A tension detection method and system for yarn conveying

Abstract

This invention belongs to the technical field of data processing methods, specifically relating to a tension detection method and system for yarn conveying, to solve the technical problem of low accuracy in existing detection methods. The method includes the following steps: S1, acquiring multimodal vibration data of the yarn within a preset detection area, including time-series images of yarn vibration displacement acquired by a line-scan camera and vibration acoustic signals acquired by an acoustic sensor; S2, constructing a spatiotemporal joint feature vector characterizing the dynamic properties of the yarn vibration; S3, separating the pure yarn vibration characteristics after removing the influence of background vibration from the equipment; S4, determining the instantaneous tension of the yarn by solving the inverse problem of the nonlinear wave equation coupling tension, bending moment, and air damping. By employing a line-scan camera and an acoustic sensor, multimodal and multidimensional information acquisition of yarn vibration is achieved, and a spatiotemporal joint feature vector is constructed, enabling a more comprehensive and accurate characterization of the complex vibration behavior of the yarn.

Description

Technical Field

[0001] This invention belongs to the technical field of data processing methods, specifically relating to a tension detection method and system for yarn conveying. Background Technology

[0002] In the yarn processing, winding, and weaving stages of the textile industry, yarn tension is a crucial process parameter. Precise and real-time monitoring and control of yarn tension are fundamental to ensuring final product quality, reducing yarn breaks, improving production efficiency, and achieving automated production. Uneven tension can lead to defects such as weft skew, streaks, and uneven fabric surfaces, while excessive tension can easily cause yarn breakage, affecting production continuity. Therefore, developing a high-precision, high-reliability online yarn tension detection method is of significant practical importance.

[0003] Currently, yarn tension detection methods are mainly divided into two categories: contact and non-contact. Traditional contact methods, such as three-roller tension sensors, indirectly reflect tension by measuring the pressure on the guide rollers as the yarn passes over them. While this method is technically mature, its drawbacks are also significant: the sensor's direct contact with the high-speed moving yarn generates additional friction, which not only interferes with the tension measurement itself but may also damage the yarn surface, making it particularly unsuitable for high-count, high-density, or fragile specialty fibers. Furthermore, wear and inertia of mechanical components limit its response speed and long-term stability. To overcome these shortcomings, non-contact detection methods based on vibration principles have emerged. This method simplifies the yarn as a vibrating string; according to string vibration theory, its natural frequency is proportional to the square root of the tension value. By measuring the yarn's vibration frequency, its tension can be deduced. However, existing non-contact vibration methods still face significant challenges in practical applications. On the one hand, the textile workshop environment is complex, with equipment such as motors, spindles, and guide rails generating strong background vibrations and noise. These interference signals couple with the weak yarn vibration signals, resulting in an extremely low signal-to-noise ratio, making it difficult to accurately extract the pure yarn intrinsic frequencies, thus severely affecting measurement accuracy. On the other hand, traditional vibration models often idealize yarn as a stiff, linearly elastic flexible string, ignoring the yarn's own bending stiffness, air damping effect, and material nonlinearity. This oversimplification leads to significant deviations between the theoretical model and actual working conditions, especially under high speed and high tension, where the errors are even more pronounced. Furthermore, single-modal sensing methods (such as using only laser displacement sensors or microphones) acquire limited information dimensions, making it difficult to comprehensively characterize the complex spatiotemporal vibration behavior of yarn, thus limiting further improvements in detection accuracy. Summary of the Invention

[0004] This invention provides a tension detection method and system for yarn conveying to solve the technical problem of low accuracy in existing detection methods.

[0005] To solve the above problems, the tension detection method for yarn conveying provided by the present invention adopts the following technical solution: A tension detection method for yarn conveying, comprising the following steps:

[0006] S1, Collect multimodal vibration data of yarn within a preset detection area. The multimodal vibration data includes time-series images of yarn vibration displacement acquired by a line scan camera and vibration acoustic signals acquired by an acoustic sensor.

[0007] S2, based on the time-series image of yarn vibration displacement, uses the optical flow method to calculate the velocity field of at least two spatially separated measurement points on the yarn, and combines the spectral analysis results of the vibration acoustic signal to construct a spatiotemporal joint feature vector characterizing the dynamic properties of yarn vibration;

[0008] S3. Using a systemic interference model of equipment related to yarn conveying speed, the spatiotemporal joint feature vector is decoupled and calculated to separate the pure yarn vibration characteristics that eliminate the influence of equipment background vibration.

[0009] S4, based on the vibration characteristics of pure yarn and combined with the preset constitutive parameters of the yarn, determines the instantaneous tension of the yarn by solving the inverse problem of the nonlinear wave equation coupled with tension, bending moment and air damping. The preset constitutive parameters include the linear density, bending stiffness and nonlinear elastic coefficient of the yarn.

[0010] Furthermore, in S1, a line scan camera is used to capture images at a frame rate capable of capturing the main vibration modes of the yarn; acoustic sensors are deployed within a preset detection area so that they can pick up the vibrational acoustic signals generated by the yarn vibration.

[0011] Furthermore, in S1, the line array camera is arranged vertically and directly opposite the yarn.

[0012] Furthermore, in S2, multiple measuring points distributed along the yarn conveying path are selected in the vibration displacement time sequence image, and optical flow algorithms are used to calculate the vibration velocity time sequence of each measuring point in the direction perpendicular to the yarn conveying direction.

[0013] Furthermore, in S2, the vibration acoustic signal is subjected to spectral analysis to extract one or more main frequency features that characterize its main energy distribution; the main frequency features are fused with the vibration velocity time sequence features of multiple measurement points to form a spatiotemporal joint feature vector.

[0014] Furthermore, in S3, during the decoupling calculation, the systemic disturbance model of the equipment is constructed as a state-space model. The parameters of this state-space model are determined by calibrating the vibration characteristics of the equipment when it is running unloaded at a specific conveying speed. A filtering-based state estimation algorithm is adopted, and the spatiotemporal joint feature vector is used as the observation input to estimate and separate the vibration characteristics of the pure yarn.

[0015] Furthermore, in S4, the nonlinear wave equation takes into account the bending stiffness of the yarn itself and the air damping effect during motion.

[0016] Furthermore, in S4, an objective function is constructed with the instantaneous tension value of the yarn as the parameter to be optimized and the goal of minimizing the error between the vibration characteristics of the pure yarn and the vibration characteristics predicted by the nonlinear wave equation. An iterative optimization algorithm is used to solve the optimization problem and obtain the instantaneous tension value.

[0017] Furthermore, the error between the vibration characteristics of the pure yarn and the vibration characteristics predicted by the nonlinear wave equation is the root mean square error.

[0018] The present invention also provides a tension detection system for yarn conveying, including a memory and a processor. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, the tension detection method for yarn conveying described above is implemented.

[0019] The beneficial effects are as follows: Compared with existing technologies, this invention, by jointly employing a linear array camera and an acoustic sensor, achieves multimodal and multidimensional information acquisition of yarn vibration and constructs a spatiotemporal joint feature vector. This enables a more comprehensive and accurate characterization of the complex vibration behavior of yarn, overcoming the shortcomings of insufficient information dimensions in single sensing methods. Simultaneously, by introducing a systematic interference model of the equipment and performing decoupled calculations, interference from strong background vibration noise can be effectively eliminated, extracting pure yarn vibration characteristics and significantly improving the anti-interference capability and reliability of the detection in complex industrial environments. Furthermore, the inverse problem is solved using a nonlinear wave equation coupling yarn bending stiffness, air damping, and nonlinear elastic coefficients, which more closely approximates the true physical state of yarn at high speeds. This fundamentally ensures the physical authenticity and accuracy of the tension calculation results, solving the technical problem of large measurement errors caused by model simplification in existing methods. Attached Figure Description

[0020] Figure 1 A flowchart for a tension detection method for yarn feeding;

[0021] Figure 2 This is a structural block diagram of a tension detection system for yarn feeding. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Those skilled in the art should understand that the embodiments described below are only some, not all, of the embodiments disclosed. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] An embodiment of the tension detection method for yarn conveying provided by the present invention:

[0024] like Figure 1 As shown, the tension detection method for yarn feeding includes the following steps:

[0025] S1, collect multimodal vibration data of the yarn within a preset detection area. The multimodal vibration data includes time-series images of yarn vibration displacement acquired by a line scan camera and vibration acoustic signals acquired by an acoustic sensor.

[0026] Specifically, a line scan camera is deployed vertically facing the yarn's movement path to continuously capture the vibration profile of the yarn within a fixed length segment at a frame rate of over 1,000 frames per second, forming a two-dimensional time-series image of the yarn vibration displacement with grayscale values ​​varying over time. Simultaneously, a high-sensitivity microelectromechanical system (MEMS) microphone is non-contactly placed near the detection area to synchronously acquire the vibration acoustic signals radiated into the surrounding air during yarn vibration at a sampling rate of 48 kHz.

[0027] In an optional embodiment, a line scan camera is used to capture images at a frame rate that can capture the main vibration modes of the yarn; an acoustic sensor is deployed in a preset detection area so that the acoustic sensor can pick up the vibration acoustic signal generated by the yarn vibration.

[0028] Specifically, to clearly capture the vibration patterns of the yarn during high-speed motion, a high-frequency line scan camera (such as the Racer series from Basler, Germany) was selected, with its acquisition frame rate set to be no less than 2000Hz. Considering that the main vibration frequencies of the yarn are usually distributed in the range of several hundred Hz to 1000Hz, according to the Nyquist theorem, an acquisition frame rate of 2000Hz is sufficient to avoid signal aliasing and ensure that the vibration waveforms of each point on the yarn can be reconstructed without distortion, thus providing a high-quality original image sequence for subsequent velocity field calculations.

[0029] The acoustic sensor is a high-sensitivity microelectromechanical system (MEMS) microphone capable of acquiring vibratory acoustic signals coupled to yarn vibrations, which supplement visual information. The acoustic sensor is installed approximately 5 cm from the average yarn path, with its pickup direction directly facing the center of the area of ​​maximum yarn vibration amplitude. This distance ensures sufficient reception of the sound pressure signal generated by yarn vibrations while effectively avoiding airflow noise interference caused by the high-speed yarn movement itself, thereby maximizing the signal-to-noise ratio and capturing vibratory acoustic signals closely related to tension fluctuations.

[0030] S2, based on the time-series image of yarn vibration displacement, uses the optical flow method to calculate the velocity field of at least two spatially separated measurement points on the yarn, and combines the spectral analysis results of the vibration acoustic signal to construct a spatiotemporal joint feature vector characterizing the dynamic properties of yarn vibration.

[0031] In the time-series image of yarn vibration displacement, two fixed columns of pixels representing the yarn position are selected as measurement points. The Lucas-Cannard optical flow estimation algorithm is applied to calculate the pixel velocity at each measurement point perpendicular to the yarn axis, resulting in two sets of pixel velocity time series. A short-time Fourier transform is performed on the synchronously acquired vibratory acoustic signal to extract the main harmonic frequencies and their energy amplitudes from its time-frequency spectrum. Finally, the instantaneous velocity values ​​of the two measurement points are concatenated with the main harmonic frequencies and energy amplitudes of the vibratory acoustic signal to form a spatiotemporal joint feature vector containing both velocity and frequency domain information.

[0032] In an optional embodiment, when calculating the velocity field of at least two spatially separated measuring points on the yarn, multiple measuring points distributed along the yarn transport path are selected in the yarn vibration displacement time series image, and an optical flow algorithm is used to calculate the vibration velocity time series of each measuring point in the direction perpendicular to the yarn transport direction.

[0033] Specifically, in the continuous image frame sequence obtained by the linear array camera, the yarn image is divided into four equal regions along its length, and the center position of each region, namely the 256th, 512th, 768th, and 1024th pixel columns along the yarn path, is set as a fixed measurement point. Selecting multiple spatially separated measurement points, such as these four points, aims to observe the propagation characteristics of vibration waves on the yarn, rather than just the vibration state at a single point. This provides rich spatial information for a more accurate subsequent inverse solution of the physical model parameters.

[0034] For each selected measurement point, the Fanebach algorithm from dense optical flow algorithms is used to calculate its velocity perpendicular to the yarn transport direction. The pixel's motion vector is estimated by analyzing the grayscale changes of pixels in the neighborhood of each measurement point between two consecutive image frames, such as frame t and frame t+1. Since the yarn primarily vibrates perpendicular to the yarn transport direction, the vertical component of this motion vector is extracted and converted into a physical velocity in m / s based on the pixel size and frame rate of the line scan camera. This process is repeated for all image frames to obtain a complete data sequence of the vibration velocity variation over time at each measurement point.

[0035] In an optional embodiment, when constructing the spatiotemporal joint feature vector, the vibration acoustic signal is subjected to spectral analysis to extract one or more main frequency features that characterize its main energy distribution; the main frequency features are fused with the vibration velocity time-series features of multiple measuring points to form the spatiotemporal joint feature vector.

[0036] Specifically, the raw vibration acoustic signals acquired by the acoustic sensors are preprocessed, and then time-frequency analysis is performed using the short-time Fourier transform method. The signal is divided into segments with a window length of 100ms and a step size of 50ms, and the power spectral density of each segment is calculated. In the obtained spectrum, the two frequency peaks with the highest energy are identified; for example, at a certain moment, the dominant frequency is identified as 450Hz and the secondary dominant frequency as 910Hz. These two frequency values ​​serve as the acoustic characteristics within that time window, reflecting the most significant resonance mode of the yarn at that moment.

[0037] Within the same time window, statistical features are extracted from the temporal characteristics of vibration velocity at four measuring points. For example, the root mean square value and kurtosis coefficient of the vibration velocity signal at each measuring point are calculated, resulting in a total of eight visual features. Then, the two acoustic features extracted in the previous step are concatenated with these eight visual features to form a ten-dimensional vector. This vector is the spatiotemporal joint feature vector at that moment, which integrates the frequency domain information of the vibration acoustic signal and the temporal and spatial distribution information of the visual signal. Compared with a single mode, it can more comprehensively characterize the complex vibration state of the yarn.

[0038] S3 utilizes a systemic interference model of equipment associated with yarn conveying speed to decouple the spatiotemporal joint feature vectors, thereby separating out the pure yarn vibration characteristics that eliminate the influence of equipment background vibration.

[0039] The equipment systemic interference model is a blind source separation matrix established based on the independent component analysis algorithm. When the equipment is unloaded or running a reference non-vibrating yarn, background vibration data at different conveying speeds are collected, and a spatiotemporal joint feature vector is constructed. A demixing matrix capable of separating background vibration sources is trained using a fast independent component analysis algorithm. During actual detection, the real-time measured spatiotemporal joint feature vector is multiplied by this demixing matrix to separate the independent components representing the pure yarn vibration from the mixed signal, reconstructing the pure yarn vibration characteristics.

[0040] During decoupling calculations, the systemic disturbance model of the equipment is constructed as a state-space model. The parameters of this model are determined by calibrating the vibration characteristics of the equipment when it is running unloaded at a specific conveying speed. A filtering-based state estimation algorithm is used, with the spatiotemporal joint feature vector as the observation input, from which the vibration characteristics of the pure yarn are estimated and separated.

[0041] Specifically, systemic interference in the equipment mainly originates from rotating components such as motors, bearings, and guide wheels, whose vibrations exhibit strong periodicity. Without threading yarn, the textile equipment is allowed to... The system operates at standard production speed while simultaneously collecting vibration signals from the equipment frame and key components using accelerometers and line scan cameras. Spectral analysis of these idle signals identifies the main interference frequencies, such as 50Hz motor power frequency interference and 180Hz guide wheel rotation frequency interference. Based on this, a second-order state-space model describing the linear superposition of these interference sources is constructed, and the model parameters are determined.

[0042] An extended Kalman filter (EPF) algorithm is employed to decouple the interference signal from the yarn signal. The systemic interference model of the equipment constructed in the previous step serves as a prediction model to predict the interference state at the next moment. The aforementioned ten-dimensional spatiotemporal joint feature vector is then input into the EPF as the observation vector. Through iterative prediction and update steps, the EPF compares and corrects the observed spatiotemporal joint feature vector with the interference predicted by the model, thereby optimally estimating the contribution of equipment interference and subtracting it from the original observations. Finally, it outputs a feature vector containing only pure yarn vibration information.

[0043] S4, based on the vibration characteristics of pure yarn and combined with the preset constitutive parameters of the yarn, determines the instantaneous tension of the yarn by solving the inverse problem of the nonlinear wave equation coupled with tension, bending moment and air damping. The preset constitutive parameters include the linear density, bending stiffness and nonlinear elastic coefficient of the yarn.

[0044] A fourth-order partial differential equation based on Euler-Bernoulli beam theory, incorporating nonlinear tension and linear air damping terms, is established as the yarn vibration model. This fourth-order partial differential equation is discretized using the finite difference method to construct a forward model, which can predict the vibration frequency and amplitude of the yarn based on a given tension value. Finally, using the measured vibration characteristics of the pure yarn as the objective, the Levenberg-Marquardt optimization algorithm is employed. By iteratively adjusting the tension value input to the forward model, the sum of squared residuals between the model-predicted vibration characteristics and the measured characteristics is minimized. When the residuals converge, the corresponding tension value is the final determined instantaneous yarn tension.

[0045] In an alternative embodiment, the nonlinear wave equation takes into account the bending stiffness of the yarn itself and the air damping effect during motion.

[0046] Specifically, the traditional ideal flexible string model neglects the bending stiffness of the yarn, which introduces significant errors when dealing with high-twist or high-density yarns. Therefore, a term proportional to the fourth-order partial derivative of the displacement with respect to spatial coordinates is added to the nonlinear wave equation, i.e. ,in It is the Young's modulus of the yarn. It is its moment of inertia. It is the fourth-order partial derivative of displacement with respect to spatial coordinates. For example, for a 150 denier polyester industrial yarn, its bending stiffness... The value is approximately The introduction of this term enables the model to more accurately describe the mechanical behavior of yarn under high-frequency vibration or small radius of curvature.

[0047] When yarn moves and vibrates at high speed in the air, it experiences viscous drag and disturbance drag, which dissipates vibrational energy and causes vibration decay. To quantify this effect, a damping term proportional to the first-order partial derivative of the yarn's vibration velocity (displacement) with respect to time is added to the nonlinear wave equation. ,in It is the air damping coefficient. This is the first-order partial derivative of displacement with respect to time. It is calibrated through wind tunnel experiments or computational fluid dynamics simulations. At the conveying speed, this damping coefficient The value is approximately By incorporating air damping, the model's predictions can better match the amplitude attenuation characteristics of the measured vibration signal.

[0048] In an optional embodiment, when solving the inverse problem, an objective function is constructed with the instantaneous tension of the yarn as the parameter to be optimized and the goal of minimizing the error between the vibration characteristics of the pure yarn and the vibration characteristics predicted by the nonlinear wave equation as the objective; and an iterative optimization algorithm is used to solve the optimization problem to obtain the instantaneous tension value.

[0049] Specifically, solving the inverse problem is constructed as an optimization task. The objective function is defined as the root mean square error between the measured pure vibration characteristics and the vibration characteristics predicted by the physical model. Specifically, the root mean square error between the pure vibration velocity time series of four decoupled measurement points and the predicted velocity time series of four corresponding measurement points obtained by solving the nonlinear wave equation using numerical methods such as the finite difference method under the assumption of a given instantaneous tension T is calculated. This root mean square error is a function of the parameter T to be optimized.

[0050] To solve this optimization problem, the Gauss-Newton iterative algorithm is employed. An initial guess of the tension is required, for example, empirically set at 15 cN. In each iteration, the Jacobian matrix of the objective function is calculated based on the current tension value, and then a linear least squares problem is solved to obtain the updated tension value. For example, after the first iteration, the tension value might be updated to 15.3 cN. This iterative process continues until the difference between the tension values ​​calculated in two consecutive iterations is less than a preset convergence threshold, such as 0.01 cN, or the decrease in the objective function is less than a minimum value. At this point, the tension value is considered the optimal estimate of the instantaneous tension of the yarn at the current moment.

[0051] Embodiments of the tension detection system for yarn conveying provided by the present invention:

[0052] like Figure 2 As shown, the yarn conveying tension detection system includes a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement the above-described yarn conveying tension detection method.

[0053] The tension detection system for yarn conveying also includes other components well known to those skilled in the art, such as communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0054] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions stored or otherwise maintained by such a computer-readable medium.

[0055] In addition, in the description of this specification, "multiple" means at least two, such as two, three or more, etc., unless otherwise expressly and specifically defined.

Claims

1. A tension detection method for yarn conveying, characterized in that, Includes the following steps: S1, Collect multimodal vibration data of yarn within a preset detection area. The multimodal vibration data includes time-series images of yarn vibration displacement acquired by a line scan camera and vibration acoustic signals acquired by an acoustic sensor. S2, based on the time-series image of yarn vibration displacement, uses the optical flow method to calculate the velocity field of at least two spatially separated measurement points on the yarn, and combines the spectral analysis results of the vibration acoustic signal to construct a spatiotemporal joint feature vector characterizing the dynamic properties of yarn vibration; S3. Using a systemic interference model of equipment related to yarn conveying speed, the spatiotemporal joint feature vector is decoupled and calculated to separate the pure yarn vibration characteristics that eliminate the influence of equipment background vibration. S4, based on the vibration characteristics of pure yarn and combined with the preset constitutive parameters of the yarn, determines the instantaneous tension of the yarn by solving the inverse problem of the nonlinear wave equation coupled with tension, bending moment and air damping. The preset constitutive parameters include the linear density, bending stiffness and nonlinear elastic coefficient of the yarn. In S3, during decoupling calculations, the systemic disturbance model of the equipment is constructed as a state-space model. The parameters of this state-space model are determined by calibrating the vibration characteristics of the equipment when it is running unloaded at a specific conveying speed. A filtering-based state estimation algorithm is used, with the spatiotemporal joint feature vector as the observation input, from which the vibration characteristics of the pure yarn are estimated and separated. In S4, an objective function is constructed with the instantaneous tension value of the yarn as the parameter to be optimized and the goal of minimizing the error between the vibration characteristics of the pure yarn and the vibration characteristics predicted by the nonlinear wave equation. An iterative optimization algorithm is used to solve the optimization problem and obtain the instantaneous tension value.

2. The tension detection method for yarn conveying according to claim 1, characterized in that, In S1, a line scan camera is used to capture images at a frame rate that can capture the main vibration modes of the yarn; an acoustic sensor is placed in a preset detection area so that the acoustic sensor can pick up the vibration acoustic signal generated by the yarn vibration.

3. The tension detection method for yarn conveying according to claim 2, characterized in that, In S1, the line array camera is positioned vertically opposite the yarn.

4. The tension detection method for yarn conveying according to claim 1, characterized in that, In S2, multiple measuring points distributed along the yarn conveying path are selected in the vibration displacement time sequence image, and optical flow algorithms are used to calculate the vibration velocity time sequence of each measuring point in the direction perpendicular to the yarn conveying direction.

5. The tension detection method for yarn conveying according to claim 3, characterized in that, In S2, the vibration acoustic signal is subjected to spectral analysis to extract one or more main frequency features that characterize its main energy distribution; the main frequency features are fused with the vibration velocity time sequence features of multiple measurement points to form a spatiotemporal joint feature vector.

6. The tension detection method for yarn conveying according to any one of claims 1-5, characterized in that, In S4, the nonlinear wave equation takes into account the bending stiffness of the yarn itself and the air damping effect during the motion.

7. The tension detection method for yarn conveying according to claim 6, characterized in that, The error between the vibration characteristics of pure yarn and the vibration characteristics predicted by the nonlinear wave equation is the root mean square error.

8. A tension detection system for yarn conveying, characterized in that, The method includes a memory and a processor, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the tension detection method for yarn conveying according to any one of claims 1-7 is implemented.

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