Product quality real-time monitoring terminal for spinning site
By deploying real-time monitoring terminals on the spinning site, and using high-sensitivity sensors and data analysis technology to automatically adjust the parameters of spinning equipment, the problems of insufficient real-time and accuracy of traditional quality control methods are solved, and efficient and stable spinning production is achieved.
Patent Information
- Application Number
- CN202510045331.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-13
- Publication Date
- 2025-05-30
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional spinning quality control methods have problems such as poor real-time, insufficient accuracy and slow response speed, and cannot effectively monitor the dynamic changes of yarn, resulting in reduced production efficiency and waste of raw materials.
Design a real-time monitoring terminal for spinning site product quality, including high-sensitivity sensor module, data processing module and control module, to collect the tension, uniformity and fracture of the yarn in real time, and automatically adjust the spinning equipment parameters through data analysis and feedback control.
Real-time monitoring and dynamic adjustment of yarn quality are achieved, yarn breaking rate is reduced, spinning efficiency is improved, energy consumption is reduced, and the consistency of yarn quality is improved.
Smart Images
Figure CN120065928A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of quality supervision, and particularly to a real-time product quality monitoring terminal for spinning sites. Background Art
[0002] Spinning is one of the core processes in the textile industry, aiming to produce yarns by subjecting raw material fibers to processes such as stretching, twisting, and forming. During the spinning process, the quality of the yarn directly affects the stability of downstream weaving and dyeing processes, as well as the appearance, feel, and strength of the finished fabric. However, due to the complex operating environment and high speed of spinning equipment, problems such as yarn tension fluctuations, breakage, and uneven thickness are relatively common. These problems not only lead to a decrease in production efficiency but also may cause a large amount of raw material waste and increase production costs.
[0003] Traditional spinning quality control methods mainly rely on manual inspection and regular sampling inspection, but this method has the following defects:
[0004] Poor real-time performance: Sampling inspection can only reflect the yarn quality within a specific time period and cannot comprehensively grasp the fluctuations during the entire production process.
[0005] Insufficient accuracy: Manual inspection relies on the experience and subjective judgment of operators, with limited inspection accuracy and human errors.
[0006] Slow response speed: Once yarn breakage or quality abnormality occurs, manual adjustment has a lag, easily leading to the production of a large number of defective products.
[0007] With the development of the textile industry towards intelligence and automation, traditional manual monitoring can no longer meet the requirements of modern spinning production for high efficiency, stability, and low energy consumption. Therefore, there is an urgent need for an automated monitoring system that can real-time monitor yarn tension, evenness, and yarn breakage to ensure the stability of the spinning process, improve product quality, reduce manual intervention, and lower production costs.
[0008] Therefore, we propose a real-time product quality monitoring terminal for spinning sites to solve the existing problems. Summary of the Invention
[0009] The object of the present invention is to propose a real-time product quality monitoring terminal for spinning sites in view of the problems existing in the background art.
[0010] To achieve the above object, the present invention provides the following technical solutions: A real-time product quality monitoring terminal for a spinning site, including a sensor module, a data processing module, a control module, and the following operating steps. The sensor module is equipped with high-sensitivity sensors to collect key parameters during the spinning process in real time. After the data processing module collects the data, the controller normalizes the parameters. If any parameter exceeds the allowable range in the control module, the system immediately issues an alarm and adjusts through an electric actuator. The operating step 1: After the system starts, set the target parameter value and the allowable error range. The step 2: Sample the data N times per second and take the average value. The step 3: If an abnormal value is detected, trigger feedback control and automatically adjust the machine operating parameters according to the formula. The step 4: When multiple adjustments are ineffective, the system determines that there is a major problem and executes a shutdown command.
[0011] Preferably, the core function of the sensor module is to monitor the dynamic parameters of the yarn during the spinning process in real time: yarn tension, yarn evenness, and yarn strength. Its sampling frequency is as high as 1000 times per second to ensure data accuracy. The sensor types include a tension sensor (mechanical type): detecting the tension fluctuation during the yarn running; an optoelectronic sensor (online evenness detection): measuring the thickness change of the yarn by the light beam occlusion method to evaluate the evenness; and a yarn break sensor (contact type): detecting the yarn break and triggering a shutdown or alarm mechanism.
[0012] Preferably, the core function of the data processing module is to normalize and detect anomalies in the data collected by the sensors. The sliding window method is used to smooth the data and reduce random errors. First, the system normalizes the data such as tension and yarn evenness, eliminates the dimensional differences between different parameters, and maps various parameters to a unified [0,1] interval. This processing method facilitates subsequent multi-parameter comprehensive analysis and improves the accuracy of anomaly detection.
[0013] Preferably, in order to reduce random errors, the system uses the sliding window method to smooth the data. Under the high-frequency sampling of 1000 times per second, the system calculates the average value within the sliding window to eliminate the influence of short-term fluctuations. This method effectively filters out environmental noise and improves the stability and reliability of the monitoring data. After the data is smoothed, the system performs anomaly detection on various parameters. If the deviation of tension or yarn evenness exceeds the threshold range, it immediately triggers an alarm or a feedback control mechanism to ensure timely adjustment of the spinning process and guarantee the continuity and high-quality output of production.
[0014] Preferably, the core function of the control module is to automatically adjust the equipment parameters based on the data analysis results to prevent the yarn break or quality fluctuation from exceeding the range, and dynamically adjust the motor speed according to the data such as tension and evenness to control the yarn feeding speed.
[0015] Preferably, in step 1, i.e., parameter initialization, after the system is started, the target parameter value and the allowable error range are set. In step 2, i.e., real-time data acquisition and analysis, data is sampled N times per second, and the moving window average is taken to smooth the signal fluctuation. The current uniformity deviation is detected. If the value is abnormal, the system automatically adjusts the spinning speed to reduce the yarn unevenness phenomenon.
[0016] Preferably, in step 3, i.e., anomaly detection and feedback control, if an abnormal value is detected, feedback control is triggered, and the coefficient is quickly corrected according to the adjustment strategy to ensure that the adjustment strength is appropriate and avoid system oscillation caused by over-adjustment.
[0017] Preferably, in step 4, i.e., the automatic shutdown function, if the system fails to correct continuously for multiple times (i.e., the parameter deviation still exceeds the set range), the system triggers an automatic shutdown.
[0018] Preferably, through the above real-time monitoring terminal, the following goals can be achieved: reducing the yarn breakage rate, improving the spinning efficiency, and reducing the energy consumption.
[0019] Compared with the prior art, the beneficial effects of the present invention are as follows:
[0020] This solution proposes a real-time monitoring terminal for product quality in the spinning site. By integrating a high-precision sensor module, a data processing module, and a feedback control module on the spinning equipment, automatic monitoring and dynamic adjustment of the yarn quality are realized, ensuring the stability and reliability of the spinning process;
[0021] Real-time monitoring and anomaly detection: Through hardware devices such as high-precision tension sensors, photoelectric sensors, and yarn break detectors, the system can monitor the tension, uniformity, and breakage of the yarn throughout the spinning process. The data collected by the sensors can reach thousands of times per second, achieving millisecond-level response, ensuring that even the slightest fluctuations in the yarn quality can be captured and analyzed, and avoiding the accumulation of problems;
[0022] Automated feedback control to reduce human intervention: Once the yarn parameters are detected to exceed the set range, the system immediately triggers an automatic feedback mechanism. By adjusting the draw ratio, twist speed, or tension wheel pressure of the spinning machine, the parameters are quickly restored to the normal range;
[0023] Data smoothing and anomaly correction: The system is built-in with a moving window smoothing algorithm and a dynamic correction strategy to ensure the stability and accuracy of the monitoring data. By normalizing and smoothing the sensor data multiple times, the system can effectively filter out external interference and random noise, ensuring the accuracy and reliability of the monitoring results, and avoiding false alarms or over-adjustment caused by short-term fluctuations;
[0024] Reducing Energy Consumption and Downtime: Since the system can detect potential risks and make adjustments before the yarn breaks, the yarn breakage rate and equipment downtime frequency are significantly reduced. The spinning machine does not need to be restarted frequently, reducing the energy consumption of equipment idling and yarn waste;
[0025] Improving Yarn Quality and Consistency: By dynamically adjusting the parameters of the spinning machine, the system can ensure that the yarn is of uniform thickness, reduce defects, improve the strength and wear resistance of the yarn, and significantly enhance the appearance and quality of the finished fabric. After the consistency of the yarn quality is enhanced, the breakage rate and the number of fabric defects in the downstream weaving process are reduced, and the overall production process becomes smoother. Brief Description of the Drawings
[0026] Figure 1 It is a schematic flow chart of the present invention; Detailed Embodiments
[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
[0028] Embodiment 1
[0029] As Figure 1 shown, a real-time product quality monitoring terminal for the spinning site proposed by the present invention includes a sensor module, a data processing module, and a control module. The sensor module is equipped with high-sensitivity sensors to collect key parameters during the spinning process in real time. After the data processing module collects the data, the parameters are normalized by the controller. If any parameter exceeds the allowable range, the system immediately issues an alarm and makes adjustments through an electric actuator.
[0030] The core function of the sensor module is to monitor the dynamic parameters of the yarn during the spinning process in real time: yarn tension, yarn evenness, and yarn strength. Its acquisition frequency is as high as 1000 times per second to ensure data accuracy. The types of sensors include tension sensors (mechanical type): detecting the tension fluctuations during the operation of the yarn. Yarn tension is an important parameter affecting the strength and breakage rate of the yarn during the spinning process. The tension sensor can monitor the tensile force changes during the operation of the yarn in real time and transmit the data to the data processing module. The tension calculation formula is as follows:
[0031]
[0032] Among them, T is the yarn tension, F represents the force on the yarn, and A is the cross-sectional area of the yarn. This formula can accurately calculate the tensile force on the yarn during the spinning process, so as to monitor whether it is within a reasonable range.
[0033] Optical sensor (online uniformity detection): Measures the thickness variation of the yarn through the beam occlusion method to evaluate the uniformity. The yarn uniformity is directly related to the smoothness and strength of the fabric surface. The system uses an optical sensor to detect the unevenness of the yarn thickness through the beam occlusion method. The specific principle is to measure the degree of beam occlusion by the yarn when passing through the sensor with an optical probe, obtain the variation of the yarn diameter, and thus calculate the yarn uniformity.
[0034] And yarn breakage sensor (contact type): Detects yarn breakage and triggers the shutdown or alarm mechanism. During the spinning process, yarn breakage will cause the equipment to stop, seriously affecting production efficiency. The breakage sensor can quickly identify yarn breakage and trigger the shutdown or alarm mechanism to prevent damage to other processes caused by the continuous operation of the equipment.
[0035] The core function of the data processing module is to filter, normalize, and perform anomaly detection on a large amount of sensor data collected. This module uses efficient mathematical models and data smoothing algorithms to ensure that the system can still maintain high stability and accuracy in a complex production environment;
[0036] The core function of the data processing module is to normalize and perform anomaly detection on the data collected by the sensors, and uses the sliding window method to smooth the data and reduce random errors. First, the system performs normalization processing on data such as tension and yarn uniformity to eliminate the dimensional differences between different parameters and map various parameters to a unified [0,1] interval. This processing method facilitates subsequent comprehensive analysis of multiple parameters and improves the accuracy of anomaly detection. Among them, the yarn tension (T, unit: N), yarn uniformity (U, unit: percentage), and the specific steps are as follows:
[0037] Since the output dimensions of different sensors are different, for the convenience of unified analysis, the system performs normalization processing on parameters such as tension, uniformity, and strength. Normalization processing: Maps parameters such as tension and uniformity to the [0,1] range for easy unified analysis and comparison.
[0038]
[0039] Uniformity deviation rate: Calculates the deviation of the current uniformity compared to the target uniformity.
[0040]
[0041] Tension volatility: The allowable fluctuation range of the yarn running tension is set to ±5%.
[0042]
[0043] If ΔT is greater than 5%, the adjustment mechanism is triggered.
[0044] The core function of the control module is to automatically adjust the equipment parameters based on the data analysis results, prevent the yarn breakage or quality fluctuation from exceeding the range, dynamically adjust the motor speed according to the data such as tension and evenness, and control the yarn feeding speed. The PID control algorithm is as follows:
[0045]
[0046] Wherein:
[0047] u(t): control signal;
[0048] e(t): the error between the target parameter and the actual parameter;
[0049] K p 、K i 、K d : the proportional, integral and differential coefficients of the PID controller.
[0050] Embodiment 2
[0051] As Figure 1 shown, a real-time product quality monitoring terminal for spinning site proposed by the present invention includes the following operation steps:
[0052] Step 1 Parameter initialization:
[0053] After the system starts, set the target parameter value and the allowable error range.
[0054] Target parameter value: T target =20N,U target =8%,
[0055] Error range: the allowable fluctuation range of tension is ±5%.
[0056] Step 2 Real-time data acquisition and analysis:
[0057] In order to reduce the random error, the system uses the sliding window method to smooth the data. Under the high-frequency sampling of 1000 times per second, the system eliminates the influence of short-term fluctuations by calculating the average value within the sliding window. This method effectively filters out the environmental noise and improves the stability and reliability of the monitoring data. After the data is smoothed, the system performs anomaly detection on various parameters. If the deviation of the tension or yarn evenness exceeds the threshold range, an alarm or feedback control mechanism is immediately triggered to ensure timely adjustment of the spinning process and guarantee the continuity and high-quality output of production.
[0058] Sample N = 1000 times of data per second, and take the average value of the sliding window to smooth the signal fluctuation:
[0059]
[0060] Wherein, Ti is the value of the i-th sampling point within the window, and N is the size of the sliding window.
[0061] Detect the current uniformity deviation. If ΔU > 10%,
[0062] the system will automatically adjust the spinning speed to reduce the yarn unevenness.
[0063] Step 3 Abnormality Detection and Feedback Control:
[0064] Its triggering conditions are:
[0065]
[0066] ΔU > 10%
[0067] Yarn breakage signal trigger
[0068] By analyzing the data collected by the sensor in real time, quickly identify parameter fluctuations that exceed the set threshold. Once fluctuations in key parameters such as tension or uniformity are detected to exceed the preset threshold (e.g., ±5%), the system immediately triggers an alarm or automatically enters the feedback control mode, activates the adjustment strategy, and quickly returns to the normal state;
[0069] If the above abnormal values are detected, feedback control is triggered and adjusted in stages in a gradually decreasing manner, so that the parameters gradually return to the target value, rather than being corrected to the target level at once. This method can effectively reduce the risk of system oscillation, while ensuring the stability and accuracy of the adjustment. According to the adjustment strategy, quickly correct the coefficient to ensure that the adjustment strength is appropriate and avoid over-adjustment resulting in system oscillation.
[0070] The mathematical expression of its adjustment strategy is:
[0071] T adjust = T - k × (T - T target )
[0072] where k is the quick correction coefficient, and its usual value range is 0.5 ≤ k ≤ 1.1;
[0073] When the fluctuation is small, set a small k value, such as k = 0.6, and slowly correct the parameter;
[0074] When the fluctuation is large, appropriately increase the k value, such as k = 1, to accelerate the return to the target value and avoid deviating from the set range for a long time;
[0075] Through this method, the yarn tension gradually approaches the target value in a short time, avoiding secondary fluctuations or oscillation phenomena caused by over-adjustment, and realizing stable and precise spinning process control.
[0076] Step 4 Automatic Shutdown Function:
[0077] If the system fails to correct itself effectively for multiple consecutive times (i.e., the parameter deviation still exceeds the set range), the system will trigger an automatic shutdown, and its mathematical expression is:
[0078]
[0079] Through the above real-time monitoring terminal, the following goals can be achieved: reducing the yarn breakage rate:
[0080] R 断纱 = R 原 × (1 - 30%)
[0081] The measured data shows that the yarn breakage rate is reduced by an average of 30%.
[0082] Improving the spinning efficiency:
[0083] Q 提升 = Q 原 × (1 + 15%)
[0084] The spinning amount per unit time is increased by about 15%.
[0085] Reducing energy consumption:
[0086] P 节能 = P 原 × (1 - 20%)
[0087] The power consumption is reduced by about 20%.
[0088] The above specific embodiments are merely several preferred embodiments of the present invention. Based on the technical solution of the present invention and the relevant inspirations of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.
[0089] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to encompass all changes falling within the meaning and scope of the equivalent elements of the claims in the present invention.
Claims
1. A real-time monitoring terminal for product quality at a spinning site, comprising a sensor module, a data processing module and a control module and the following operating steps, characterized in that: The sensor module is equipped with a high-sensitivity sensor to collect key parameters in the spinning process in real time. After the data processing module collects data, the parameters are normalized through the controller. If any parameter of the control module exceeds the allowable range, the system will immediately issue an alarm and make adjustments through the electric actuator. The operation step 1: after the system is started, the target parameter value and the allowable error range are set. The step 2: sample data N times per second and take the average. The step 3: if an abnormal value is detected, feedback control is triggered to automatically adjust the machine operation parameters according to the formula. The step 4: when multiple adjustments are invalid, the system determines that there is a major problem and executes a shutdown command.
2. A spinning product quality real-time monitoring terminal according to claim 1, characterized in that: The core function of the sensor module is to monitor the dynamic parameters of the yarn during the spinning process in real time: yarn tension, yarn uniformity and yarn strength. The acquisition frequency is as high as 1000 times / second to ensure data accuracy. The sensor types include tension sensor (mechanical type): detect tension fluctuations during yarn operation; photoelectric sensor (online uniformity detection): measure yarn thickness changes through beam blocking method, evaluate uniformity and yarn break sensor (contact type): detect yarn breakage and trigger shutdown or alarm mechanism.
3. A spinning product quality real-time monitoring terminal according to claim 1, characterized in that: The core function of the data processing module is to normalize and detect anomalies of the data collected by the sensor, and use the sliding window method to smooth the data and reduce random errors. First, the system normalizes the tension, yarn evenness and other data to eliminate the dimensional differences between different parameters and map various parameters to a unified [0,1] interval. This processing method facilitates subsequent multi-parameter comprehensive analysis and improves the accuracy of anomaly detection.
4. A spinning product quality real-time monitoring terminal according to claim 3, characterized in that: In order to reduce random errors, the system uses a sliding window method to smooth the data. Under a high-frequency sampling of 1000 times per second, the system eliminates the impact of short-term fluctuations by calculating the average value within the sliding window. This method effectively filters out environmental noise and improves the stability and reliability of monitoring data. After the data is smoothed, the system performs abnormal detection on various parameters. If the tension or yarn uniformity deviation exceeds the threshold range, an alarm or feedback control mechanism is immediately triggered to ensure timely adjustment of the spinning process and ensure production continuity and high-quality output.
5. The real-time monitoring terminal for product quality at a spinning site according to claim 1, characterized in that: The core function of the control module is to automatically adjust equipment parameters based on data analysis results to prevent yarn breakage or quality fluctuations beyond a range, dynamically adjust the motor speed according to tension, uniformity and other data, and control the yarn feeding speed.
6. A spinning product quality real-time monitoring terminal according to claim 1, characterized in that: The step 1 is parameter initialization. After the system is started, the target parameter value and the allowable error range are set. The step 2 is real-time data acquisition and analysis. Data is sampled N times per second, the sliding window average is taken to smooth the signal fluctuation, and the current uniformity deviation is detected. If the value is abnormal, the system automatically adjusts the spinning speed to reduce the unevenness of the yarn.
7. A spinning product quality real-time monitoring terminal according to claim 1, characterized in that: The step 3 is anomaly detection and feedback control. If an abnormal value is detected, feedback control is triggered, and the coefficient is quickly corrected according to the adjustment strategy to ensure that the adjustment force is moderate and avoid excessive adjustment that causes system oscillation.
8. The real-time monitoring terminal for product quality at a spinning site according to claim 1, characterized in that: If the system fails to make corrections for several consecutive times (i.e. the parameter deviation still exceeds the set range), the system will trigger an automatic shutdown.
9. A spinning product quality real-time monitoring terminal according to claim 1, characterized in that: The real-time monitoring terminal can achieve the following goals: reduce yarn breakage rate, improve spinning efficiency and reduce energy consumption.