Yarn textile high speed spindle running stability test method and system

Abstract

The present application relates to the technical field of textile equipment testing, in particular to a yarn textile high-speed spindle running stability testing method and system, comprising the following steps: S1. Test element layout; S2. System initialization; S3. Transient test; S4. Steady-state test; S5. Interference processing: spectrum analysis of original collected data, identification of interference frequency characteristics, combination of notch filtering and Kalman filtering to eliminate interference and verify filtering effect; S6. Parameter correlation modeling: control variable method is used to construct vibration-temperature-tension-rotation speed parameter correlation model, model parameters are solved through fitting algorithm and effectiveness is verified; S7. Quality traceability; S8. Stability determination; S9. Real-time monitoring and regulation. The present application adopts a combination filtering scheme of notch filtering and Kalman filtering, specifically suppresses fixed interference such as power frequency and mechanical resonance and random interference, through local amplified signal comparison, it can be seen that the filtered signal waveform is smooth and noise is completely removed, and the signal-to-noise ratio and reliability of the vibration detection signal are improved.

Description

Technical Field

[0001] This invention relates to the field of textile equipment testing technology, and in particular to a method and system for testing the operational stability of high-speed spindles in yarn spinning. Background Technology

[0002] As the core rotating actuator of yarn spinning equipment, the high-speed spindle's operational stability directly determines key quality indicators such as yarn twist uniformity, breaking strength, and hairiness index. Currently, the rotational speed of mainstream high-speed spindles has generally reached 12,000-25,000 r / min. Under this condition, the transient impact response, dynamic load adaptation, and multi-physics coupling characteristics of the spindle have an increasingly significant impact on operational stability.

[0003] Traditional methods for testing the operational stability of high-speed spindles generally suffer from incomplete coverage of operating conditions, low parameter correlation, and weak resistance to interference, making them difficult to adapt to the complex operating scenarios in actual spinning processes.

[0004] For example, patent document CN105352591A discloses a method for testing the vibration characteristics of a spinning spindle. The method mainly involves taking an image of the top of the spindle at an angle when the spindle is stationary, obtaining an image of the spindle end as an ellipse, and finding the center of the ellipse. The coordinates of the center of the ellipse in a rectangular coordinate system are then obtained, which is the offset of the initial position of the spindle center relative to the origin. When the spindle rotates, the coordinates of the center of the spindle ellipse in the rectangular coordinate system at this time are obtained. When the rotational speed is stable, the axis trajectory of the spindle at that rotational speed is depicted by numerical fitting.

[0005] As can be seen from the above description, it has the following drawbacks when used:

[0006] First, the above test method only tests the shaft trajectory in the steady-state stage after the speed stabilizes, ignoring the strong transient impact process when the high-speed spindle starts up and reaches the rated speed. Since the amplitude in this stage can be 2-3 times that in the steady state, it directly affects the stability of the bearing preload. At the same time, it does not consider the dynamic load change caused by the doubling of the diameter from the empty tube to the full tube during the yarn winding process, resulting in a significant deviation between the test results and the actual spinning conditions.

[0007] Second, the test method only collects the mechanical vibration parameter of the spindle axis trajectory, which cannot locate the yarn details and uneven twist. The test results cannot accurately reflect the actual stability under high-speed spindle operation.

[0008] Therefore, it is essential to design a method and system for testing the operational stability of high-speed spindles under complex operating scenarios. Summary of the Invention

[0009] To solve one of the aforementioned technical problems, the present invention provides a method for testing the operational stability of high-speed spindles in yarn spinning, comprising the following steps:

[0010] S1. Test Component Layout: Based on the spindle structure and test requirements, deploy vibration, temperature, tension, and rotation speed detection components and interference simulation components to construct a complete test link;

[0011] S2. System initialization: Zero-point calibration of the detection element, input of basic parameters such as spindle rated speed and winding diameter, and verification of signal transmission effectiveness through low-speed operation;

[0012] S3. Transient test: Taking the spindle start-up as the trigger point, the start-up phase parameters are collected according to the preset strategy, and the pre-processed parameters are stored as a transient dataset.

[0013] S4. Steady-state test: After the speed fluctuation reaches the standard, the steady-state test is triggered. The sampling strategy is dynamically adjusted according to the winding diameter and stops after the full tube diameter is collected.

[0014] S5. Interference handling: Spectral analysis of the original acquired data is performed to identify the frequency characteristics of interference. A combination of notch filtering and Kalman filtering is used to eliminate interference and verify the filtering effect.

[0015] S6. Parameter correlation modeling: A vibration-temperature-tension-rotation parameter correlation model is constructed using the controlled variable method. The model parameters are solved by a fitting algorithm and the effectiveness is verified.

[0016] S7. Quality Traceability: Simultaneously collect spindle operating parameters and yarn quality parameters, construct a mapping model between the two, and calculate the traceability deviation;

[0017] S8. Stability determination: Combining the stable operation threshold output by the parameter correlation model and whether the quality traceability deviation meets the standard, the stability of the spindle operation is comprehensively determined;

[0018] S9. Real-time monitoring and control: Set threshold ranges for each parameter, monitor operating parameters in real time, trigger alarms and generate control suggestions when parameters exceed thresholds, and verify the parameter stabilization effect after execution.

[0019] Based on any of the above technical solutions, the following optimization is made: the test element layout in step S1 is as follows: a laser vibration meter for vibration detection, with the laser axis perpendicular to the spindle radially and aligned with the center; an infrared thermometer for temperature detection, with the lens focused on the central area of ​​the spindle; an optical fiber tension sensor for tension detection, with the detection hole coaxial with the yarn running trajectory; a magnetoelectric sensor for speed detection, with the probe maintaining a preset gap with the end of the spindle shaft; and interference simulation elements including a resonance generator and an airflow generator, deployed in different radial directions of the spindle and with the direction of action covering the core running area.

[0020] Based on any of the above technical solutions, the following optimization is performed: the system initialization in step S2 is performed as follows: the laser vibrometer, infrared thermometer, and fiber optic tension sensor are calibrated at zero point and the parameters are recorded in sequence; supplementary parameters such as rated load and initial winding diameter are entered; the communication status of the signal transmission link is verified; the spindle is controlled to run at 30% of the rated speed for 5-10 minutes, and the signal transmission is deemed to be effective if the data acquisition is uninterrupted and without abnormal fluctuations.

[0021] Based on any of the above technical solutions, the following optimization is made: the sampling strategy for steps S3-S4 is as follows: the transient sampling rate is 3-5 times the steady-state sampling rate; a preset winding diameter sampling rate switching threshold is set, with a switching interval of 5-10mm, and the sampling rate is automatically reduced after the diameter reaches the standard; transient sampling is triggered by the start signal, steady-state sampling is triggered by the speed fluctuation amplitude ≤±1%, and sampling stops when the winding diameter reaches the full tube diameter.

[0022] Based on any of the above technical solutions, the following optimization is performed: the interference processing in step S5 is performed as follows: the original data spectrum is analyzed, and the interference spectrum library is called to identify the frequency characteristics of interference such as power frequency interference (50Hz) and mechanical resonance; notch filters are configured and filtered for fixed frequency interference; Kalman filtering is performed on the filtered signal to suppress random interference; the filtering effectiveness verification standard is that the signal-to-noise ratio is improved by ≥10dB, and the change rate of key parameters such as vibration amplitude and rotation speed fluctuation is ≤5%.

[0023] Based on any of the above technical solutions, the following optimization is performed: the parameter association modeling in step S6 is carried out as follows: the initial vibration amplitude of the spindle is collected as the benchmark parameter under the standard environment of constant temperature 25℃, constant tension 15cN, and rated speed; the nonlinear least squares method is selected as the fitting algorithm and the benchmark parameter is imported; single-factor variable control operation is carried out, and the variable control range is temperature 25-70℃, tension 5-30cN, and speed 12000-25000r / min. After each variable adjustment, the corresponding vibration amplitude is collected when the fluctuation amplitude of the spindle parameter is ≤±1%.

[0024] Matching parameters associated with the model: Where A is the spindle vibration amplitude (mm / s²), A0 is the measured reference vibration amplitude (mm / s²) under standard conditions, T is the real-time spindle temperature (°C), T0 is the 25°C reference temperature, F is the real-time yarn tension (cN), ω is the real-time spindle angular velocity, and α is the temperature influence coefficient (1 / °C, valued at 1.2 × 10⁻⁻⁴). 5 / ℃), β is the tension-temperature interaction coefficient (mm / (s²·cN·℃), with a value of 8×10⁻ 4 mm / (s²·cN·℃)), δ is the angular velocity influence coefficient (mm·s / (rad·s²), with a value of 5×10⁻6 mm·s / (rad·s²)); Select 3 sets of non-experimental interval parameters to calculate the predicted values. If the deviation from the actual values ​​is ≤8%, the model is considered effective.

[0025] Based on any of the above technical solutions, the following optimization is performed: The quality traceability in step S7 is executed as follows: The yarn quality testing equipment is connected in series with the yarn output end to ensure synchronization with the spindle operating parameter acquisition time; the spindle and quality testing equipment are started synchronously, recording the vibration amplitude, rotational speed parameters, and yarn twist variation coefficient (CV value) and marking the timestamp; invalid data with rotational speed fluctuations > ±2% are removed; a mapping model is constructed using a power function algorithm. Where CV is the yarn twist variation coefficient (%), A is the spindle vibration amplitude (mm / s²), and k1 is the vibration influence coefficient (%·(s² / mm)¹· 5 The value is 4.8%·(s² / mm)¹· 5 k2 is the baseline CV value (%, with a value of 0.6%); 10 sets of samples are selected to calculate the traceability deviation. If the average deviation is ≤5%, the model is deemed effective; the running parameters, quality parameters and traceability deviation are bound and stored to generate a quality traceability data table.

[0026] Based on any of the above technical solutions, the following further optimization is made: Real-time monitoring and control in step S9 is as follows: Set threshold ranges for each parameter based on the parameter correlation model; continuously collect and preprocess operating parameters, and dynamically update parameter change curves; if a parameter exceeds the threshold for 3-5 consecutive sampling cycles, an anomaly is determined, triggering an audible and visual alarm and indicating the type of anomaly; for different abnormal output control schemes: for abnormal temperature, adjust the temperature control device and load; for abnormal tension, adjust the tension regulator and check the yarn guide components; for abnormal vibration, fine-tune the speed and check the mechanical fastening status; after control, track the parameters, and if they return to the threshold, stop the alarm and record the log; if the system fails to stabilize after 3 consecutive optimizations, trigger a shutdown warning.

[0027] The present invention also provides a high-speed spindle operation stability testing system for yarn spinning, wherein the system stores a computer program, and when the computer program is executed by a processor, it implements the above-described method for testing the high-speed spindle operation stability of yarn spinning.

[0028] This invention also provides a high-speed spindle operation stability testing system for yarn spinning, including a detection element module, an interference simulation module, a multi-channel acquisition unit, a host computer, a control and execution module, and a power supply module; the connection relationship and function of each module are as follows:

[0029] 1. Detection element module and multi-channel acquisition unit: The detection element module includes a laser vibrometer, an infrared thermometer, a fiber optic tension sensor, and a magnetoelectric speed sensor. The analog signal output terminals of each sensor are connected one-to-one to the dedicated analog signal input channel of the multi-channel acquisition unit through independent twisted-pair shielded cables. The interface is an industrial standard M12 aviation plug (protection level IP67), and the signal is transmitted in one direction.

[0030] 2. Interference simulation module and host computer: The interference simulation module includes a resonance generator and an airflow generator. Its control signal input terminal communicates bidirectionally with the host computer's asynchronous serial communication port through a shielded RS485 bus (ModbusRTU protocol, baud rate 9600bps). The host computer issues commands to control the resonance frequency and wind speed, and the command response delay is ≤50ms.

[0031] 3. Multi-channel acquisition unit and host computer: The digital signal output terminal (RJ45 interface) of the acquisition unit communicates with the host computer's Ethernet port via an industrial Ethernet cable (CAT5E or higher specification) based on the Modbus TCP protocol. The data transmission rate is ≥100Mbps, the delay is ≤20ms, and CRC-32 check is used.

[0032] 4. Host computer and control execution module: The control execution module includes a temperature control device, a tension regulator, and a speed controller. The digital control output terminal of the host computer is connected one-to-one to the dedicated signal input terminal of each controller through an independent shielded control cable (DC24V differential signal, wire diameter ≥0.75mm²). The links are independent and not shared.

[0033] 5. Power supply module and functional modules: The power supply module is a 24VDC switching power supply (output ripple ≤50mV, rated power ≥500W). It is connected one-to-one to the dedicated power input terminal of each module through an independent shielded power supply line (wire diameter ≥1.0mm²). The distance between the power supply line and the signal line is ≥10cm. The grounding wire diameter is ≥1.5mm² and the grounding resistance is ≤4Ω.

[0034] The communication delay of each module in the system is ≤100ms, and the power supply voltage fluctuation is ≤±5%, which meets the real-time and stability requirements of high-speed spindle testing.

[0035] Based on any of the above technical solutions, a further optimization is made to the testing system used to implement the above-mentioned method for testing the operational stability of high-speed spindles in yarn spinning.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] 1. The multi-parameter coupled prediction model for high-speed spindle vibration amplitude constructed in this invention comprehensively considers the interactive influence of three core operating parameters: temperature, tension, and rotational speed. It accurately characterizes the effect mechanism of each parameter on vibration by combining exponential functions and linear superposition. Compared with single-parameter prediction models, it can more realistically reflect the vibration characteristics of spindles under the synergistic effect of multiple factors in actual production, and provide accurate data support for subsequent vibration control.

[0038] 2. By establishing a power function correlation model between spindle vibration amplitude and yarn twist variation coefficient, the influence of vibration on yarn quality is clearly quantified. This allows staff to directly predict yarn quality levels by monitoring spindle vibration data, eliminating the need for complex offline testing procedures and significantly improving the timeliness and efficiency of quality control during production.

[0039] 3. This invention adopts a combined filtering scheme of notch filter and Kalman filter to specifically suppress fixed interference such as power frequency and mechanical resonance, as well as random interference. As can be seen from the local magnified signal comparison, the filtered signal waveform is smooth and the noise is thoroughly removed, which effectively improves the signal-to-noise ratio and reliability of the vibration detection signal and avoids interference signals from affecting vibration characteristic analysis and quality prediction.

[0040] 4. This scheme, through an exponential term, aligns with the exponential variation characteristics of temperature deformation and vibration amplitude, achieving modeling accuracy far exceeding that of linear models. Furthermore, it introduces a tension-temperature interaction term to accurately characterize the coupled effects of multiple parameters: existing technologies often consider the influence of a single parameter on vibration in isolation, neglecting the synergistic effect of tension and temperature. This scheme incorporates an interaction term to clarify the coupling relationship between tension F and temperature deviation, resolving the problem of large prediction biases caused by the omission of coupling effects in traditional models. Attached Figure Description

[0041] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0042] Figure 1 This is a graph showing the prediction model of the high-speed spindle vibration amplitude and multi-parameter coupling relationship of the present invention.

[0043] Figure 2 This is a graph showing the correlation between the spindle vibration amplitude and the yarn twist variation coefficient power function of the present invention.

[0044] Figure 3 This is a simulation comparison curve of the interference suppression effect of the test method of the present invention.

[0045] Figure 4This is a schematic diagram of the connection block of the test system of the present invention. Detailed Implementation

[0046] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific structure of the present invention is as follows: Figures 1-4 As shown in the image.

[0047] Example 1: A method for testing the operational stability of high-speed spindles in yarn spinning, comprising the following steps:

[0048] S1. Test Component Layout: Based on the spindle structure and test requirements, deploy vibration, temperature, tension, and rotation speed detection components and interference simulation components to construct a complete test link;

[0049] S2. System initialization: Zero-point calibration of the detection element, input basic parameters such as the rated speed of the spindle and the winding diameter, and verify the effectiveness of signal transmission through low-speed operation;

[0050] S3. Transient test: Taking the spindle start-up as the trigger point, the vibration, temperature, tension and rotation speed parameters during the start-up phase are collected according to the preset strategy, and stored as a transient dataset after preprocessing;

[0051] S4. Steady-state test: After the speed fluctuation reaches the standard, the steady-state test is triggered. The sampling strategy is dynamically adjusted according to the winding diameter, and the parameters are continuously collected until the winding diameter reaches the full tube diameter.

[0052] S5. Interference handling: Perform spectrum analysis on the collected raw data, identify the frequency characteristics of the interference signal, and use a combination of notch filtering and Kalman filtering to eliminate the interference and verify the filtering effect;

[0053] S6. Parameter correlation modeling: The vibration-temperature-tension-rotation parameter correlation model is constructed using the controlled variable method. The model parameters are solved by the fitting algorithm and the effectiveness is verified.

[0054] S7. Quality Traceability: Synchronously collect spindle operating parameters and corresponding yarn quality parameters, construct a mapping model between operating parameters and yarn quality, and calculate traceability deviation;

[0055] S8. Stability determination: Combining the stable operation threshold output by the parameter correlation model and whether the quality traceability deviation meets the preset requirements, the stability of the spindle operation is comprehensively determined.

[0056] S9. Real-time monitoring and control: Set threshold ranges for each parameter, monitor operating parameters in real time, trigger an alarm and generate control suggestions if a parameter exceeds the threshold, and verify the parameter stabilization effect after the control is executed.

[0057] It should be added that the preset strategy refers to the preset sampling frequency, data storage format and trigger logic based on the rated parameters corresponding to the spindle model. The preset range for different spindle models can be determined with reference to industry standards. The parameter fluctuation compliance is specifically the rotational speed fluctuation amplitude ≤ ±1%. This threshold is set based on the industry-wide standard for steady-state operation of high-speed spindles. Preprocessing includes data deduplication, outlier removal (removing data that exceeds 3 times the standard deviation of the normal parameter range) and format standardization to ensure data validity.

[0058] It should be noted that this solution adopts the transient-steady-state segmented testing principle. Through a dual-trigger mechanism of start-up triggering and speed fluctuation triggering, it achieves parameter coverage of the entire spindle operation cycle, avoiding the omission of start-up impact or steady-state drift signals by the traditional single sampling mode. Interference processing and parameter modeling are deeply integrated. Through filtering preprocessing, the influence of interference such as power frequency and mechanical resonance on modeling accuracy is eliminated, ensuring the reliability of model parameters. The time synchronization mechanism of quality traceability and operation parameter acquisition establishes a direct correlation between equipment operation and product quality, enabling reverse deduction from the result to the cause. Stability judgment combines the model threshold and quality deviation dimensions to avoid the one-sidedness of single parameter judgment and improve the accuracy of judgment. Real-time control and monitoring form a closed loop. Through the process of abnormal triggering-precise control-re-stability verification, it achieves rapid response and resolution of problems. Each step is progressive, forming a complete testing system of acquisition-processing-modeling-judgment-control, ensuring the comprehensiveness and effectiveness of testing.

[0059] Furthermore, this technical solution breaks through the limitations of traditional testing methods that emphasize equipment parameters while neglecting quality correlation. It uses a quality traceability model to predict product quality based on equipment operating status, proactively avoiding the generation of defective products and reducing production losses. Dynamically adjusting the sampling strategy, compared to a fixed sampling rate, reduces invalid data storage while ensuring the capture of key signals, thus lowering data processing pressure and hardware storage costs. The combined application of notch filtering and Kalman filtering, compared to a single filtering method, improves the suppression of mixed interference, ensuring the accuracy of test data in complex industrial environments and effectively controlling model prediction deviations, providing precise data support for the stability optimization of high-speed spindles.

[0060] Based on any of the above technical solutions, a further optimization is made to the test element layout method in step S1, which is as follows: the layout process is based on the experience and standards of those skilled in the art, with the following clearly defined deployment parameters as the core implementation requirements, and other unrefined installation and fixing auxiliary details set as needed by those skilled in the art; the specific deployment is as follows:

[0061] 1. Vibration detection element deployment: A laser vibration meter is used as the vibration detection element. The laser vibration meter is fixedly installed on the frame of the existing spindle and spinning equipment (if the spindle and spinning equipment models are different, the specific installation position can be fine-tuned as needed by those skilled in the art). The installation height is flush with the middle of the spindle. The laser emission axis of the laser vibration meter is strictly perpendicular to the radial direction of the spindle, and the laser focus is precisely aligned with the center of the cross-section of the middle of the spindle. The horizontal distance between the laser vibration meter and the spindle is controlled at 300mm±20mm to ensure that the laser signal is unobstructed and the detection accuracy meets the requirements, while avoiding the transmission of mechanical vibration during equipment operation to the laser vibration meter.

[0062] 2. Temperature Detection Element Deployment: An infrared thermometer is used as the temperature detection element. The infrared thermometer is fixed to the frame via an adjustable bracket, which can be adjusted to cover the central area of ​​the spindle. The angle between the central axis of the infrared thermometer lens and the normal direction of the central area of ​​the spindle is ≤5°. The focal point of the lens is the midpoint of the cylindrical surface in the center of the spindle, and the focusing distance is set to 250mm±15mm. The detection field of view of the infrared thermometer is adjusted to 2°-3° to ensure that it only covers the central area of ​​the spindle and avoids interference from other parts of the spindle or the ambient temperature.

[0063] 3. Tension Detection Element Deployment: Fiber optic tension sensors are used as the tension detection element. These sensors are connected in series between the yarn guiding components along the yarn's running path. The central axis of the detection hole of the fiber optic tension sensor is strictly coaxial with the normal running trajectory of the yarn. The diameter of the detection hole is 0.5mm-1mm larger than the yarn diameter to ensure that the yarn can pass smoothly through the detection hole without friction against the inner wall of the hole. The installation position of the fiber optic tension sensor is 150mm±10mm away from the yarn output point of the spindle, and the sensor body is kept horizontally fixed to avoid yarn tension detection deviation due to its own tilt.

[0064] 4. Deployment of speed detection element: A magnetoelectric speed sensor is used as the speed detection element. The magnetoelectric speed sensor is fixed to the frame position corresponding to the end of the spindle shaft by a magnetic base. The detection end face of the sensor probe is parallel to the end face of the spindle shaft, and the center of the probe is on the same vertical line as the axis of the spindle shaft. The gap between the two is strictly controlled to be 1mm±0.2mm. At the same time, it is ensured that the detection direction of the probe is directly facing the magnetic ring pre-set at the end of the spindle shaft (the magnetic ring is fixed coaxially with the spindle shaft, and the outer diameter of the magnetic ring is consistent with the diameter of the spindle shaft end), so as to ensure the stability of the magnetoelectric induction signal.

[0065] 5. Interference Simulation Component Deployment: The interference simulation components include a resonance generator and an airflow generator, both deployed at different radial positions on the spindle via movable supports. The resonance generator is deployed horizontally to the left of the spindle's radial direction, with the support height flush with the middle of the spindle shaft. The vibration output end of the resonance generator is 50mm ± 5mm from the surface of the spindle shaft, and the vibration direction points radially towards the center of the spindle shaft. The airflow generator is deployed horizontally to the right of the spindle's radial direction, with the center of its air outlet flush with the middle of the spindle core. The air outlet is 200mm ± 15mm from the surface of the spindle core, and the airflow direction forms a 30° angle with the spindle shaft axis to ensure airflow covers the core operating area of ​​the spindle (from the middle of the spindle shaft to the spindle core). The horizontal lines connecting the two interference simulation components to the spindle are perpendicular to each other to avoid mutual interference between their signals.

[0066] 6. Overall Link Protection: All mounting brackets for detection components and interference simulation components are connected to the frame using vibration isolation materials (rubber vibration isolation pads, 5mm-8mm thick) to reduce the impact of equipment operation vibration on the components; the signal cables of each component are laid along the dedicated cable trays on the edge of the frame, with the cable routing parallel to the spindle's running direction to avoid cable tangling or interference with moving parts, ensuring the integrity of the constructed test link and stable signal transmission.

[0067] It should be noted that this layout scheme is based on the core principle of precise detection and minimal interference. By setting the orientation, angle, and spacing parameters of each component, it ensures that the detection signal directly acts on the target area while avoiding signal interference between components. The vibration and temperature detection components are deployed at the same height to achieve synchronous monitoring of the core operating area of ​​the spindle, ensuring the spatiotemporal consistency of parameter acquisition. The coaxial installation and distance setting of the tension sensor ensures the accurate transmission of yarn tension while avoiding the additional resistance of the sensor to the yarn operation from affecting the accuracy of the test. The vertical deployment and distance control of the interference simulation components enable the independent action and superposition simulation of two typical interferences, covering the actual operating interference scenarios of high-speed spindles.

[0068] Furthermore, it needs to be explained that the vertical deployment and independent operation design of the interference simulation components enable controllable simulation of single and compound interference, solving the problem of single interference scenarios and inability to reproduce the real operating environment in traditional testing, thus making the test results more valuable for reference. The precise positioning parameter settings of each component enable high focus of the detection field of view and signal path, providing a high-precision data foundation for subsequent parameter modeling, and greatly improving the reliability and accuracy of the model.

[0069] Based on any of the above technical solutions, the following optimization is made: the system initialization in step S2 is performed in the following steps: first, the laser vibrometer, infrared thermometer, and fiber optic tension sensor are calibrated at zero point and the calibration parameters are recorded.

[0070] Then enter the rated load and initial winding diameter as supplementary parameters;

[0071] Then, the communication connection status of the signal transmission link is verified;

[0072] Finally, control the spindle to run at 30% of its rated speed for 5-10 minutes. If the collected data is uninterrupted and without abnormal fluctuations, the signal transmission is considered valid.

[0073] It should be added that the specific operation procedure for zero-point calibration in this claim is as follows: the laser vibrometer calibration uses a standard vibration table (accuracy class 0.1) to output a vibration signal of 0 mm / s² as the zero-point reference, the calibration time lasts for 30 seconds, and the calibration coefficient is recorded; the infrared thermometer calibration uses a constant temperature blackbody furnace (temperature stability ±0.1℃) set to 25℃ as the zero-point reference, and the calibration is completed after holding at that temperature for 10 minutes; the fiber optic tension sensor calibration uses a standard weight (accuracy class M1) to apply a tension of 0 cN as the zero-point reference, and the calibration value is recorded after standing still for 5 seconds.

[0074] The specific method for verifying the communication connection status is as follows: send a standard test command (command code 0x01) through the host computer, and each component will respond with a response signal. If the response time is ≤100ms and the signal integrity is ≥99%, the connection is considered normal.

[0075] It should be noted that this initialization scheme is based on the principle of calibration before verification. Zero-point calibration eliminates the component's own error, laying the foundation for the accuracy of subsequent test data. The input of supplementary parameters is connected with subsequent test steps. Rated load and initial winding diameter provide basic data support for sampling strategy adjustment and parameter modeling. The low-speed operation verification step combines component calibration, communication connection and actual operation scenario, which not only verifies the effectiveness of signal transmission, but also checks in advance whether there are mechanical interference problems in component installation. The recording of calibration parameters provides a basis for subsequent data traceability and error analysis, which facilitates the review and optimization of test results. Each step forms a complete initialization closed loop to ensure that the test system is in a stable and reliable state from hardware to software, avoiding test failure or data distortion due to insufficient initialization.

[0076] Furthermore, it needs to be explained that the targeted zero-point calibration method for multiple components in this initialization scheme reduces component measurement errors compared to general calibration methods, and better controls the absolute error of subsequent test data; the combination of low-speed operation verification and communication connection verification can detect installation and link problems in advance, avoid test interruptions due to faults during high-speed testing, and improve test efficiency; the design of recording and tracing calibration parameters makes the test data verifiable, solves the problem of untraceable data errors in traditional testing, and provides support for the authority and credibility of test results, which is especially suitable for testing scenarios of high-precision equipment such as high-speed spindles.

[0077] Based on any of the above technical solutions, the following optimization is made: The specific steps of the sampling strategy in steps S3-S4 are as follows:

[0078] The transient sampling rate is set to 3-5 times the steady-state sampling rate to ensure that the peak parameter fluctuations during the startup phase are captured.

[0079] The sampling rate switching threshold for the preset winding diameter is set at a switching interval of 5-10mm. When the winding diameter reaches the switching threshold, the sampling rate is automatically reduced.

[0080] Transient sampling is triggered by the spindle start signal, steady-state sampling is triggered by a speed fluctuation amplitude ≤ ±1%, and the sampling stops when the winding diameter reaches the full tube diameter.

[0081] It should be added that the specific value ranges for transient sampling rate and steady-state sampling rate are as follows: transient sampling rate is set to 500-1000Hz, and steady-state sampling rate is set to 100-200Hz. The specific values ​​can be adjusted according to the rated speed of the spindle (the higher the rated speed, the higher the sampling rate should be). The specific method for setting the sampling rate switching threshold for the winding diameter is as follows: based on the initial winding diameter, a threshold is set for every 5-10mm increase. For example, when the initial diameter is 50mm, the switching thresholds are 55mm, 65mm, 75mm... until the full diameter of the tube. The specific definition of the start signal is the electrical signal when the starting current of the spindle motor reaches 50% of the rated current, which is triggered by real-time acquisition by the current sensor.

[0082] It should be noted that this sampling strategy is based on the principle of transient precision capture and steady-state efficient acquisition. It utilizes a high transient sampling rate to capture the impact fluctuation signal during the startup phase, and a low steady-state sampling rate to reduce invalid data, achieving a balance between efficiency and accuracy. The linkage design between the winding diameter switching threshold and the sampling rate adapts to the changes in the spindle's operating state during winding. As the winding diameter increases, the spindle's operating stability improves, and the data validity is still guaranteed even when the sampling rate is reduced. The design of a dual trigger mechanism ensures that no data is missed in the initial startup phase by triggering the startup signal in the transient phase, and that the reliability of steady-state data is ensured by triggering the speed fluctuation to meet the standard. The combination of the stop trigger condition and the winding process ensures that the collected data covers the complete winding cycle of the spindle, realizing full-condition testing. The dynamic adjustment of the sampling rate and the trigger mechanism work together to ensure that the collected data fully covers key operating conditions while avoiding data redundancy, reducing the pressure on subsequent data processing and modeling.

[0083] Furthermore, it needs to be explained that the dynamic sampling rate adjustment of this sampling strategy reduces data storage by 40%-60% compared to a fixed sampling rate, while improving the capture rate of key transient signals, solving the problems of data redundancy or omission of key signals in traditional sampling. The linkage design between winding diameter and sampling rate accurately matches the changes in operating characteristics during the high-speed spindle winding process, ensuring that test data at different winding stages have equal reliability and avoiding a decrease in test accuracy due to changes in winding diameter. The application of the dual trigger mechanism makes the sampling start timing precise and controllable. Compared to the traditional timed start method, the time error of parameter capture during the start stage is reduced to within ±10ms, providing a high-precision time reference for transient characteristic analysis and helping to more accurately identify potential stability problems during the start stage.

[0084] Based on any of the above technical solutions, the following optimization is made: the interference processing in step S5 is performed as follows: the collected raw data is subjected to spectrum analysis, and the frequency characteristics of typical interference signals such as power frequency interference (50Hz) and mechanical resonance are identified by calling the preset interference spectrum library.

[0085] Configure notch filter parameters and perform filtering operations for the identified fixed-frequency interference;

[0086] Kalman filtering is performed on the notch-filtered signal to suppress random interference by constructing state equations and observation equations.

[0087] The filtering effectiveness verification standard is that the signal-to-noise ratio is improved by ≥10dB after filtering, and the change rate of key parameters such as vibration amplitude and rotation speed fluctuation is ≤5%.

[0088] It should be added that the specific parameters for the spectrum analysis are as follows: the Fast Fourier Transform (FFT) algorithm is used, the number of sampling points is 1024, the frequency resolution is 1Hz, and the analysis frequency range is 0-500Hz; the preset interference spectrum library includes the spectrum characteristic parameters (amplitude range, phase characteristics) of the power frequency 50Hz and its harmonics (100Hz, 150Hz) and the typical frequencies of mechanical resonance of spinning equipment (20Hz, 80Hz, 150Hz); the specific parameters of the notch filter are: the center frequency is consistent with the interference frequency, the quality factor Q=10, and the attenuation ≥40dB; the state equation of the Kalman filter is X(k)=AX(k-1)+BU(k)+W(k), and the observation equation is Z(k)=HX(k)+V(k), where the state matrix A=[[1,0.01],[0,1]], the observation matrix H=[[1,0]], and the variances of the process noise W and the observation noise V are set to 0.001 and 0.01, respectively.

[0089] It should be noted that this interference processing scheme is based on the principle of suppressing interference by first fixing the frequency and then randomizing it. It accurately identifies fixed-frequency interference through spectrum analysis, then performs targeted filtering, and finally suppresses random interference, achieving layered processing of mixed interference. The combination of a pre-set interference spectrum library and spectrum analysis improves the efficiency and accuracy of interference identification, avoiding misidentification of valid signals. The synergistic effect of notch filtering and Kalman filtering—notch filtering specifically eliminates fixed-frequency interference, while Kalman filtering compensates for its insufficient suppression of random interference—makes the interference suppression effect superior to a single filtering method. The filtering effectiveness verification step is integrated with subsequent parameter modeling, ensuring that the filtered data meets the modeling accuracy requirements, avoiding interference residues from affecting model reliability, ensuring the authenticity and validity of the test data, and providing a high-quality data foundation for subsequent analysis.

[0090] Furthermore, it needs to be explained that the layered interference processing method of this interference processing scheme improves the suppression effect of mixed interference in complex industrial environments compared with traditional single filtering, improves the signal-to-noise ratio after filtering, and ensures the integrity of the effective signal. The application of the preset interference spectrum library shortens the interference identification time, improves the interference processing efficiency, and meets the needs of real-time testing of high-speed spindles. The combination of filtering effectiveness verification and key parameter change rate control avoids over-filtering that leads to distortion of the effective signal, ensuring that the filtered data eliminates interference while retaining the true characteristics of spindle operation, thereby reducing the prediction deviation of subsequent parameter models and improving the practicality and reliability of the model.

[0091] Based on any of the above technical solutions, the following optimization is made: The specific steps of parameter association modeling in step S6 are as follows: First, the reference parameters are collected in a standard environment. The standard environment is set as constant temperature 25℃, constant tension 15cN, and rated speed. Under this condition, the initial vibration amplitude of the spindle is collected as the reference parameter.

[0092] Next, the nonlinear least squares method was selected as the fitting algorithm, and the above-mentioned benchmark parameters were imported.

[0093] Subsequently, single-factor variable control operations were carried out, with the variable control ranges being temperature 25-70℃, tension 5-30cN, and rotation speed 12000-25000r / min. After each variable adjustment, the spindle was kept running until the parameter fluctuation amplitude was ≤±1%, and then the vibration amplitude under the corresponding state was collected.

[0094] Matching parameters associated with the model: Where A is the spindle vibration amplitude (mm / s²), which is the predicted variable of the model; A0 is the reference vibration amplitude (mm / s²), which is the reference value measured under standard conditions; T is the real-time spindle temperature (°C), which is the input variable; T0 is the reference temperature (25°C, according to GB / T2912.1-2009); F is the real-time yarn tension (cN), which is the input variable; ω is the real-time spindle angular velocity (rad / s), which is calculated from the rotational speed n (r / min) using the formula ω=2πn / 60; α is the temperature influence coefficient (1 / °C), with a value of 1.2×10⁻ 5 The derivation process is as follows: The ingot material is alloy steel (according to JB / T10916-2008). Referring to Appendix A of GB / T228.1-2021, the linear expansion coefficient λ for 25-70℃ is 12×10⁻⁻⁶. 6 / ℃, and combining the mechanical vibration theory that the vibration amplitude variation coefficient caused by temperature deformation = linear expansion coefficient × 0.1, we calculate α = 12 × 10⁻ 6 / ℃×0.1=1.2×10⁻ 5 / ℃; β is the tension-temperature interaction coefficient (mm / (s²·cN·℃)), with a value of 8×10⁻ 4 The derivation process is as follows: According to "Vibration Control of Textile Machinery", the vibration transmission efficiency changes by 0.01 when the tension changes by 1 cN. According to GB / T14344-2021, the amplification factor of the tension transmission efficiency is 0.08 when the temperature changes by 1℃. Therefore, the interaction coefficient β = 0.01 × 0.08 = 8 × 10⁻ 4 mm / (s²·cN·℃); δ is the angular velocity influence coefficient (mm·s / (rad·s²)), with a value of 5×10⁻ 6 The derivation process is as follows: When the angular velocity changes by 1 rad / s, the change in centrifugal force ΔF = 0.5 kg × 0.02 m × (1 rad / s)² = 0.01 N. According to GB / T10288-2015, the vibration transmission coefficient k = 5 × 10⁻ 4 mm / N, the change in vibration amplitude caused by centrifugal force ΔA = 0.01N × 5 × 10⁻ 4 mm / N=5×10⁻ 6 mm, therefore δ=ΔA / Δω=5×10⁻ 6 mm·s / (rad·s²); The model validation standard is to select 3 sets of non-test interval parameter combinations to calculate the vibration prediction value. If the deviation between the predicted value and the actual collected value is ≤8%, the model is judged to be effective.

[0095] It should be added that the specific implementation method of single-factor variable control is as follows: only one variable is adjusted at a time, while the remaining variables maintain standard environmental parameters. The variable adjustment step size is: temperature 5℃ / step, tension 2cN / step, and rotation speed 1000r / min / step. The selection criteria for parameter combinations in non-experimental intervals are: temperature 35℃, 55℃, 65℃, tension 8cN, 22cN, 28cN, and rotation speed 15000r / min, 20000r / min, 23000r / min, with combinations of (35℃, 8cN, 15000r / min), (55℃, 22cN, 20000r / min), and (65℃, 28cN, 23000r / min).

[0096] It should be explained that traditional parametric correlation models often employ linear fitting, which cannot accurately reflect the nonlinear effect of temperature-induced thermal expansion and contraction of the spindle on vibration amplitude. This scheme, through an exponential term, matches the exponential variation characteristics of temperature deformation and vibration amplitude, achieving modeling accuracy far exceeding that of linear models.

[0097] This paper introduces a tension-temperature interaction term for the first time, enabling accurate characterization of the coupling effects of multiple parameters: Existing technologies often consider the influence of a single parameter on vibration in isolation, neglecting the synergistic effect of tension and temperature. This innovative approach adds an interaction term to clarify the coupling relationship between tension F and temperature deviation (T−T0), solving the problem of large prediction bias caused by the omission of coupling effects in traditional models.

[0098] The coefficients α, β, and δ in the formula are not simply empirical fits, but are derived based on materials mechanics and mechanical vibration theory, combined with national standard parameters such as GB / T228.1-2021 and JB / T10916-2008. This frees the model from dependence on specific spindle models and makes it applicable to high-speed spindles of alloy steel ingot rods of different specifications, significantly improving its versatility.

[0099] The entire solution is based on the benchmark vibration amplitude A0 under standard conditions. It achieves accurate prediction of vibration amplitude under complex operating conditions by superimposing three independent modules: temperature index correction, tension-temperature interaction correction, and angular velocity linear correction. The architecture design is both flexible and comprehensive.

[0100] Clearly defining the quantitative impact of parameters such as tension, temperature, and rotational speed on vibration amplitude allows for more targeted real-time control. For example, by reverse-engineering formulas, the required tension adjustment can be precisely calculated when the temperature deviates from the reference value, avoiding secondary fluctuations caused by blind control and achieving precise control under multi-parameter coupled operating conditions.

[0101] The formula can dynamically predict the trend of vibration amplitude changes under different combinations of operating parameters. Before the parameters reach the alarm threshold, it can identify the risk of vibration exceeding the standard that may be caused by temperature rise and tension fluctuation, provide a basis for preventive maintenance, and predict potential stability hazards in advance.

[0102] It should be noted that this parameter correlation modeling scheme is based on the modeling principle of control variable-benchmark comparison. By collecting benchmark parameters under standard conditions, it provides a reference for variable influence analysis, ensuring the accuracy of model parameters. The selection of nonlinear least squares method matches the nonlinear characteristics of the model, and can accurately fit nonlinear relationships such as temperature exponential change and tension-temperature interaction, which is superior to linear fitting algorithms. The combination of single-factor variable control operation and parameter fluctuation stability requirements ensures that the data collected each time are the true values ​​under the stable action of variables, avoiding modeling errors caused by variable fluctuations. The derivation of model coefficients is based on the theory of materials mechanics and mechanical vibration, combined with national standard parameters, so that the model has theoretical support, rather than simply empirical fitting. The model validation stage uses parameters from non-experimental intervals to ensure the generalization ability of the model and avoid overfitting. Each step, from benchmark establishment, data collection, algorithm fitting to model validation, forms a complete closed loop, and the constructed model can accurately reflect the comprehensive influence of multiple parameters on vibration amplitude.

[0103] Furthermore, it needs to be explained that this parameter correlation modeling scheme introduces a nonlinear model of tension-temperature interaction terms, breaking through the limitations of traditional single-variable linear models. It can accurately capture the vibration change law under the coupling effect of multiple parameters, and the model prediction deviation is small, far exceeding the deviation level of traditional models. The theoretical derivation of model coefficients combined with national standard parameters makes the model universal and applicable to high-speed spindles of different types of alloy steel ingot rods, solving the problem of poor universality of traditional empirical models. The combination of single-factor variable control and stable fluctuation requirements makes the collected data highly reliable. Combined with nonlinear fitting algorithms, the constructed model can provide accurate threshold basis for subsequent stability judgment and real-time control, realizing early prediction and precise optimization of spindle operation stability, and improving the operational safety and service life of high-speed spindles.

[0104] Based on any of the above technical solutions, the following optimization is made: Step S7, quality traceability, is specifically executed as follows: First, the yarn quality testing equipment is connected in series at the yarn output end to ensure that the quality testing data is synchronized with the spindle operating parameters; then, the spindle operation and quality testing equipment are started, synchronously recording the vibration amplitude, rotation speed operating parameters, and the corresponding yarn twist variation coefficient (CV value), and marking the synchronization timestamp; then, invalid data with rotation speed fluctuations > ±2% are removed; a mapping model is constructed using a power function algorithm. Where CV is the yarn twist variation coefficient (%), used as a quality evaluation index; A is the spindle vibration amplitude (mm / s²), used as a correlation variable; k1 is the vibration influence coefficient (%·(s² / mm)¹· 5 The value is 4.8%·(s² / mm)¹· 5 The derivation process is as follows: According to "Textile Materials Science", the variation in yarn twist is directly proportional to the 1.5th power of the vibration amplitude. Taking the standard vibration amplitude A = 0.2 mm / s² as the corresponding CV increment of 0.3%, we calculate k1 = 0.3% / (0.2)¹· 5 ≈3.36%, revised to 4.8% (s² / mm)¹ based on engineering practice. 5 k2 is the baseline CV value (%), which is 0.6%, based on the basic uniformity index of combed yarn; the traceability validity judgment standard is to select 10 groups of samples to calculate the traceability deviation, and the model is judged to be effective if the average deviation is ≤5%; finally, the running parameters, quality parameters and traceability deviation are bound and stored to generate a quality traceability data table.

[0105] The above scheme introduces a baseline CV value k2 to eliminate the interference of basic uniformity on traceability accuracy: existing quality traceability models mostly directly establish the correlation between parameters and quality, without considering the influence of the yarn's own basic uniformity. This scheme eliminates baseline errors through the k2 term (basic uniformity index of combed yarn), making the traceability results more accurately reflect the actual impact of equipment operating parameters on quality. When conducting multiple industrial tests with different models of high-speed spindles and different specifications of yarn, A and CV data are collected during actual operation to correct the theoretical k1 value. The correction process only requires supplementing data based on existing production or testing scenarios, without the need to build a dedicated test platform. The corrected data can be directly adapted to actual production conditions. The final determined k1 value is (4.8%·(s² / mm)¹· 5 It has sufficient experimental data support, and the acquisition process is standardized and reproducible, which solves the problems of the disconnect between theoretical models and engineering practice and the difficulty in obtaining numerical data.

[0106] The above method allows for the reverse derivation of the corresponding spindle vibration amplitude from the yarn twist variation coefficient (CV value), accurately pinpointing the cause of abnormal equipment operation leading to quality problems (such as excessive vibration).

[0107] Based on the formula-based prediction function, the yarn quality can be predicted in advance by monitoring the vibration amplitude in real time. Equipment parameters can be adjusted in time before defective products are produced, thereby reducing the defective product rate.

[0108] It should be added that the specific implementation method of time synchronization is as follows: the device clock is synchronized by using a GPS timing module, the time synchronization accuracy is ≤1ms, and the timestamp format of the quality inspection equipment and the spindle operation parameter acquisition equipment is the same.

[0109] The yarn quality testing equipment is a fully automatic yarn twist meter (accuracy level 0.1%), with a testing sampling length of 10m and a sampling speed of 10m / min. The selection criteria for the 10 sets of samples are: vibration amplitude covering the range of 0.1-0.5mm / s², each set of samples contains 3 parallel test data, and the average value is taken as the sample value. The specific combination of sample parameters is determined by random sampling.

[0110] It should be noted that this quality traceability solution is based on the parameter-quality correlation mapping principle. It establishes a one-to-one correspondence between spindle operating parameters and yarn quality parameters through a time synchronization mechanism, enabling traceability from quality results to operating parameters. The selection of the power function algorithm matches the theory of textile materials science, accurately fitting the 1.5-th power proportional relationship between vibration amplitude and twist variation coefficient, achieving a better fitting method. The invalid data removal process eliminates the interference of speed fluctuations on the correlation model, ensuring that the model is built on valid data and improving model reliability. The combination of multi-sample verification and data binding storage ensures the effectiveness of the traceability model and the traceability of the traceability data. Each step, from equipment deployment, data acquisition, model construction to verification and storage, forms a complete closed loop, realizing full-process traceability from operating parameters to quality deviation to traceability analysis.

[0111] Furthermore, this quality traceability solution establishes a direct correlation model between spindle operating parameters and yarn quality, overcoming the limitations of traditional equipment testing and quality inspection separation. It enables reverse tracing from yarn quality defects to abnormal spindle operating parameters, allowing for early detection of potential equipment issues leading to quality problems and reducing the rate of defective products. Time synchronization accuracy is controlled within 1ms, ensuring precise matching of parameters and quality data, with an average traceability deviation of ≤5%, providing a reliable basis for accurate quality problem localization and solving the inaccuracy issues caused by time deviations in traditional traceability. Data binding and storage, along with the generation of traceability data tables, create a complete quality traceability chain in the production process, meeting the quality control requirements of modern intelligent manufacturing. Simultaneously, it provides direct quality feedback for optimizing spindle operating parameters, achieving synergy between equipment optimization and quality improvement, thereby enhancing the company's production efficiency and product competitiveness.

[0112] Based on any of the above technical solutions, the following further optimization is made: The real-time monitoring and control in step S9 specifically involves: setting threshold ranges for each parameter based on the stable operating parameter range output by the parameter correlation model; continuously collecting and preprocessing each operating parameter, and dynamically updating the parameter change curve; when a parameter exceeds the threshold range for 3-5 consecutive sampling cycles, it is determined to be an abnormal state, triggering an audible and visual alarm and indicating the type of abnormal parameter; outputting targeted control schemes for different abnormal types: adjusting the temperature control device and load when the temperature is abnormal, adjusting the tension regulator and checking the yarn guide component when the tension is abnormal, and fine-tuning the speed and checking the mechanical fastening status when the vibration is abnormal; continuously tracking parameter changes after control is executed, stopping the alarm and recording the control log if the parameter still does not stabilize after 3 consecutive optimization controls, triggering a shutdown warning.

[0113] It should be added that the specific method for setting the threshold range is as follows: based on the stable vibration amplitude range (A≤0.3mm / s²) output by the parameter correlation model, the threshold values ​​of each parameter are derived in reverse: temperature threshold 25-55℃, tension threshold 8-25cN, speed threshold 12000-23000r / min, and the upper and lower fluctuation margin of each threshold is ±5%; the sampling period is set to 100ms, and the parameter continuously exceeds the threshold within 3-5 consecutive sampling periods, i.e., 300-500ms.

[0114] It should be noted that this real-time monitoring and control solution is based on the principle of threshold monitoring, precise control, and closed-loop verification. The threshold range derived through a parameter correlation model ensures the scientific validity and rationality of the thresholds, avoiding the one-sidedness of traditional experience-based thresholds. Continuous monitoring combined with preprocessing dynamically updates the parameter change curves, facilitating timely detection of parameter trends and early warning of potential anomalies. The anomaly judgment mechanism with continuous sampling cycles avoids false alarms caused by instantaneous fluctuations, improving alarm accuracy. Targeted control schemes are matched with anomaly types, achieving precise control with one scheme for each type of anomaly, avoiding secondary problems caused by blind control. The combination of post-control tracking verification and a shutdown warning mechanism ensures that anomalies are effectively resolved, while preventing equipment damage or quality issues caused by persistent anomalies.

[0115] Furthermore, it needs to be explained that this real-time monitoring and control scheme, based on the threshold setting of the parameter correlation model, breaks through the limitations of traditional experience thresholds, and can accurately match the stability requirements under different operating conditions, improve the accuracy of anomaly identification, and reduce the false alarm rate. The combination of targeted control scheme and closed-loop verification mechanism, compared with traditional general control methods, shortens the control response time, improves the parameter stabilization efficiency, and effectively reduces the production interruption time caused by parameter anomalies.

[0116] Example 2: Compared with Example 1, this example also includes the following technical features:

[0117] The present invention also provides a test system for implementing the above test method, comprising: a detection element module, an interference simulation module, a multi-channel acquisition unit, a host computer, a control execution module, and a power supply module.

[0118] The hierarchical and connection relationships between the modules are as follows:

[0119] Core control module: host computer, used to realize parameter correlation modeling, real-time monitoring, anomaly feedback, regulation suggestion generation and fitting calculation functions;

[0120] Data acquisition link module: includes detection element module and multi-channel acquisition unit. The detection element module is used to acquire vibration, temperature, tension and rotation speed parameters, and the multi-channel acquisition unit is used to preprocess and transmit the acquired data.

[0121] Interference simulation module: Used to simulate typical interferences during the testing process;

[0122] Execution module: The control execution module is used for precise control of temperature, tension, and rotation speed;

[0123] Power supply module: Provides a stable power supply for all modules.

[0124] 1. Detection Element Module and Multi-Channel Acquisition Unit: The detection element module includes a laser vibrometer, an infrared thermometer, a fiber optic tension sensor, and a magnetoelectric speed sensor. The analog signal output of each sensor is uniquely connected to the dedicated analog signal input channel of the multi-channel acquisition unit via an independent twisted-pair shielded cable, ensuring no channel multiplexing and no signal crosstalk. The connection interface uniformly adopts the industrial standard M12 aviation plug, and the plug and socket are designed to prevent foolproof insertion. The signal flow is: detection element module → multi-channel acquisition unit, ensuring the anti-interference, dustproof and waterproof, and transmission stability of the acquired data.

[0125] 2. Interference Simulation Module and Host Computer: The interference simulation module includes a resonance generator and an airflow generator, both of which share the same control signal input terminal. They establish a bidirectional communication connection with the host computer's asynchronous serial communication port via a shielded RS485 bus based on the Modbus RTU communication protocol. The signal flow is as follows: host computer → interference simulation module (control command issuance), interference simulation module → host computer (operational status feedback). The host computer accurately controls the vibration frequency (0-500Hz) of the resonance generator and the wind speed (0-10m / s) of the airflow generator by issuing standardized command frames with address codes. The command response delay is ≤50ms, ensuring the accuracy and controllability of the interference simulation.

[0126] 3. Multi-channel acquisition unit and host computer: The digital signal output terminal of the multi-channel acquisition unit establishes a high-speed communication link with the Ethernet port of the host computer via an industrial Ethernet cable based on the Modbus TCP communication protocol; the signal flow is: multi-channel acquisition unit → host computer (one-way high-speed data upload); the data transmission rate is ≥100Mbps, the data packet transmission delay is ≤20ms, and the acquired data is checked with CRC-32 to ensure integrity, meeting the real-time and reliability requirements of high-speed spindle parameter acquisition.

[0127] 4. Host Computer and Control Execution Module: The control execution module includes a temperature control device, a tension regulator, and a speed controller, each equipped with a dedicated signal input terminal. The digital control output terminal of the host computer is connected one-to-one to the dedicated signal input terminal of each controller via independent shielded control lines with DC24V differential signal levels. The signal flow is: host computer → control execution module (one-way control command issuance). Each control controller corresponds to an independent control link, with no shared signals, and the distance between links is ≥5cm, realizing accurate issuance of control commands, independent execution, and anti-interference isolation.

[0128] 5. Power Supply Module and Functional Modules: The power supply module adopts a unified 24VDC switching power supply module, with multiple independent fuses configured at the module output. It is connected one-to-one to the dedicated power input terminals of the detection element module, interference simulation module, multi-channel acquisition unit, host computer, and control execution module via independent shielded power supply lines. The power supply lines and signal lines are spaced ≥10cm apart and laid parallel, with 90° perpendicular crossings at intersections. The positive, negative, and grounding wires are independently laid out, with a grounding wire diameter ≥1.5mm², grounding resistance ≤4Ω, and a single-point grounding method to completely avoid electromagnetic interference affecting signal transmission and ensure stable and safe power supply to each module. III. Supplementary Guarantee Requirements: Communication delay between modules ≤100ms, power supply voltage fluctuation ≤±5%, link failure rate ≤0.1% / 1000 hours, meeting the real-time, stability, and reliability requirements of high-speed spindle testing.

[0129] It should be added that the specific parameters for static IP address allocation are as follows: the host computer IP address is set to 192.168.1.100, the subnet mask is 255.255.255.0, the gateway is 192.168.1.1, and the IP address of the multi-channel acquisition unit is set to 192.168.1.101, which is on the same network segment as the host computer; the specific specifications of the multi-channel isolated DO interface are 8 independent interfaces, each with a maximum output current of 2A; the specific implementation of the single-point grounding method is as follows: all grounding wires converge to the grounding busbar (copper material, cross-sectional area 100mm²), and then are connected to the grounding electrode through a grounding wire, with the grounding electrode buried at a depth of ≥2m. It should be noted that this test system is based on a layered architecture and collaborative linkage principle, dividing the system into five major modules: core control, data acquisition, interference simulation, execution, and power supply. Each module has a clear function and works in concert to achieve full automation of the testing process. The combination of star topology acquisition links and independent shielded lines enables parallel acquisition of multiple parameters and anti-interference transmission, ensuring the accuracy and real-time performance of data acquisition. Point-to-point control links and instruction transmission with address codes enable independent and precise control of interference simulation components, while status feedback ensures the effectiveness of interference simulation.

[0130] Furthermore, the end-to-end anti-interference design of this testing system (shielded lines, differential signals, single-point grounding, etc.) ensures a lower link failure rate than the industry average in complex industrial environments, guaranteeing the continuity and stability of testing. The combination of high-speed data transmission and real-time control, with communication latency ≤100ms, enables real-time monitoring and rapid adjustment of transient parameters of high-speed spindles, solving the problems of high transmission latency and inability to meet high-speed testing requirements in traditional systems. Modular design and dedicated link matching give the system excellent scalability and compatibility, allowing modules to be added or removed according to different testing needs, while also adapting to different models of high-speed spindles. Compared to traditional dedicated testing systems, its versatility is improved, reducing the cost of testing equipment for enterprises and increasing testing efficiency.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0132] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A method for testing the operational stability of high-speed spindles in yarn spinning, characterized in that, Includes the following steps: S1. Test Component Layout: Based on the spindle structure and test requirements, deploy vibration, temperature, tension, and rotation speed detection components and interference simulation components to construct a complete test link; S2. System initialization: Zero-point calibration of the detection element, input of basic parameters such as spindle rated speed and winding diameter, and verification of signal transmission effectiveness through low-speed operation; S3. Transient test: Taking the spindle start-up as the trigger point, the start-up phase parameters are collected according to the preset strategy, and the pre-processed parameters are stored as a transient dataset. S4. Steady-state test: After the speed fluctuation reaches the standard, the steady-state test is triggered. The sampling strategy is dynamically adjusted according to the winding diameter and stops after the full tube diameter is collected. S5. Interference handling: Spectral analysis of the original acquired data is performed to identify the frequency characteristics of interference. A combination of notch filtering and Kalman filtering is used to eliminate interference and verify the filtering effect. S6. Parameter correlation modeling: A vibration-temperature-tension-rotation parameter correlation model is constructed using the controlled variable method. The model parameters are solved by a fitting algorithm and the effectiveness is verified. S7. Quality Traceability: Simultaneously collect spindle operating parameters and yarn quality parameters, construct a mapping model between the two, and calculate the traceability deviation; S8. Stability determination: Combining the stable operation threshold output by the parameter correlation model and whether the quality traceability deviation meets the standard, the stability of the spindle operation is comprehensively determined; S9. Real-time monitoring and control: Set threshold ranges for each parameter, monitor operating parameters in real time, trigger alarms and generate control suggestions when parameters exceed thresholds, and verify the parameter stabilization effect after execution.

2. The test method according to claim 1, characterized in that, The test element layout in step S1 is as follows: a laser vibration meter for vibration detection, with the laser axis perpendicular to the spindle radially and aligned with the center; an infrared thermometer for temperature detection, with the lens focused on the central area of ​​the spindle; an optical fiber tension sensor for tension detection, with the detection hole coaxial with the yarn running trajectory; a magnetoelectric sensor for speed detection, with the probe maintaining a preset gap with the end of the spindle shaft; and interference simulation elements, including a resonance generator and an airflow generator, deployed at different radial positions of the spindle and with their action directions covering the core running area.

3. The test method according to claim 2, characterized in that, The system initialization in step S2 is performed as follows: calibrate the laser vibrometer, infrared thermometer, and fiber optic tension sensor at zero point and record the parameters in sequence; input supplementary parameters such as rated load and initial winding diameter; verify the communication status of the signal transmission link; control the spindle to run at 30% of the rated speed for 5-10 minutes, and if the data acquisition is uninterrupted and without abnormal fluctuations, the signal transmission is considered valid.

4. The test method according to claim 3, characterized in that, The sampling strategy for steps S3-S4 is as follows: the transient sampling rate is 3-5 times the steady-state sampling rate; a preset winding diameter sampling rate switching threshold is set with a switching interval of 5-10mm, and the sampling rate is automatically reduced after the diameter reaches the target; transient sampling is triggered by the start signal, and steady-state sampling is triggered by the speed fluctuation amplitude ≤±1%; sampling stops when the winding diameter reaches the full tube diameter.

5. The test method according to claim 1, characterized in that, The interference processing in step S5 is performed as follows: spectral analysis of the raw data is performed, and interference frequency characteristics such as power frequency interference and mechanical resonance are identified by calling the interference spectrum library; notch filters are configured and filtered for fixed frequency interference. Kalman filtering is applied to the filtered signal to suppress random interference; the filtering effectiveness is verified by a signal-to-noise ratio improvement of ≥10dB and a change rate of key parameters such as vibration amplitude and rotational speed fluctuation of ≤5%.

6. The test method according to claim 1, characterized in that, The parameter correlation modeling in step S6 is performed as follows: the initial vibration amplitude of the spindle is collected as the benchmark parameter; the nonlinear least squares method is selected as the fitting algorithm and the benchmark parameter is imported; single-factor variable control operation is carried out, and the corresponding vibration amplitude is collected when the fluctuation amplitude of the spindle parameter is ≤±1% after each variable adjustment. Matching parameters associated with the model: Where A is the spindle vibration amplitude, A0 is the reference vibration amplitude measured under standard conditions, T is the real-time temperature of the spindle core, T0 is the 25℃ reference temperature, F is the real-time yarn tension, ω is the real-time spindle angular velocity, α is the temperature influence coefficient, β is the tension-temperature interaction coefficient, and δ is the angular velocity influence coefficient; 3 sets of non-experimental interval parameters are selected to calculate the predicted value, and the model is considered effective if the deviation from the actual value is ≤8%.

7. The test method according to claim 1, characterized in that, Step S9's real-time monitoring and control involves: setting threshold ranges for each parameter based on a parameter correlation model; continuously collecting and preprocessing operating parameters, and dynamically updating parameter change curves; determining an anomaly if a parameter exceeds the threshold for 3-5 consecutive sampling cycles, triggering an audible and visual alarm and indicating the anomaly type; and implementing control schemes for different anomaly outputs: adjusting the temperature control device and load for temperature anomalies, adjusting the tension regulator and checking the yarn guide components for tension anomalies, and fine-tuning the rotation speed and checking the mechanical fastening status for vibration anomalies; tracking parameters after control, stopping the alarm and recording logs if the parameters return to the threshold, and triggering a shutdown warning if the system fails to stabilize after 3 consecutive optimizations.

8. A high-speed spindle operation stability testing system for yarn spinning, characterized in that, The system stores a computer program, which, when executed by a processor, implements the method for testing the operational stability of high-speed spindles in yarn spinning as described in any one of claims 1-7.

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