A dynamic detection method for roundness fluctuation of bearing inner ring

Abstract

This invention relates to a dynamic detection method for the roundness fluctuation of the inner ring of a bearing in the field of bearing testing technology, comprising the following steps: S1, supporting the outer ring of the bearing with non-contact magnetic force and actively stabilizing the outer ring in a preset spatial posture; S2, applying a preset multi-dimensional dynamic excitation force to the outer ring of the bearing; S3, driving the inner ring of the bearing to rotate around its axis. This invention combines non-contact magnetic support and stabilization of the outer ring with independent driving of the inner ring rotation, so that the bearing under test is in a dynamic condition that is closer to the actual complex support state, thereby stimulating a more realistic and richer dynamic fluctuation signal. Based on the dynamic response signal, the dynamic roundness fluctuation component caused by the roundness error of the inner ring raceway is separated and quantified by a coupling and decoupling algorithm.

Description

Technical Field

[0001] This invention relates to the field of bearing testing technology, and in particular to a dynamic testing method for the roundness fluctuation of the inner ring of a bearing. Background Technology

[0002] The roundness accuracy of the inner ring raceway of a rolling bearing is its core quality indicator, directly affecting rotational accuracy and service reliability. Traditional roundness inspection methods are mainly divided into two categories: static geometric measurement and dynamic runout measurement, both of which have inherent limitations in their methodology.

[0003] Static geometric measurement methods (such as roundness measurement) involve high-precision scanning of the raceway profile while the bearing is stationary. While this method can obtain accurate profile shape data, its methodology is fundamentally detached from actual operating conditions. In real-world applications, bearings rotate at high speeds and bear dynamic loads, resulting in error behavior that differs drastically from its static state. Static measurements cannot capture the vibrations and fluctuations generated by the interaction of roundness errors with the rolling elements and cage in a dynamic system; therefore, their evaluation results have a weak correlation with the bearing's actual operating performance.

[0004] Dynamic runout measurement methods (such as radial runout measurement) involve rotating the bearing and measuring the displacement changes on the bearing race surfaces. While this method introduces a dynamic factor, it suffers from signal aliasing and boundary distortion. First, the measured runout signal is a mixture of roundness error, installation eccentricity, surface waviness, and even drive system vibration. Existing methods lack effective techniques to separate the pure roundness error component from this mixed signal. Second, to achieve the measurement, this method typically requires rigidly clamping the bearing outer race and rigidly connecting the inner race to the drive shaft. This artificially introduced strong constraint boundary condition alters the bearing's flexible support state in most real-world applications, leading to distorted dynamic response. The measured "dynamic" signal cannot accurately reflect its fluctuation characteristics under free or weakly constrained conditions.

[0005] Some studies have attempted to assess bearing ripple by monitoring changes in centrifugal force during rotation (e.g., a bearing roundness ripple detection device and method disclosed in Chinese Patent CN118031886B). While this method offers improvements in dynamic assessment, its core reliance on single-dimensional physical signals (such as unidirectional force or displacement) remains a limiting factor. In complex bearing dynamic systems, single-dimensional signals cannot fully characterize the multi-dimensional coupled vibration state, leading to incomplete diagnostic information, an inability to distinguish fault sources, and limitations on the accuracy and reliability of the assessment.

[0006] In summary, existing detection methods face common methodological challenges: either the detection state is disconnected from real operating conditions, or they cannot decouple target features from mixed dynamic signals, or their single perception dimension leads to biased evaluation. Therefore, there is an urgent need for a new detection method that can accurately isolate, perceive, and quantitatively evaluate the roundness fluctuation component of the bearing inner ring raceway under dynamic conditions that closely resemble real operating conditions. Summary of the Invention

[0007] To overcome the shortcomings of the prior art, this invention discloses a dynamic detection method for the roundness fluctuation of the inner ring of a bearing.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A dynamic detection method for the roundness fluctuation of the inner ring of a bearing includes the following steps: S1. The outer ring of the bearing is supported by non-contact magnetic force, and the outer ring of the bearing is actively stabilized in a preset spatial position. S2. Apply a preset multi-dimensional dynamic excitation force to the outer ring of the bearing; S3, drives the inner ring of the bearing to rotate around its axis; S4: Synchronously acquire multidimensional dynamic response signals of the bearing under rotation and excitation states, wherein the dynamic response signals include at least multidimensional force / torque feedback signals from the outer ring of the bearing and multidimensional displacement signals from the inner ring of the bearing; S5. Based on the dynamic response signal, the dynamic roundness fluctuation component caused by the roundness error of the inner ring raceway of the bearing is separated and quantified by the coupling and decoupling algorithm.

[0009] Furthermore, in step S1, a primary levitation force is provided by multiple first magnetic action points distributed axially downward along the outer ring of the bearing, and a radial positioning and excitation force is provided by multiple second magnetic action points distributed radially outward along the outer ring of the bearing, so as to synthesize and realize six-degree-of-freedom active control of the spatial pose of the outer ring of the bearing.

[0010] Furthermore, the multidimensional force / torque feedback signal is obtained by measuring three orthogonal force components and three orthogonal torque components at each of the first and second magnetic force application points.

[0011] Furthermore, the multidimensional displacement signal from the bearing inner ring is obtained by simultaneously measuring the axial displacement of the upper and lower end faces of the bearing inner ring, and calculating the radial runout component of the inner ring based on the displacement difference between the two end faces.

[0012] Furthermore, in step S2, the multidimensional dynamic excitation force includes harmonic excitation forces whose frequency components cover the fundamental frequency of the inner ring of the bearing and its harmonics.

[0013] Furthermore, in step S3, a sub-step for outer ring angular stabilization control is performed simultaneously: real-time monitoring and counteracting of the torque acting on the outer ring of the bearing due to internal bearing friction, so as to keep the outer ring of the bearing in a fixed angular position.

[0014] Further, step S5 includes: A1. Establish a parameterized digital twin model that includes the magnetic levitation support system, active excitation input, and the internal coupling relationship of the bearing; A2. Based on the digital twin model and the collected dynamic response signals, identify system parameters in real time; A3. From the dynamic response signal, apply a coupling-decoupling algorithm to subtract the response components predicted by the active excitation and the known dynamic characteristics of the system, and extract the residual fluctuation signal that is strictly synchronized with the inner ring rotation angle position as the dynamic roundness fluctuation component.

[0015] Furthermore, the dynamic roundness fluctuation component is output in the form of a dynamic roundness fluctuation spectrum, which characterizes the contribution of roundness error to the dynamic response under different harmonic orders, and is quantitatively evaluated by calculating the integral value B of the fluctuation degree.

[0016] Furthermore, in step A3, the coupling decoupling algorithm includes the following steps: B1. Construct state-space equations based on the parameterized digital twin model; B2. Using the recursive least squares method or Kalman filter algorithm, combined with the collected dynamic response signal, the equivalent stiffness and damping parameters of the bearing system are identified in real time online. B3. Estimate and subtract the predicted response components generated by the active excitation and the known dynamic characteristics of the system using a state observer; B4. Perform synchronous averaging or order tracking analysis on the residual response signal with reference to the inner ring rotation angle position, and extract the periodic component that is strictly phase-locked with the rotation angle position as the dynamic roundness fluctuation component.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This method creates a dynamic testing environment with flexible boundaries by combining "non-contact magnetic support and stabilization of the outer ring" with "independent driving of the inner ring rotation". This is different from the traditional rigid clamping method. It puts the bearing under test in a dynamic condition that is closer to the actual complex support state, thereby stimulating more realistic and richer dynamic fluctuation signals. Based on the dynamic response signals, the dynamic roundness fluctuation component caused by the roundness error of the bearing inner ring raceway is separated and quantified by the coupling and decoupling algorithm. This realizes a closed-loop evaluation of the dynamic roundness accuracy of the bearing inner ring and ensures a high correlation between the test results and the actual operating performance of the bearing. 2. This method includes the active step of "applying a preset multi-dimensional dynamic excitation force", which makes the detection no longer just passively observe the performance of the bearing under a single rotation, but can actively inject a known multi-directional excitation spectrum into the system, thereby systematically stimulating the defect response that may be masked by a single working condition. This proactive approach greatly enhances the detection depth and coverage of potential roundness errors. 3. This method requires "synchronous acquisition of multi-dimensional force / torque feedback signals and multi-dimensional displacement signals". This synchronous acquisition of multi-source heterogeneous information provides a comprehensive and three-dimensional data profile for subsequent decoupling analysis, fundamentally overcoming the shortcomings of insufficient information in single-dimensional signals and the inability to distinguish coupling effects. 4. This method, through a series of algorithmic steps such as establishing a digital model, online identification, and signal separation, can intelligently eliminate the influence of known excitations and the inherent dynamics of the system from complex multidimensional mixed responses, and finally extract the unique residual wave component that is strictly synchronized with the inner circle rotation angle position.

[0018] This invention systematically integrates four core methodologies: flexible boundary simulation, multi-dimensional active excitation, full-information synchronous perception, and intelligent signal decoupling, forming a novel, closed-loop methodology for bearing dynamic precision testing. It effectively addresses the inherent limitations of existing methods in terms of dynamic realism, signal separation, and evaluation accuracy, and is of great significance for achieving high-level evaluation and precise control of bearing product quality. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the main hardware distribution in this invention; Figure 2 This is a schematic diagram showing the fit between the non-contact displacement sensor and the inner ring of the bearing in this invention.

[0020] In the diagram: 1. Axial suspension electromagnet; 2. Radial excitation electromagnet; 3. Six-axis force sensor; 4. Servo motor; 5. Flexible coupling; 6. Conical guide head; 7. Non-contact displacement sensor. Detailed Implementation

[0021] The technical solutions of the present invention will be described in detail, completely, and practically through the following embodiments. It should be understood that the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Example 1, in conjunction with Appendix Figure 1-2 A dynamic detection method for the roundness fluctuation of the inner ring of a bearing includes the following steps: S1: The outer ring of the bearing is supported by non-contact magnetic force, and the outer ring of the bearing is actively stabilized in a preset spatial position.

[0023] Specifically, the main levitation force is provided by multiple first magnetic action points distributed axially downward along the outer ring of the bearing, and the radial positioning and excitation force is provided by multiple second magnetic action points distributed radially outward along the outer ring of the bearing, so as to achieve six degrees of freedom active control of the spatial pose of the outer ring of the bearing.

[0024] As a specific embodiment, the first magnetic force application point can be achieved by four axially levitating electromagnets 1. In an established spatial rectangular coordinate system (the theoretical center of the outer ring of the stabilized bearing is defined as the origin O, and its theoretical axis is the Z-axis), the coordinates of the center points of the magnetic pole surfaces of these four axially levitating electromagnets 1 can be set as (Ra,0,-Ha), (-Ra,0,-Ha), (0,Ra,-Ha), and (0,-Ra,-Ha), respectively, where Ra is a constant greater than the radius of the outer ring of the bearing, Ha is the set levitation gap height, and the normal direction of the magnetic pole surface of each axially levitating electromagnet 1 is parallel to the positive direction of the Z-axis, thereby generating a vertically upward levitation force. The second magnetic force application point can be achieved by four additional radial excitation electromagnets 2. The coordinates of the center point of their magnetic pole application surfaces can be set as (Ra+δ,0,0), (-(Ra+δ),0,0), (0,Ra+δ,0), and (0,-(Ra+δ),0), respectively, where δ is the set radial application gap. The normal direction of the magnetic pole surface of each radial excitation electromagnet 2 points to the negative or positive direction of the coordinate axis, respectively, to provide horizontal positioning force and excitation force. To achieve active stabilization control, a six-axis force sensor 3 is rigidly connected to each of the axial suspension electromagnets 1 and radial excitation electromagnets 2 to measure the three force components and three torque components at the application point in real time. The controller (not shown in the figure) adjusts the current of each axial suspension electromagnet 1 and radial excitation electromagnet 2 in real time according to the signals fed back by these six-axis force sensors 3, so that the position and attitude of the bearing outer ring in three-dimensional space remain stable.

[0025] S2: Apply a preset multi-dimensional dynamic excitation force to the outer ring of the bearing.

[0026] Specifically, by controlling the force on the radial excitation electromagnet 2 at the second magnetic application point, a dynamic excitation force containing a specific frequency component can be applied. For example, by inputting a current with an amplitude that varies sinusoidally and whose frequency is adjustable to a pair of radial excitation electromagnets 2 with center points located at (Ra+δ,0,0) and (-(Ra+δ),0,0), a simple harmonic excitation force with a controllable frequency along the X-axis can be generated on the outer ring of the bearing. The frequency component of the excitation force can be set according to the detection requirements, for example, covering the fundamental frequency of the inner ring of the bearing and its harmonics.

[0027] S3: Drive the inner ring of the bearing to rotate about its axis.

[0028] Specifically, a servo motor 4, coaxial with the Z-axis, is connected to the inner ring of the bearing via a flexible coupling 5 and a tapered guide head 6 to drive its rotation. During this process, to maintain the fixed angular position of the outer ring, angular stability control of the outer ring must be performed simultaneously. This involves real-time monitoring of the torque acting on the outer ring due to internal bearing friction, and dynamically adjusting the force distribution of the radially excited electromagnet 2 at the second magnetic application point using a closed-loop control algorithm to generate a reverse compensation torque equal in magnitude but opposite in direction to the friction torque.

[0029] S4: Synchronously acquire the multidimensional dynamic response signal of the bearing under rotation and excitation states. The dynamic response signal includes at least the multidimensional force / torque feedback signal from the outer ring of the bearing and the multidimensional displacement signal from the inner ring of the bearing.

[0030] Specifically, the multidimensional force / torque feedback signal is obtained by reading the output of the six-axis force sensor 3. As described in step S1, the six-axis force sensor 3 is rigidly connected to the corresponding axial suspension electromagnet 1 or radial excitation electromagnet 2. Each six-axis force sensor 3 provides three orthogonal force components and three orthogonal torque components at its corresponding point of action.

[0031] The multidimensional displacement signal from the inner ring of the bearing is obtained by using two non-contact displacement sensors 7, which are aligned with the upper and lower end faces of the inner ring of the bearing for synchronous measurement.

[0032] In one specific embodiment, to avoid the upper drive connection components (such as couplings or drive shafts), the two sensors are mounted laterally. Specifically, the first displacement sensor for measuring the upper end face has its probe located at coordinates (Rs, 0, Zu), and its measuring beam direction points towards the outer edge measuring point of the upper end face of the inner ring. This beam direction is not parallel to the Z-axis, but has a direction vector (-cosθ, 0, -sinθ), where θ is the angle between the beam and the horizontal plane, Rs > bearing outer ring radius, and Zu > 0. Similarly, the second displacement sensor for measuring the lower end face has its probe located at coordinates (Rs, 0, Zb), and its measuring beam direction points towards the outer edge measuring point of the lower end face of the inner ring, with a direction vector of (-cosθ, 0, sinθ), where Zb < 0.

[0033] After system installation, a calibration program determines the reference distance of each sensor along its measuring optical axis. During measurement, the sensors read the changes in distance ΔLu(t) and ΔLb(t) from their respective optical axes to the end-face measurement point. Based on the geometric relationship between the sensor's spatial position and the beam direction, ΔLu(t) and ΔLb(t) can be calculated as the pure axial displacements du(t) and db(t) of the upper and lower end faces of the inner ring in the Z-axis direction. The radial runout component of the inner ring can then be further calculated based on the difference between du(t) and db(t).

[0034] At the same time, the encoder signal of the motor is collected synchronously and used as a reference for the rotation angle position of the inner ring.

[0035] S5: Based on the dynamic response signal, the dynamic roundness fluctuation component caused by the roundness error of the inner ring raceway of the bearing is separated and quantified by the coupling and decoupling algorithm.

[0036] Specifically, it includes the following sub-steps: A1: Establish a parameterized digital twin model that includes the magnetic levitation support system, active excitation input, and the internal coupling relationship of the bearing.

[0037] Among them, the internal coupling relationship of the bearing mainly refers to the nonlinear Hertzian contact stiffness between the rolling elements and the inner and outer raceways, the dynamic behavior of the cage, and the influence of lubrication conditions. These relationships are parameterized and embedded in the digital twin model.

[0038] A2: Based on the digital twin model and the dynamic response signal collected by S4, the system model parameters (such as damping and stiffness) are identified and updated in real time using algorithms such as recursive least squares.

[0039] A3: From the total dynamic response signal acquired in S4, a coupling-decoupling algorithm is applied to subtract the theoretical response components predicted by the active excitation input and the system model updated online, thereby extracting the residual response signal. Subsequently, using the encoder signal of the motor synchronously acquired in step S4 as a phase reference, synchronous averaging or order tracking analysis is performed on the residual signal to extract the periodic component that is strictly synchronized with the inner ring rotation angle position; this is the dynamic roundness fluctuation component.

[0040] The coupling-decoupling algorithm specifically includes the following steps: B1. Construct state-space equations based on this digital twin model; B2. Using the recursive least squares method or Kalman filter algorithm, combined with the collected dynamic response signal, the equivalent stiffness and damping parameters of the bearing system are identified in real time online. B3. Estimate and subtract the predicted response components generated by the active excitation and the known dynamic characteristics of the system using a state observer; B4. Perform synchronous averaging or order tracking analysis on the residual response signal with reference to the inner ring rotation angle position, and extract the periodic component that is strictly phase-locked with the rotation angle position as the dynamic roundness fluctuation component.

[0041] The dynamic roundness fluctuation component is output in the form of a dynamic roundness fluctuation spectrum, and is quantitatively evaluated by calculating the integral value B of the fluctuation degree. The B value is used to comprehensively evaluate the intensity of dynamic fluctuations caused by roundness error throughout the entire test speed range. Its calculation method is as follows: (Integration interval: from the initial angular velocity ω) start angular velocity ω to the endpoint end ); Wherein, the integral variable ω is the rotational angular velocity of the inner ring of the bearing, F(ω) is the vertical coordinate value of the measured fluctuation curve obtained from the residual fluctuation signal at the angular velocity ω, and F'(ω) is the vertical coordinate value of the fitted curve obtained after fitting the measured fluctuation curve at the angular velocity ω. This fitted curve is used to characterize the ideal smooth state without roundness error. represents the absolute value of the difference between the two; 'a' is a normalization constant used to eliminate the dimensional influence of factors such as sensor gain and system amplification factor on the signal amplitude. Its value can be the nominal signal amplitude under benchmark test conditions, the theoretically calculated amplitude, or the maximum permissible fluctuation amplitude of the system.

[0042] Example 2, in conjunction with Appendix Figure 1-2 A dynamic detection method for the roundness fluctuation of the inner ring of a bearing. The difference between this embodiment and Embodiment 1 is that the non-contact displacement sensor can be replaced by a laser triangulation rangefinder or an eddy current displacement sensor. The waveform of the dynamic excitation force signal can be replaced with a pseudo-random binary sequence or a pulse sequence; The online identification algorithm can be replaced by the Kalman filter algorithm.

[0043] The parts of this invention not described in detail are prior art. It will be apparent to those skilled in the art that this invention is not limited to the details of the above exemplary embodiments, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the above embodiments should be regarded as exemplary and non-limiting in all respects. The scope of this invention is defined by the appended claims rather than the foregoing description. Therefore, it is intended to include all changes that fall within the meaning and scope of the equivalents of the claims within this invention, and no reference numerals in the claims should be regarded as limiting the content of the claims.

Claims

1. A dynamic detection method for the roundness fluctuation of the inner ring of a bearing, characterized in that: Includes the following steps: S1. The outer ring of the bearing is supported by non-contact magnetic force, and the outer ring of the bearing is actively stabilized in a preset spatial position. S2. Apply a preset multi-dimensional dynamic excitation force to the outer ring of the bearing; S3, drives the inner ring of the bearing to rotate around its axis; S4: Synchronously acquire multidimensional dynamic response signals of the bearing under rotation and excitation states, wherein the dynamic response signals include at least multidimensional force / torque feedback signals from the outer ring of the bearing and multidimensional displacement signals from the inner ring of the bearing; S5. Based on the dynamic response signal, the dynamic roundness fluctuation component caused by the roundness error of the inner ring raceway of the bearing is separated and quantified by the coupling and decoupling algorithm.

2. The dynamic detection method for the roundness fluctuation of the bearing inner ring according to claim 1, characterized in that: In step S1, the main levitation force is provided by multiple first magnetic action points distributed axially downward along the outer ring of the bearing, and the radial positioning and excitation force is provided by multiple second magnetic action points distributed radially outward along the outer ring of the bearing, so as to achieve six degrees of freedom active control of the spatial pose of the outer ring of the bearing.

3. The dynamic detection method for the roundness fluctuation of the bearing inner ring according to claim 2, characterized in that: The multidimensional force / torque feedback signal is obtained by measuring three orthogonal force components and three orthogonal torque components at each of the first and second magnetic force application points.

4. The dynamic detection method for the roundness fluctuation of the bearing inner ring according to claim 1, characterized in that: The multidimensional displacement signal from the bearing inner ring is obtained by simultaneously measuring the axial displacement of the upper and lower end faces of the bearing inner ring, and calculating the radial runout component of the inner ring based on the displacement difference between the two end faces.

5. A dynamic detection method for the roundness fluctuation of the inner ring of a bearing according to claim 1 or 2, characterized in that: In step S2, the multidimensional dynamic excitation force includes harmonic excitation forces whose frequency components cover the fundamental frequency of the inner ring of the bearing and its harmonics.

6. The dynamic detection method for the roundness fluctuation of the bearing inner ring according to claim 2, characterized in that: In step S3, the outer ring angular stabilization control sub-step is executed synchronously: real-time monitoring and counteracting of the torque acting on the outer ring of the bearing due to internal bearing friction, so as to keep the outer ring of the bearing in a fixed angular position.

7. The dynamic detection method for the roundness fluctuation of the bearing inner ring according to claim 1, characterized in that: Step S5 includes: A1. Establish a parameterized digital twin model that includes the magnetic levitation support system, active excitation input, and the internal coupling relationship of the bearing; A2. Based on the digital twin model and the collected dynamic response signals, identify system parameters in real time; A3. From the dynamic response signal, apply a coupling-decoupling algorithm to subtract the response components predicted by the active excitation and the known dynamic characteristics of the system, and extract the residual fluctuation signal that is strictly synchronized with the inner ring rotation angle position as the dynamic roundness fluctuation component.

8. A dynamic detection method for the roundness fluctuation of the bearing inner ring according to claim 1 or 7, characterized in that: The dynamic roundness fluctuation component is output in the form of a dynamic roundness fluctuation spectrum, which characterizes the contribution of roundness error to the dynamic response under different harmonic orders, and is quantitatively evaluated by calculating the integral value B of the fluctuation degree.

9. The method according to claim 7, characterized in that, In step A3, the coupling decoupling algorithm includes the following steps: B1. Construct state-space equations based on the parameterized digital twin model; B2. Using the recursive least squares method or Kalman filter algorithm, combined with the collected dynamic response signal, the equivalent stiffness and damping parameters of the bearing system are identified in real time online. B3. Estimate and subtract the predicted response components generated by the active excitation and the known dynamic characteristics of the system using a state observer; B4. Perform synchronous averaging or order tracking analysis on the residual response signal with reference to the inner ring rotation angle position, and extract the periodic component that is strictly phase-locked with the rotation angle position as the dynamic roundness fluctuation component.

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