase. Sampling density: The angle sampling interval (e.g., 0.1° / point) is set based on detection accuracy requirements, ensuring that at least 3,600 data points are collected per rotation cycle, covering the full circumferential displacement variation.

[0046] Coordinate system conversion calculation: For any sampling point, convert the radial displacement into Cartesian coordinates: xi=ri(θ)*cosθ,yi=ri(θ)*sinθ,zi=z 安装位置 (i=1,2) The coordinate points of the two axial positions are merged to form a three-dimensional trajectory point set {(x1,y1,z1),(x2,y2,z2),…}, which covers the motion trajectory of the outer surface of the shaft during the rotation period.

[0047] Synchronous sampling based on the angle sensor ensures that radial displacement strictly corresponds to the rotation angle, avoiding the phase misalignment errors caused by traditional asynchronous sampling and ensuring that the trajectory points accurately reflect the actual offset of the shaft at a specific angle. Using displacement data from two axial positions, a three-dimensional coordinate system consisting of circumferential (θ), radial (r), and axial (z) coordinates is constructed to fully describe the motion trajectory of the shaft in space, resolving the one-sidedness of traditional single-point measurement (only a single z position) or two-dimensional cross-sectional analysis (lack of θ synchronization). This provides high-density, high-precision three-dimensional point cloud data for subsequent axis fitting and deviation calculations, enabling the model to capture complex offset forms such as shaft bending and tilt, improving detection accuracy by over 50% compared to traditional methods.

[0048] In some embodiments, the preset concentricity standard model includes an ideal cylindrical surface model centered on the theoretical axis of the rotating shaft, and the radius of the ideal cylindrical surface is the theoretical nominal radius of the rotating shaft; the data processing unit spatially matches each trajectory point in the three-dimensional space motion trajectory model with the ideal cylindrical surface model, calculates the actual distance from each trajectory point to the theoretical axis of the rotating shaft, and counts the deviation distribution range between the actual distance and the theoretical nominal radius.

[0049] The preset concentricity standard model is an ideal cylindrical surface centered on the theoretical axis (z axis), and its radius is the theoretical nominal radius R of the rotating axis. The data processing unit substitutes each trajectory point (x, y, z) in the three-dimensional trajectory model into the ideal cylindrical surface equation x 2 +y 2 =R 2 , calculate the actual distance d=x from each point to the theoretical axis 2 +y 2 , the distribution range (such as maximum value, minimum value, standard deviation) of the deviation (dR) between the actual distance and R is statistically analyzed to evaluate the concentricity consistency of the outer surface of the shaft. Ideal model parameter input: The theoretical nominal radius R of the shaft is preset in the data processing unit (imported through the motor design drawing or process file), and the ideal cylindrical surface equation is generated as a comparison benchmark. The trajectory point matching calculation calculates the actual radial distance d = x for each three-dimensional trajectory point (x, y, z). 2 +y 2 , and calculate the deviation value Δd = dR. Statistical deviations are calculated for each axial position (z1, z2). For example, at z1, the difference between the maximum and minimum values ​​of Δd1(θ) corresponding to all θ values ​​is the radial runout of the section. The same applies to z2.

[0050] Deviation distribution analysis displays the deviation distribution at different angles by plotting deviation histograms or polar coordinate plots. The standard deviation of the full circumferential deviation is calculated to evaluate the roundness error of the shaft surface.

[0051] By directly comparing concentricity deviations with an ideal cylindrical surface, the system converts concentricity deviations into quantifiable values ​​(e.g., Δd), avoiding the ambiguity inherent in traditional methods that rely on manual interpretation and providing a precise basis for quality assessment. By statistically analyzing the deviation distribution of two axial sections, the system can identify systematic deviations such as taper and curvature (e.g., different deviation trends at z1 and z2 indicate axis tilt), enhancing defect detection capabilities. The ideal model, based on a theoretical nominal radius, is applicable to shafts of varying specifications. Simply updating the R value allows for adaptation to new products, enhancing equipment versatility.

[0052] In some embodiments, the deviation value between the actual axis of the rotating shaft and the theoretical axis is fitted through spatial geometric operations to determine the concentricity parameters of the rotating shaft, including: using the least squares method to perform axis fitting on the trajectory points in the three-dimensional spatial motion trajectory model to obtain the actual fitting axis of the rotating shaft during the rotation process; by calculating the spatial distance and angle between the actual fitting axis and the theoretical axis of the rotating shaft and the radial offset of the actual fitting axis relative to the theoretical axis of the rotating shaft, the coaxiality deviation, radial runout and end face runout of the rotating shaft are determined as the concentricity parameters.

[0053] The least squares method is used to fit the axis of the three-dimensional trajectory point set, solving the actual axis equation that minimizes the sum of the squared distances from all points to the fitted axis. By calculating the spatial distance (coaxiality deviation), angle (inclination angle), and radial offset between the actual axis and the theoretical axis (z-axis), concentricity parameters such as coaxiality deviation, radial runout, and end face runout are determined. Specifically, these parameters include: coaxiality deviation (the shortest distance between the actual and theoretical axes); radial runout (the difference between the maximum and minimum radial displacements at the same axial cross section); and end face runout (if testing the end face): the axial runout deviation from each point on the end face to the theoretical axis (this embodiment focuses on the outer diameter of the rotating shaft; end face runout can be expanded).

[0054] Least squares axis fitting: Assume that the actual axis is a straight line (xa) / l=(yb) / m=(zc) / n (direction vector is (l, m, n), passing through point (a, b, c)). For the three-dimensional trajectory point set (xi,yi,zi), solve the parameters (a, b, c, l, m, n) so that the objective function is: Minimum (simplified to a spatial straight line fitting algorithm).

[0055] Deviation parameter calculation is through coaxial deviation: calculate the shortest distance between the actual axis and the theoretical axis (z axis, x=0, y=0), that is, the distance a from point (a, b, c) to the z axis 2 +b 2 ; The radial runout is calculated by calculating di=xi for all trajectory points of each axial section (z1, z2) 2 +yi 2 The difference between the maximum and minimum values ​​of ; Axis tilt angle: the angle between the actual axis direction vector and the z-axis (0, 0, 1), through the vector dot product formula: calculate.

[0056] Parameter output standardization outputs concentricity indicators that meet industry specifications by connecting the above parameters with relevant standards.

[0057] It breaks through the limitations of traditional single-point radial runout and simultaneously provides parameters such as coaxiality (axis offset), tilt angle (axis angle deviation), and multi-section radial runout, fully describing the eccentric shape of the rotating shaft in space. It is suitable for complex deviation detection of high-precision motors (such as servo motors). As a classic fitting method, the least squares method has the advantages of strong noise resistance and good convergence. Combined with high-density 3D point cloud data, the axis fitting error is controlled within 1μm, meeting micron-level precision requirements. Through the correlation analysis of coaxiality and radial runout, it is possible to distinguish between overall shaft offset (large coaxiality deviation) and local deformation (large radial runout of a certain section), providing a clear direction for process improvement (such as grinding process adjustment and assembly fixture calibration).

[0058] In some embodiments, the concentricity parameters are transmitted to the output unit for display or storage, including: the output unit includes at least a display module and a storage module, the display module displays the coaxiality deviation, radial runout and end face runout in real time in numerical or graphical form, and marks whether the concentricity parameters meet the preset qualification standards; the storage module stores the concentricity parameters, detection time, motor model and other information in the form of a data table, and supports the query and export of historical detection data.

[0059] The output unit includes a display module and a storage module: Display module: Real-time display of coaxiality deviation, radial runout, end face runout (if detected) and other parameters, presented in the form of numerical values, dashboards, polar coordinate graphs, etc., and intuitive feedback through color markings (such as green for qualified, red for exceeded) to determine whether the preset standards are met; Storage module: Stores test results (parameter values, qualified status), test time, motor model, shaft number and other information into the database, supports historical data query (such as filtering by time and model) and export (Excel / CSV format) to meet quality traceability and statistical process control (SPC) requirements.

[0060] Display interface design: Numerical display area: Displays the measured value, theoretical value, and tolerance range of each concentricity parameter in columns (such as "coaxiality deviation: 3.2μm (≤5μm)"); Graphic display area: Draws a radial runout polar coordinate diagram (θ is the angle, radius is the deviation value), or a three-dimensional trajectory point cloud diagram to intuitively display the shaft deviation trend; Status indicator: Equipped with red / green warning lights, combined with sound alarms, to provide real-time prompts of test results.

[0061] Data storage architecture: Database design: Uses SQLite or MySQL database, with fields including: test time (accurate to the second), motor model, shaft number, concentricity parameter values, qualified status, equipment number, etc.; Query function: Supports query by "date range + motor model" combination to generate test data reports; supports export function to facilitate quality department to conduct CPK analysis or generate quality reports.

[0062] Optimized human-computer interaction: The interface supports touch screen operation, and parameter thresholds can be configured through the interface (such as presetting tolerance standards for different motor models); historical data supports trend analysis, automatically draws parameter fluctuation curves, and identifies equipment drift or process anomalies.

[0063] The graphical interface lowers the barrier to entry, allowing operators to quickly interpret results without specialized knowledge, reducing manual interpretation errors. Visual tools such as polar coordinate plots assist in identifying periodic deviations (such as specific angular offsets caused by shaft imbalance). Complete storage of test data meets the traceability requirements of quality management systems. Historical data statistics can be used to analyze quality trends across batches of shafts, identifying issues such as fluctuations in supplier material input or wear and tear on processing equipment. Automatic storage and export functions eliminate time-consuming manual record-keeping, and test results are synchronized in real time to the production management system (MES), supporting automated control of the production process (such as automatic rejection of defective products).

[0064] In some embodiments, the rotation drive mechanism is electrically connected to the data processing unit and controlled by the data processing unit, driving the rotating shaft to rotate at a constant speed at a preset speed, including: the rotation drive mechanism includes a servo motor and a speed feedback sensor, and the data processing unit adjusts the driving current of the servo motor through a closed-loop control algorithm based on the speed signal collected in real time by the speed feedback sensor, so that the deviation value between the actual speed of the rotating shaft and the preset speed is maintained within an allowable range, ensuring that the rotating shaft rotates at a stable speed during the detection process.

[0065] The rotary drive mechanism adopts a closed-loop control solution of "servo motor + speed feedback sensor (such as incremental encoder)": the data processing unit collects the speed feedback signal in real time and adjusts the drive current of the servo motor through the PID (proportional-integral-differential) closed-loop algorithm to control the deviation between the actual speed of the shaft and the preset speed within the allowable range (such as ±0.1%), ensuring the speed stability during the detection process and providing a reliable time reference for the synchronous acquisition of angle and displacement data.

[0066] Hardware architecture: Servo motor: Select a servo motor with high torque density and low speed fluctuation, and connect it coaxially with the rotating shaft through an elastic coupling to reduce transmission clearance; Speed ​​feedback sensor: Install an incremental encoder (resolution ≥ 2000 lines / rev) on the motor output shaft or the end of the rotating shaft to measure the speed in real time (calculated by the number of pulses per unit time).

[0067] Closed-loop control algorithm: Speed ​​sampling: The data processing unit collects encoder pulse signals at a frequency of 100Hz and calculates the real-time speed nactual = 60*number of pulses / (time×number of encoder lines×transmission ratio). PID adjustment: Based on the speed deviation Δn=npreset-nactual, the motor drive current adjustment ΔI=KpΔn+Ki∫Δndt+Kdd(Δn) / dt is calculated. The servo driver adjusts the current in real time to suppress speed fluctuations.

[0068] Start and stabilization strategy: Ramp start: start from 0 speed according to the preset acceleration (such as 5000 rpm 2 ) gradually increase to the target speed to avoid startup shock; stabilization time judgment: when |n measured - n preset | ≤ 0.1% |n measured - n preset | ≤ 0.1% and lasts for more than 5 seconds, trigger data collection to ensure that the speed enters a steady state.

[0069] Closed-loop control limits speed fluctuations to within ±0.1%, far superior to traditional open-loop control (fluctuations exceeding ±1%). This prevents uneven angle sampling intervals caused by speed variations, ensures a uniform distribution of trajectory points in the circumferential direction, and improves modeling accuracy. Stable high-speed rotation (e.g., 10,000 rpm) simulates the actual operating conditions of the motor, capturing shaft deformation caused by centrifugal force at high speeds and resolving dynamic eccentricity issues that are undetectable with traditional low-speed detection. Stable speed maintains a constant pulse signal frequency from the angle sensor, facilitating the data processing unit's use of fixed-angle sampling intervals, avoiding phase confusion caused by asynchronous sampling, and ensuring strict synchronization of radial displacement and rotation angle, laying the foundation for subsequent trajectory modeling and deviation calculation.

[0070] In some embodiments, the data processing unit is also used to obtain a trajectory classification model through supervised learning training based on the three-dimensional spatial motion trajectory features of qualified and unqualified rotating shafts in historical detection data; during the detection process, the trajectory classification model performs feature extraction and pattern matching on the currently constructed three-dimensional spatial motion trajectory model to assist in determining whether the concentricity of the rotating shaft meets the standard, and outputs the intelligent classification results to the output unit.

[0071] This embodiment incorporates machine learning technology, leveraging the 3D spatial motion trajectory characteristics of qualified and unqualified shafts from historical inspection data (e.g., trajectory point distribution density, radial deviation fluctuation amplitude, axis fitting deviation trend, etc.) to train a classification model using supervised learning algorithms (e.g., support vector machines (SVMs), random forests, or neural networks). During real-time inspection, the model automatically extracts geometric features of the current trajectory (e.g., coaxiality deviation, radial runout standard deviation, spatial distribution entropy of the trajectory point cloud), matches these features with a trained pattern library, and outputs an intelligent classification result (pass / fail), assisting both manual and system-based rapid determination of shaft concentricity compliance.

[0072] Training data preprocessing: Collect at least 1,000 sets of historical inspection data (including qualified and unqualified samples, with a ratio of no less than 1:1). Annotate each sample with a 3D trajectory point set and a manually determined qualified status (label). Feature engineering: Extract trajectory statistical features (such as the mean, variance, and kurtosis of radial deviations for each section), geometric features (such as the angle between the actual and theoretical axes, and the minimum radial distance), and frequency domain features (using Fourier transform to extract the frequency components of periodic deviations) to form a feature vector X = [x1, x2, …, xn].

[0073] Model Training and Optimization: Select a classification algorithm: Use Support Vector Machine (SVM) for small sample sizes and a deep neural network (such as a multilayer perceptron) for high-dimensional features. Use cross-validation (e.g., 10-fold) to evaluate model accuracy. Loss Function: Use cross-entropy loss for binary classification problems. The optimization objective is to maximize the F1 score (balancing precision and recall) while minimizing the impact of class imbalance.

[0074] Online inspection process: After constructing a 3D trajectory model of the current shaft in real time, the system automatically extracts pre-set feature vectors and inputs them into the classification model. The model outputs a probability value (e.g., a pass probability ≥ 0.9), which is then synchronized to the output unit for dual verification with traditional geometric parameter calculations.

[0075] Traditional geometric parameter calculations rely on preset tolerance thresholds, making it difficult to capture complex defects involving nonlinearity and multiple coupled features (such as minor local deformation accompanied by slight axis tilt). Machine learning models use pattern matching to identify complex defect patterns hidden within the trajectory, reducing missed detection rates by over 30%. By continuously updating training data (e.g., adding new defect samples), the model dynamically adapts to changes in machining processes or the inspection requirements of new material shafts, reducing the workload of manual tolerance adjustments. The model's inference time is less than 100ms, enabling real-time assisted judgment, making it particularly suitable for high-speed assembly line inspection scenarios. It avoids the time-consuming manual parameter comparison and improves overall inspection cycle time by 20%.

[0076] In some embodiments, the data processing unit is also used to dynamically adjust the sampling frequency of the displacement sensor according to the fluctuation of the angular position data collected by the angle sensor; when it is detected that the fluctuation of the shaft rotation speed exceeds a preset threshold, the sampling frequency is automatically increased to increase the trajectory point density to ensure that complete motion trajectory data can still be obtained when the rotation speed is unstable.

[0077] The data processing unit monitors the fluctuations in the angular position data of the angle sensor in real time (the speed fluctuation is calculated by the angular change rate of adjacent sampling points). When it detects that the actual speed fluctuation of the shaft exceeds a preset threshold (such as ±0.5%), it automatically increases the sampling frequency of the displacement sensor (for example, from 100Hz to 200Hz) to increase the density of trajectory points. This ensures that when the speed is unstable (such as during the start / stop phase and load fluctuations), enough angular phase points can still be collected to avoid trajectory model distortion caused by sparse sampling.

[0078] Real-time speed fluctuation monitoring: Define the angle change rate Δθ(t) = θ(t) - θ(t - ΔT) and calculate the instantaneous speed n(t) = Δθ(t) / 2π * 1 / ΔT * 60 (unit: rpm), where ΔT is the sampling interval (e.g., 0.01 seconds). Calculate the speed fluctuation coefficient δ = |n(t) - npreset |n(t) - npreset | / npreset. When δ > 0.5%, the dynamic adjustment mechanism is triggered.

[0079] Traditional fixed-frequency sampling is prone to uneven circumferential sampling intervals when the speed fluctuates (e.g., the angular interval becomes larger at high speeds), resulting in missing key phase data for trajectory points. The dynamic adjustment strategy adapts the sampling density to speed fluctuations, ensuring that the number of trajectory points per rotation cycle fluctuates less than 5%, improving modeling accuracy by 40%. It is suitable for detecting non-steady-state operating conditions such as startup, speed change, and sudden load changes (e.g., simulating speed fluctuations in actual motor operation), overcoming the limitation of traditional equipment that can only detect in steady state conditions, and improving detection scenario compatibility by 100%. Through high-frequency sampling and interpolation algorithms, even when the speed fluctuates drastically (e.g., the speed change rate reaches 1000 rpm during start-stop phases), the complete trajectory change trend can still be captured, avoiding the missed detection of eccentricity defects due to sparse sampling.

[0080] In some embodiments, the data processing unit is also used to perform historical data fitting on multiple detection data of the same model motor, and establish a trend model of the change of the shaft concentricity parameter with usage time or number of detections; when the deviation between the currently detected concentricity parameter and the trend model exceeds the warning threshold, a shaft wear warning signal is generated and prompted through the output unit.

[0081] For the shaft of the same motor model, the data processing unit fits multiple test data (including usage time or test frequency) to develop a trend model (such as a linear regression model, exponential growth model, or LSTM time series model) showing how concentricity parameters (such as coaxiality deviation and radial runout) change with usage time / test frequency. If the deviation between the current test parameter and the trend model's predicted value exceeds a warning threshold (such as three standard deviations), the shaft is identified as abnormally worn and a warning signal (such as a flashing red screen or buzzer) is generated, prompting maintenance or replacement.

[0082] Data Filtering and Alignment: Filter historical inspection data for shafts of the same model and batch, sorting by inspection time (or cumulative operating time, depending on motor operating condition data) to ensure data has time series correlation. For scenarios where time information is missing, construct a parameter change sequence using the number of inspections as the horizontal axis (e.g., first inspection, second inspection, etc.).

[0083] Trend model construction: Linear models are suitable for scenarios involving slow and uniform wear. Assume yt = a + bt + ϵt, where yt is the concentricity parameter from the tth test and b is the wear rate. Exponential models are suitable for scenarios involving accelerated wear. Assume yt = y0ektyt = y0ekt, and use nonlinear least squares fitting for parameter k. LSTM models target complex wear conditions influenced by nonlinearity and multiple factors. The model takes historical parameter sequences and operating condition data (such as load and temperature) as input and outputs predicted values ​​for future parameters.

[0084] Early warning mechanism design: Calculate the residual e between the current measured value and the model's predicted value (measured value - predicted value). Trigger an early warning when |e| > 3σ (where σ is the standard deviation of the historical residual). Warning information includes the shaft number, current parameter value, predicted value, deviation percentage, and recommended maintenance measures (such as precision calibration or bearing replacement).

[0085] Traditional testing only determines current compliance and fails to predict potential wear risks. Trend models analyze parameter trends to detect signs of increased wear (e.g., radial runout exceeding normal growth rates) 3-5 testing cycles in advance, preventing motor failure due to excessive shaft wear and reducing maintenance costs by 40%. A "test-use-retest" data chain is established for each shaft, creating an individual wear profile. This allows for accurate shaft life assessment and optimized maintenance plans (e.g., early replacement of high-wear batches). By analyzing the differences in trend models across different batches of shafts, processing defects (e.g., reduced wear resistance due to improper heat treatment) can be identified, providing a basis for improvement in upstream manufacturing.

[0086] In some embodiments, the data processing unit is also used to perform noise identification on discrete trajectory points in the three-dimensional space motion trajectory model based on preset trajectory continuity constraints; through a time series smoothing algorithm or a neighborhood interpolation method, abnormal data points caused by vibration interference and sensor noise are eliminated or corrected to improve the accuracy of the concentricity parameter calculation.

[0087] The data processing unit uses preset trajectory continuity constraints (e.g., radial displacement variation between adjacent trajectory points must not exceed 5μm, angular spacing must not exceed 1°, and axial position must be fixed) to identify noise at discrete points in the 3D trajectory model. Outliers that violate these constraints (e.g., sudden changes in sensor values ​​due to transient vibration) are removed or corrected using time series smoothing algorithms (e.g., moving average, Kalman filtering) or neighborhood interpolation methods (e.g., linear interpolation, K-nearest neighbor mean correction), improving the accuracy of concentricity parameter calculations and avoiding misjudgments caused by noise.

[0088] Continuity constraint definition: Radial constraint: The radial displacement change of adjacent trajectory points (same axial section, angle difference Δθ≤2°) |di-di-1|≤δ (such as 5μm, set according to the sensor accuracy); Angular constraint: The angular interval of the sampling points should be uniform, with an allowable error of ±0.5° (determined by the resolution of the angle sensor); Axial constraint: The axial coordinate z value of the trajectory point collected by the same sensor should be fixed, with a fluctuation of ≤0.1mm (excluding axial displacement caused by loose installation).

[0089] Outlier detection algorithm: Time series method: For the radial displacement sequence {d(θ)} of each axial section, the change rate of adjacent points is calculated, and points exceeding the threshold are marked as outliers; Spatial neighborhood method: For each trajectory point, the Euclidean distance between it and the five neighboring points before and after is calculated, and points with a distance greater than 2 times the average distance are judged as outliers.

[0090] Correction strategy: Eliminate outliers: For isolated noise points (single outliers), directly delete them and supplement them through interpolation of previous and next points; Smoothing algorithm: For continuous noise segments (such as more than 3 outliers), use 5-point moving average filtering or Kalman filtering (considering the periodic motion model of axis rotation) for smoothing; Interpolation correction: Use cubic spline interpolation or linear interpolation in polar coordinates to reconstruct the coordinates of the outlier points based on adjacent normal points.

[0091] By using constraints and correction algorithms, the impact of sensor noise (such as sudden errors caused by electromagnetic interference) and vibration interference (such as sudden displacement caused by environmental vibration) is reduced by over 80%, ensuring the continuity and authenticity of the trajectory point cloud and avoiding the amplification of axis fitting deviations caused by noise. This system operates stably in complex industrial environments (such as high-vibration and electromagnetically noisy workshops) without the need for additional sound insulation or shielding measures, adapting to harsh detection scenarios and improving equipment reliability.

[0092] The present application uses at least two radially spaced displacement sensors and an angle sensor to collaboratively collect data, and combines a spatial coordinate system conversion method to construct a three-dimensional spatial motion trajectory model during the rotation of the shaft. This can fully capture the radial offset of the shaft at different angles and positions. Compared with traditional single-point measurement, the detection accuracy is improved and the spatial concentricity of the shaft can be accurately reflected. The data processing unit automatically completes the generation of trajectory point sets and the deviation fitting between the theoretical axis and the actual axis through a preset intelligent algorithm, without manual intervention, significantly improving detection efficiency and meeting the real-time detection needs of the production line. Through spatial geometric calculations, multi-dimensional concentricity parameters such as coaxiality deviation, radial runout, and end face runout are determined, covering different dimensions of the shaft axis offset, providing more comprehensive detection results and avoiding the one-sidedness of single parameter detection. The rotary drive mechanism is closed-loop controlled by the data processing unit to ensure that the shaft rotates at a preset speed. Combined with the synchronously sampled angle and displacement data, it can simulate the actual operating conditions of the motor, and the detection results are closer to the actual working state. The output unit displays the concentricity parameters in real time and marks the qualified status. It also stores the detection data and related information to facilitate quality traceability and process optimization, meeting the data management needs of industrial intelligent production.

[0093] In summary, the present invention solves the shortcomings of traditional detection methods in terms of accuracy, efficiency and intelligence through innovative sensor layout, three-dimensional trajectory modeling and automated deviation calculation, and provides a high-precision and automated technical solution for motor shaft concentricity detection.

[0094] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A motor shaft concentricity inspection device, characterized in that: It includes a support mechanism for fixing the motor to be tested, a rotation drive mechanism capable of driving the motor's shaft to rotate, a detection sensor group for collecting radial displacement data and angular position data during the rotation of the shaft, a data processing unit electrically connected to the detection sensor group, and an output unit for outputting the concentricity test result; The detection sensor group includes at least two displacement sensors spaced apart along the radial direction of the rotating shaft and at least one angle sensor for obtaining the rotation angle of the rotating shaft. The displacement sensors and the angle sensors are both electrically connected to a data processing unit. The data processing unit constructs a three-dimensional spatial motion trajectory model of the rotating shaft during the rotation process based on the angular position data and radial displacement data collected in real time by the angle sensor. The method includes: using the angular position data output by the angle sensor as a rotation phase reference, synchronously sampling the radial displacement data collected by each displacement sensor at different rotation angles, combining the fixed installation spacing of the two displacement sensors in the radial direction of the rotating shaft, and based on the rotation phase reference. A spatial coordinate system conversion method maps the radial displacement and angular position of each sampling point into a three-dimensional spatial coordinate centered on the theoretical axis of the rotating shaft, forming a set of continuous trajectory points on the outer cylindrical surface of the rotating shaft within the rotation period, and the trajectory point set constitutes a three-dimensional spatial motion trajectory model; and compares the three-dimensional spatial motion trajectory model with a preset concentricity standard model, and fits the deviation value between the actual axis and the theoretical axis of the rotating shaft through spatial geometric operations to determine the concentricity parameters of the rotating shaft; the rotation drive mechanism is electrically connected to the data processing unit and is controlled by the data processing unit, driving the rotating shaft to rotate at a constant speed at a preset speed, and the data processing unit transmits the concentricity parameters to the output unit for display or storage.

2. The motor shaft concentricity inspection device according to claim 1, characterized in that: The preset concentricity standard model includes an ideal cylindrical surface model centered on the theoretical axis of the rotating shaft, and the radius of the ideal cylindrical surface is the theoretical nominal radius of the rotating shaft; the data processing unit spatially matches each trajectory point in the three-dimensional space motion trajectory model with the ideal cylindrical surface model, calculates the actual distance from each trajectory point to the theoretical axis of the rotating shaft, and counts the deviation distribution range between the actual distance and the theoretical nominal radius.

3. The motor shaft concentricity inspection device according to claim 2, characterized in that: The step of fitting the deviation between the actual axis and the theoretical axis of the rotating shaft by spatial geometric calculation to determine the concentricity parameter of the rotating shaft includes: The least squares method is used to perform axis fitting on the trajectory points in the three-dimensional space motion trajectory model to obtain the actual fitting axis of the rotating shaft during the rotation process; by calculating the spatial distance and angle between the actual fitting axis and the theoretical axis of the rotating shaft, as well as the radial offset of the actual fitting axis relative to the theoretical axis of the rotating shaft, the coaxiality deviation, radial runout and end face runout of the rotating shaft are determined as the concentricity parameters.

4. The motor shaft concentricity inspection device according to claim 3, characterized in that: The transmitting the concentricity parameter to the output unit for display or storage includes: The output unit includes at least a display module and a storage module. The display module displays the coaxiality deviation, radial runout and end face runout in real time in numerical or graphical form, and marks whether the concentricity parameters meet the preset qualification standards; the storage module stores the concentricity parameters, detection time, motor model and other information in the form of a data table, and supports the query and export of historical detection data.

5. The motor shaft concentricity inspection device according to claim 1, characterized in that: The rotation drive mechanism is electrically connected to the data processing unit and is controlled by the data processing unit to drive the rotating shaft to rotate at a constant speed at a preset speed, including: The rotation drive mechanism includes a servo motor and a speed feedback sensor. The data processing unit adjusts the drive current of the servo motor through a closed-loop control algorithm based on the speed signal collected in real time by the speed feedback sensor, so that the deviation value between the actual speed of the rotating shaft and the preset speed is kept within an allowable range, ensuring that the rotating shaft rotates at a stable speed during the detection process.

6. The motor shaft concentricity inspection device according to claim 1, characterized in that: The data processing unit is also used to obtain a trajectory classification model through supervised learning training based on the three-dimensional spatial motion trajectory features of qualified and unqualified rotating shafts in historical detection data; during the detection process, the trajectory classification model performs feature extraction and pattern matching on the currently constructed three-dimensional spatial motion trajectory model to assist in determining whether the concentricity of the rotating shaft meets the standard, and outputs the intelligent classification result to the output unit.

7. The motor shaft concentricity inspection device according to claim 1, characterized in that: The data processing unit is also used to dynamically adjust the sampling frequency of the displacement sensor according to the fluctuation of the angular position data collected by the angle sensor; when it is detected that the fluctuation of the rotation speed of the shaft exceeds a preset threshold, the sampling frequency is automatically increased to increase the trajectory point density, ensuring that complete motion trajectory data can still be obtained when the rotation speed is unstable.

8. The motor shaft concentricity inspection device according to claim 1, characterized in that: The data processing unit is also used to perform historical data fitting on multiple detection data of the same model motor, and establish a trend model for the change of the shaft concentricity parameter with the use time or the number of detections; when the deviation between the currently detected concentricity parameter and the trend model exceeds the warning threshold, a shaft wear warning signal is generated and prompted through the output unit.

9. The motor shaft concentricity inspection device according to claim 1, characterized in that: The data processing unit is also used to perform noise identification on discrete trajectory points in the three-dimensional space motion trajectory model based on preset trajectory continuity constraints; and to eliminate or correct abnormal data points caused by vibration interference and sensor noise through a time series smoothing algorithm or a neighborhood interpolation method, thereby improving the accuracy of the concentricity parameter calculation.

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