A loom operating parameter monitoring method and system

Abstract

The present application relates to the field of data processing, and particularly relates to a weaving machine operation parameter monitoring method and system. Specifically comprising: obtaining temperature data, humidity data and mechanical parameter data; respectively analyzing the data gradual change degree of the temperature data and the humidity data, determining a fiber water content gradient value set; determining a mechanical tension attention value set and a motor power attention value set, and calculating a weaving machine state parameter set; inputting the fiber water content gradient value set and the weaving machine state parameter set of the weaving machine into an ARIMA model respectively, and outputting a temperature and humidity prediction sequence and a weaving machine state parameter sequence through the ARIMA model respectively; and using a Pearson correlation coefficient to calculate the change synchronization rate of the temperature and humidity prediction sequence and the weaving machine state parameter sequence. The present application can determine a state abnormal result according to the change synchronization rate of the weaving machine state parameter sequence and the temperature and humidity, timely feedback adjust the environment temperature and humidity, and improve the production efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a weaving machine operation parameter monitoring method and system. BACKGROUND

[0002] A weaving machine is an automatic textile equipment widely used in the production of fabrics or cloth. Its basic working principle is to interweave warp and weft yarns into a fabric, usually forming a planar, warp and weft direction fabric structure through interlaced yarns. The weaving machine can adjust process parameters according to different product requirements to achieve different fabric production effects. Modern weaving machines use digital control technology to achieve high-speed, high-precision automatic production. During the operation of the weaving machine, multiple dimensions of operation parameters are collected and monitored in real time. These parameters are usually presented in the form of data points, including: 1. Mechanical parameters, mainly the running speed of the weaving machine (the speed of the driving motor); 2. Yarn tension value, i.e. the tension value of warp and weft yarns (unit: N); 3. The specific position and motion state of the shuttle in the weaving machine, which is collected by sensors in real time, mainly including its running speed and acceleration.

[0003] In the production process of the weaving machine, due to the local heating effect caused by the cooperative work of multiple motors, as well as the influence of environmental humidity, the temperature and humidity in the actual working environment change randomly. In the textile industry, environmental temperature and humidity directly affect the moisture content in natural fibers (such as cotton, wool, silk) and synthetic fibers (such as viscose, nylon), and moisture absorption and expansion will cause the yarn diameter to increase slightly, the softness to change, and thus the mechanical tension to change, especially in the weft insertion and cloth pressing stages. Under the influence of this condition, the final product cloth density will be high and low, causing the cloth to wrinkle or have different hand feelings. SUMMARY

[0004] In order to solve the technical problem that the abnormal environmental temperature and humidity in the production process of the weaving machine affect the production cloth, the purpose of the present application is to provide a weaving machine operation parameter monitoring method and system, and the technical scheme adopted is as follows:

[0005] A weaving machine operation parameter monitoring method, comprising:

[0006] Obtaining temperature data, humidity data and mechanical parameter data of the weaving machine, the mechanical parameter data including tension data and motor power data, the temperature data, humidity data and mechanical parameter data having the same time period;

[0007] Respectively analyzing the data gradual change degree of the temperature data and the humidity data to determine a fiber moisture content gradient value set;

[0008] determine a mechanical tension attention value set and a motor power attention value set according to the tension data and the motor power data respectively, and calculate a weaving machine state parameter set according to the mechanical tension attention value set and the motor power attention value set;

[0009] input the fiber water content gradient value set and the weaving machine state parameter set into an ARIMA model respectively, and output a temperature and humidity prediction sequence and a weaving machine state parameter sequence respectively through the ARIMA model;

[0010] calculate a change synchronization rate of the temperature and humidity prediction sequence and the weaving machine state parameter sequence using a Pearson correlation coefficient, and determine whether the running environment of the weaving machine is abnormal according to the change synchronization rate.

[0011] Further, analyze the data gradient degree of the temperature data and the humidity data respectively to determine the fiber water content gradient value set, specifically including:

[0012] calculate a temperature gradient degree value set and a humidity gradient degree value set according to the temperature data and the humidity data respectively;

[0013] calculate the fiber water content gradient value set according to the temperature gradient degree value set and the humidity gradient degree value set.

[0014] Further, calculate the temperature gradient degree value set and the humidity gradient degree value set according to the temperature data and the humidity data respectively, specifically including:

[0015] calculate a temperature outlier value at the time of and a humidity outlier value at the time of through a LOF operator respectively;

[0016] obtain the temperature data and the humidity data within a preset range adjacent to the time of , calculate the absolute value of the difference between the temperature data within the preset range adjacent to the time of and the temperature data at the time of , and sum them up to obtain a temperature difference value at the time of , calculate the absolute value of the difference between the humidity data within the preset range adjacent to the time of and the humidity data at the time of ;

[0017] multiply the temperature outlier value at the time of and the temperature difference value at the time of to obtain a temperature gradient degree value at the time of , and multiply the humidity outlier value at the time of and the humidity difference value at the time of to obtain a humidity gradient degree value at the time of ;

[0018] calculate The degree of temperature change at time and The humidity gradient values ​​at each time point are used to obtain the temperature gradient value set and the humidity gradient value set, respectively.

[0019] Furthermore, based on the set of temperature gradient values ​​and the set of humidity gradient values, a set of fiber water content variability values ​​is calculated, specifically including:

[0020] Obtain each The temperature and humidity data of the fabric samples produced at any given time during the production period are collected, and the temperature and humidity data are calculated separately. The average temperature at time t and Average humidity at any given time;

[0021] Seeking The average temperature at time t and The absolute value of the difference between the mean temperatures at time points is obtained. Calculate the temperature difference value at time t. Average humidity at time and The absolute value of the difference between the mean humidity at time t is obtained. Humidity differences at different times;

[0022] The sum of the set of temperature gradient values Multiply the temperature difference values ​​at different times to obtain The first fiber moisture content change value at a given time, the mean of the set of humidity change values ​​and... Multiply the humidity difference values ​​at different times to obtain The time-varying value of water content in the second fiber at a given time;

[0023] according to The first fiber water content variation value at time t and The degree of change in water content of the second fiber at a given time was determined. The changing value of fiber water content at any given time;

[0024] calculate The change in fiber water content at any given time is used to obtain the set of change values ​​for fiber water content.

[0025] Furthermore, based on the tension data and motor power data, the sets of mechanical tension attention values ​​and the set of motor power attention values ​​are determined respectively, specifically including:

[0026] Obtain each The tension data and motor power data of the fabric sample produced at each moment are obtained during the production time. The isolated forest algorithm is used to obtain the isolated tension value and isolated motor power value of the fabric sample at each moment during the production time.

[0027] The isolated tension values ​​at each time point are summed and averaged to obtain the result. The mechanical tension of the fabric sample produced at each time step is measured, and the isolated motor power values ​​at each time step are summed and averaged to obtain the result. The motor power attention value of the fabric sample produced at any given time;

[0028] Calculate separately The mechanical tension attention value of the fabric sample produced at any given time and The motor power attention value of the fabric sample produced at any given time is used to obtain the set of mechanical tension attention value and the set of motor power attention value.

[0029] Furthermore, the set of state parameters of the loom is calculated based on the set of mechanical tension attention values ​​and the set of motor power attention values, specifically including:

[0030] Will The mechanical tension attention value of the fabric sample produced at any given time and The motor power values ​​of the fabric samples produced at each time point are summed, averaged, and normalized to obtain the following result. The state parameters of the first loom for the fabric sample produced at any given moment;

[0031] Calculate separately The first loom state parameters of the fabric sample produced at any given time are used to obtain the loom state parameter set.

[0032] Furthermore, the function used for normalization is: function.

[0033] Furthermore, the synchronization rate of changes in the temperature and humidity prediction sequence and the weaving machine state parameter sequence was calculated using the Pearson correlation coefficient, specifically including:

[0034] The correlation coefficient between the temperature and humidity prediction sequence and the weaving machine state parameter sequence was calculated using the Pearson correlation coefficient, and the correlation coefficient was used as the rate of change synchronization.

[0035] Furthermore, based on the change synchronization rate, it is determined whether the operating environment of the weaving machine is abnormal, specifically including:

[0036] When the change synchronization rate is greater than the preset safety threshold, it is determined that the operating environment of the weaving machine is in an abnormal state.

[0037] When the change synchronization rate is less than or equal to a preset safety threshold, it is determined that the running environment of the weaving machine is in a safe state.

[0038] The application provides a weaving machine running parameter monitoring system, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program is used to realize the steps of the weaving machine running parameter monitoring method when executed by the processor.

[0039] The application has the following beneficial effects:

[0040] The application obtains temperature data and humidity data of the environment where the weaving machine is located and mechanical parameter data of the weaving machine, monitors and transmits the environment temperature and humidity and the mechanical parameter data of the weaving machine in real time, analyzes the temperature and humidity change gradient to obtain the production line fiber water content gradient, determines the weaving machine mechanical tension attention value according to the tension data, combines the motor power attention value to obtain the weaving machine state parameter sequence and perform prediction processing, analyzes the weaving machine state parameter sequence and the temperature and humidity change synchronization rate to determine the state abnormal result, feeds back and adjusts the environment temperature and humidity, avoids the problem of the finished cloth surface density of the weaving machine production, and improves the production efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0042] Figure 1 A flow chart of a weaving machine running parameter monitoring method provided by one embodiment of the present application;

[0043] Figure 2 A structure diagram of a weaving machine running parameter monitoring system provided by one embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purposes, the following describes the specific implementation, structure, features and effects of the weaving machine running parameter monitoring method and system according to the present application in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0046] The application provides a weaving machine operation parameter monitoring method and system, and the specific scheme is specifically explained below in combination with the drawings.

[0047] The application provides a weaving machine operation parameter monitoring method and system, and the specific scheme is specifically explained below in combination with the drawings. Figure 1 Fig. 1 shows a flow chart of a weaving machine operation parameter monitoring method according to an embodiment of the application, and the method comprises the following steps:

[0048] S101, acquiring temperature data and humidity data of an environment where the weaving machine is located and mechanical parameter data of the weaving machine, the mechanical parameter data comprising tension data and motor power data, the temperature data, the humidity data and the mechanical parameter data being of the same time period.

[0049] Exemplarily, the main purpose of the application is to analyze the environmental temperature and humidity and the mechanical parameter data of the weaving machine, and therefore the temperature data and humidity data of the environment where the weaving machine is located and the mechanical parameter data of the weaving machine need to be acquired. Specifically, an environmental temperature and humidity sensor and a weaving machine mechanical parameter sensor can be installed to monitor and transmit the environmental temperature and humidity and the weaving machine mechanical parameter data in real time.

[0050] In a specific embodiment, the environmental temperature and humidity sensor is DHT22. The sensor has high precision and stability and is suitable for industrial environments. The installation position can be near the working area of the weaving machine to avoid direct exposure to the heat source of the machine or the humid environment. The main parameters of the environmental temperature and humidity sensor include: 1) temperature range: -40 degrees Celsius to +125 degrees Celsius; 2) humidity range: 0% to 100% RH (relative humidity); 3) precision: temperature: ±0.3°C; humidity: ±2% RH (relative humidity); resolution: temperature 0.1°C, humidity 0.1% RH; and the working principle is that the environmental temperature and humidity sensor measures the changes of temperature and humidity in the air, converts the data into an electrical signal output, and transmits it to a data acquisition system for real-time monitoring.

[0051] The mechanical parameter sensor of the weaving machine can include a tension sensor, specifically a Kistler sensor, installed in the yarn guide system of the weaving machine or on the tension control device of the yarn; the measurement range is 0-1000 N; the accuracy is ±1% FS (full scale accuracy). The motor power sensor can be a current sensor and a power meter; the current sensor is installed in the motor power line control system to monitor the current change of the motor; the current range is 0-100 A; the accuracy is ±1% FS; the output signal is an analog signal: 4-20 mA. The data of all sensors will be collected, processed and transmitted through the data acquisition module (PLC). And the transmission protocol uses the industrial communication protocol: CAN bus, which is used to transmit the sensor data to the central control system or cloud platform in real time.

[0052] Optionally, a rotational speed sensor can also be used to measure the rotational speed of the motor in this embodiment, specifically a Hall effect sensor, installed on the motor or the driving shaft of the textile machine, the rotational speed range is 0-5000 RPM (revolutions per minute); the accuracy is ±0.1% FS.

[0053] It should be noted that in order to facilitate calculation, all index data involved in the calculation in the embodiment of the application are pre-processed, thereby canceling the dimensional influence. The means for de-dimensioning are well-known to those skilled in the art and are not limited here.

[0054] Specifically, in this embodiment, since it is necessary to analyze the correlation between temperature and humidity data and mechanical parameter data, the data obtained here are temperature data, humidity data and mechanical parameter data in the same time period.

[0055] S102, analyze the data gradual change degree of the temperature data and the humidity data respectively to determine a fiber water content gradual change value set.

[0056] In this embodiment, the data gradual change degree of the temperature data and the humidity data is analyzed respectively to determine the fiber water content gradual change value set, specifically including:

[0057] According to the temperature data and the humidity data, a temperature gradual change degree value set and a humidity gradual change degree value set are calculated respectively;

[0058] According to the temperature gradual change degree value set and the humidity gradual change degree value set, the fiber water content gradual change value set is calculated.

[0059] According to the temperature data and the humidity data, a temperature gradual change degree value set and a humidity gradual change degree value set are calculated respectively, specifically including:

[0060] The temperature outlier of the temperature data and the humidity outlier of the humidity data at the time of t and t respectively are calculated by the LOF operator; The temperature outlier of the temperature data and the humidity outlier of the humidity data at the time of t and t respectively are calculated by the LOF operator;​

[0061] respectively obtain the absolute value of the difference between the temperature data and the humidity data at the adjacent preset range, and sum them up, to obtain the temperature difference value at the time, and obtain the absolute value of the difference between the humidity data and the humidity data at the adjacent preset range, and sum them up, to obtain the humidity difference value at the time;

[0062] multiply the temperature outlier value at the time and the temperature difference value at the time, to obtain the temperature gradual change degree value at the time, and multiply the humidity outlier value at the time and the humidity difference value at the time, to obtain the humidity gradual change degree value at the time;

[0063] calculate the temperature gradual change degree value at the time and the humidity gradual change degree value at the time, to obtain the temperature gradual change degree value set and the humidity gradual change degree value set respectively.

[0064] calculate the fiber water content gradient value set according to the temperature gradual change degree value set and the humidity gradual change degree value set, specifically including:

[0065] respectively obtain the temperature data and the humidity data of the cloth sample produced at the time within the production time, and calculate the temperature mean value at the time and the humidity mean value at the time;

[0066] obtain the absolute value of the difference between the temperature mean value at the time and the temperature mean value at the time, to obtain the temperature difference value at the time, and obtain the absolute value of the difference between the humidity mean value at the time and the humidity mean value at the time, to obtain the humidity difference value at the time;

[0067] multiply the mean value of the temperature gradual change degree value set and the temperature difference value at the time, to obtain the first fiber water content gradient value at the time, and multiply the mean value of the humidity gradual change degree value set and ​​Multiply the humidity difference values ​​at different times to obtain The time-varying value of water content in the second fiber at a given time;

[0068] according to The first fiber water content variation value at time t and The degree of change in water content of the second fiber at a given time was determined. The changing value of fiber water content at any given time;

[0069] calculate The change in fiber water content at any given time is used to obtain the set of change values ​​for fiber water content.

[0070] For example, the data monitoring results obtained after sensor deployment, collection, and real-time transmission include: temperature and humidity values ​​(this data type is a scatter digital signal with a fixed sampling rate of 3 times / second); tension and power sensor values ​​(this data type is also a scatter digital signal with a fixed sampling rate of 3 times / second). Because of the chain reaction caused by changes in environmental temperature and humidity, problems such as wrinkling occur in the fabric produced by the weaving machine. However, in actual production, it is impossible to accurately determine whether the processing abnormality is caused by the former. Therefore, this invention quantifies the state parameters of the weaving machine, which represent the real-time operating state of the weaving machine at the current moment. It is obtained by quantifying and calculating the characteristics of several real-time sensor data of the weaving machine. When there is a high synchronization rate between the state parameters of the weaving machine and the actual temperature and humidity changes, that is, the cause and effect are reflected in the synchronicity of data changes. For example, the increase in real-time humidity directly leads to an increase in the moisture content of fibers (such as viscose and nylon). Moisture absorption and expansion will cause a slight increase in yarn diameter and a change in softness, which in turn increases mechanical tension and the power of the loom motor. This proves that there is a direct correlation between the state parameters obtained by changing the parameters of the loom and the ambient temperature and humidity.

[0071] In order to accurately monitor the abnormalities in fabric production caused by ambient temperature and humidity through the analysis of operating parameters, it is first necessary to analyze the degree of temperature and humidity change.

[0072] When the temperature and humidity change significantly, it indicates that the aforementioned chain reaction may be occurring, requiring timely analysis to determine the necessity or adjustment value for temperature and humidity feedback regulation.

[0073] First, acquire the temperature and humidity data detected by sensors and transmitted to the control system. The temperature data can be represented as follows: Humidity data can be represented as .in, and They represent time. Temperature and humidity values ​​(time) This refers to the time corresponding to a certain environmental sample value.

[0074] Taking humidity data as an example, using each element in the humidity data as a sample, calculate the first... The outlier for humidity at a given time point is denoted as: (After normalization); the closer to 1, the higher the outlier the element is. The LOF operator is a well-known existing technology and will not be elaborated here.

[0075] calculate The degree of humidity change at any given time can be expressed by the following formula:

[0076]

[0077] in, express The degree of humidity change at any given time. express Humidity outlier at any given time express Humidity data at any given time express Humidity data at any given time express Preset range of adjacent moments.

[0078] The outlier value in the above formula As a product coefficient, the higher the coefficient, the larger the sample range (i.e., the preset range). Within the range, the more outlier the humidity value, the more abnormal the humidity level is compared to other humidity levels under the current working environment of the weaving machine, and the more abnormal the working environment. Furthermore, the humidity difference of elements within the sample range... Summing gives the difference between other humidity values ​​and the current humidity value. The larger this difference, the higher the fluctuation of humidity values ​​within the corresponding sample range. This means that the humidity variation is relatively high, and the temperature and humidity conditions of the current working environment for the weaving machine are quite demanding, which could potentially lead to the chain reaction mentioned in the technical issues above. Therefore, the degree of humidity change at each moment is obtained, and the [previous value] is acquired. Within a sample range All humidity gradient values.

[0079] For example, the calculation method for temperature gradient values ​​can refer to the calculation formula for humidity gradient values, simply by replacing the humidity data with temperature data; details will not be elaborated further here. At this point, we have obtained the sets of temperature gradient values ​​and the sets of humidity gradient values.

[0080] Further analysis of the first The variation in fiber moisture content within a sample segment of the production line, i.e., the degree of change in moisture content due to temperature and humidity variations, is not a direct value of fiber moisture content, as this is difficult to detect. The analysis process is as follows:

[0081] When the sample segment (the first) Average temperature and humidity within a sample range Compared to the average temperature and humidity of the previous sample segment (No. As the number of elements increases, the fiber moisture content shows an increasing trend. The relationship between temperature / humidity and fiber moisture content is as follows:

[0082] When humidity increases, the fibers absorb moisture, the fiber diameter increases, elasticity increases, and softness improves, but tension decreases.

[0083] When humidity decreases, fibers lose water, moisture content decreases, fiber diameter shrinks, fibers harden, elasticity decreases, but tension increases.

[0084] Specifically, this changing nature needs to be quantified, as shown in the following formula:

[0085]

[0086] in, Indicates the first The second fiber water content variation value of the sample production line segment express The average humidity of the fabric sample produced at any given time during the production period (i.e., (average humidity at that time) express The average humidity of the fabric sample produced at any given time during the production period (i.e., (average humidity at that time) This represents the mean of the set of values ​​indicating the degree of gradual change in humidity.

[0087] Optionally, according to The first fiber water content variation value at time t and The degree of change in water content of the second fiber at a given time was determined. The current fiber water content variation values ​​can be summed and averaged or weighted and averaged. The weights for weighted averaging can be set according to the actual situation, and no specific restrictions are made here.

[0088] When the mean humidity differs between sample segments The higher the humidity level, for example, if the humidity level is 50% in the first 10 minutes of a weaving machine's processing environment and 59% in the next 10 minutes, the greater the difference in average humidity within the sample segment, and consequently, the higher the rate of change in fiber moisture content. Clearly, the higher the degree of gradation, the greater the rate of change in moisture content. Whether the fiber moisture content increases or decreases, the rate of change will be higher, easily triggering chain reactions such as changes in mechanical tension mentioned in the technical issues above.

[0089] Optionally, the fiber water content variation value of the production line for each sample segment is obtained (i.e., The fiber moisture content variation values ​​at any given time are used to obtain a set of fiber moisture content variation values. These values ​​are then normalized. For values ​​in the range [0.6, 1], the possibility of production abnormalities in the weaving machine due to environmental factors is considered high, requiring real-time analysis of the weaving machine's status parameters to determine its monitoring status. For other sample segments, the environmental temperature and humidity changes are low or stable, making analysis unnecessary.

[0090] S103. Determine the mechanical tension attention value set and the motor power attention value set based on the tension data and motor power data respectively, and calculate the loom state parameter set based on the mechanical tension attention value set and the motor power attention value set.

[0091] In this embodiment, the mechanical tension attention value set and the motor power attention value set are determined based on tension data and motor power data, respectively, specifically including:

[0092] Obtain each The tension data and motor power data of the fabric sample produced at each moment are obtained during the production time. The isolated forest algorithm is used to obtain the isolated tension value and isolated motor power value of the fabric sample at each moment during the production time.

[0093] The isolated tension values ​​at each time point are summed and averaged to obtain the result. The mechanical tension of the fabric sample produced at each time step is measured, and the isolated motor power values ​​at each time step are summed and averaged to obtain the result. The motor power attention value of the fabric sample produced at any given time;

[0094] Calculate separately The mechanical tension attention value of the fabric sample produced at any given time and The motor power attention value of the fabric sample produced at any given time is used to obtain the set of mechanical tension attention value and the set of motor power attention value.

[0095] The set of state parameters for the weaving machine is calculated based on the set of mechanical tension attention values ​​and the set of motor power attention values, specifically including:

[0096] Will The mechanical tension attention value of the fabric sample produced at any given time and The motor power values ​​of the fabric samples produced at each time point are summed, averaged, and normalized to obtain the following result. The state parameters of the first loom for the fabric sample produced at any given moment;

[0097] Calculate separately The first loom state parameters of the fabric sample produced at any given time are used to obtain the loom state parameter set.

[0098] For example, due to the real-time nature of the weaving process, analyzing the synchronization rate solely based on real-time weaving machine status data and real-time temperature and humidity data is not feasible. This is because fabric defects can be observed directly in real time, rather than being predicted and corrected in advance. Therefore, once changes in the environmental fiber moisture content occur, it is necessary to perform predictive analysis based on the real-time weaving machine status data and then analyze the synchronization rate between the predicted status data and the corresponding predicted environmental temperature and humidity data. A high synchronization rate indicates that the abnormal weaving machine operation is caused by environmental temperature and humidity.

[0099] Among the state parameters of a weaving machine, the relationship between mechanical tension and water content is as follows: For temperature, high temperature environments will make the fibers looser, resulting in uneven yarn and breakage, which in turn leads to unstable tension; low temperature will increase fiber rigidity, which will affect the tensile properties of the fabric; for humidity, moisture absorption and expansion will cause a slight increase in yarn diameter and changes in softness, which will in turn change its mechanical tension.

[0100] Therefore, abnormal temperature and humidity can further cause abnormal changes in tension. Thus, it is necessary to quantify the mechanical tension attention value and the motor power attention value, specifically (taking tension data as an example):

[0101] The sequence of real-time tension values ​​detected by the sensor can be represented as: ,in, Indicates the first Real-time tension value sequence within a variable sample segment; Indicates time The tension value is generally fixed in a weaving production line because a stable weaving force is required for the fabric surface. Therefore, even a slight change in the tension value indicates a high mechanical tension, requiring close monitoring for any abnormalities in production.

[0102] The formula for quantifying the mechanical tension attention value is:

[0103]

[0104] wherein, represents the mechanical tension attention value of the i-th changing sample section (within the fabric sample production time); represents the total number of elements of the tension data within the sample section; represents the tension value at the i-th time, and the isolated value is calculated by the Isolation Forest algorithm with all tension values within the sample section as element samples. The higher the value (closer to 1), the higher the isolation, and even a slight change in tension can be filtered out at this time. The Isolation Forest algorithm is a known technology in the art, and will not be described here. Sum all tension values within the sample section. The higher the sum, the more obvious the surface tension change caused by the change in fiber moisture content in the loom due to the influence of environmental temperature and humidity. Further, tension changes will affect the friction and resistance in the transmission system, resulting in changes in the load of the main motor, and further changes in the internal resistance of the motor; the increase in the internal resistance of the motor will cause the working efficiency of the motor to decrease, thereby requiring more power to maintain a constant speed or output power. Long-term changes may cause overheating, further affecting the stability of the equipment.

[0105] Therefore, according to the recursive logical relationship, the motor power value (motor power data value sequence and its analysis can refer to the analysis process of the tension value) will also change, i.e., the change of its strain.

[0106] Therefore, the calculation formula of the motor power attention value can be:

[0107]

[0108] wherein,

[0109] represents the motor power attention value of the i-th changing sample section (within the fabric sample production time); represents the total number of elements of the motor power data within the sample section; represents the motor power value at the i-th time, and the isolated value is calculated by the Isolation Forest algorithm with all tension values within the sample section as element samples. The calculation method of the loom state parameter can be:

[0110]

[0111] ​​​​

[0112] wherein, represents the first weaving machine state parameter in the i-th sample section, represents a normalization function. The closer the value is to 1, the higher the degree of abnormality of the weaving machine running state at this time (i.e., abnormality of tension, motor power, etc.).

[0113] The first weaving machine state parameter of each sample section is obtained, and a prediction parameter sequence (i.e., a weaving machine state parameter set) is obtained.

[0114] S104, the fiber water content gradient value set and the weaving machine state parameter set are respectively input into an ARIMA model, and a temperature and humidity prediction sequence and a weaving machine state parameter sequence are respectively output by the ARIMA model.

[0115] The prediction parameter sequence is predicted by the ARIMA model (prior art, not described here), and the weaving machine state parameter sequence can be represented as: .

[0116] Similarly, the fiber water content gradient value set can be predicted by the ARIMA model (prior art, not described here), and the temperature and humidity prediction sequence (here, the humidity prediction sequence is taken as an example) can be represented as: . Wherein, represents the average humidity value in the i-th sample section.

[0117] S105, the change synchronization rate of the temperature and humidity prediction sequence and the weaving machine state parameter sequence is calculated using the Pearson correlation coefficient, and whether the running environment of the weaving machine is abnormal is determined according to the change synchronization rate.

[0118] In this embodiment, the change synchronization rate of the temperature and humidity prediction sequence and the weaving machine state parameter sequence is calculated using the Pearson correlation coefficient, specifically including:

[0119] The correlation coefficient of the temperature and humidity prediction sequence and the weaving machine state parameter sequence is calculated by the Pearson correlation coefficient, and the correlation coefficient is taken as the change synchronization rate.

[0120] According to the change synchronization rate, whether the running environment of the weaving machine is abnormal is determined, specifically including:

[0121] When the change synchronization rate is greater than a preset safety threshold, it is determined that the running environment of the weaving machine is in an abnormal state;

[0122] When the change synchronization rate is less than or equal to the preset safety threshold, it is determined that the running environment of the weaving machine is in a safe state.

[0123] ​​Exemplarily, the two sequences are acquired, and a change synchronization rate between the two sequences is calculated through a Pearson correlation coefficient The value is closer to 1, and the change synchronization rate between the two is higher Correspondingly, because of the existence of a chain reaction of changes in the water content of the fiber caused by changes in the ambient temperature and humidity to the running state of the weaving machine, at this time, the weaving machine is in abnormal operation, and needs to be fed back and adjusted in time. The adjustment mode is to keep the balance of the ambient temperature and humidity, that is, the gradual change degree of the temperature and humidity is lower than the alarm threshold 0.4.

[0124] In summary, the embodiment of the present application provides a weaving machine running parameter monitoring method and system, by acquiring the temperature data, humidity data and mechanical parameter data of the weaving machine, the ambient temperature and humidity and the mechanical parameter data of the weaving machine are monitored and transmitted in real time, and the gradual change degree of the temperature and humidity is analyzed to obtain the fiber water content gradient of the production line; and the mechanical tension attention value of the weaving machine is determined according to the tension data respectively, and the state parameter sequence of the weaving machine is obtained by combining the motor power attention value, and the prediction processing is carried out. The state parameter sequence of the weaving machine and the change synchronization rate of the temperature and humidity are analyzed to determine the state abnormal result, the ambient temperature and humidity are fed back and adjusted to avoid the problem of the finished cloth surface density of the weaving machine production, and the production efficiency is improved.

[0125] The present application also provides a weaving machine running parameter monitoring system, please refer to Figure 2 It shows the structure diagram of a weaving machine running parameter monitoring system provided by an embodiment of the present application, and the system comprises a data acquisition module 101, a data analysis module 102 and an abnormality detection module 103.

[0126] The data acquisition module 101 is used for acquiring the temperature data, humidity data and mechanical parameter data of the weaving machine, the mechanical parameter data comprises tension data and motor power data, and the time period of the temperature data, humidity data and mechanical parameter data is the same.

[0127] The data analysis module 102 is used for analyzing the data gradual change degree of the temperature data and the humidity data respectively, determining the fiber water content gradient value set, determining the mechanical tension attention value set and the motor power attention value set according to the tension data and the motor power data respectively, and calculating the weaving machine state parameter set according to the mechanical tension attention value set and the motor power attention value set; the fiber water content gradient value set and the weaving machine state parameter set are input into the ARIMA model respectively, and the temperature and humidity prediction sequence and the weaving machine state parameter sequence are output through the ARIMA model respectively.

[0128] The anomaly detection module 103 is configured to calculate a change synchronization rate of the temperature and humidity prediction sequence and the loom state parameter sequence by using a Pearson correlation coefficient, and determine whether the operation environment of the loom is abnormal according to the change synchronization rate.

[0129] It should be noted that the system provided in the above embodiments is only exemplified by the division of the above functional modules. In actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the above-described functions. In addition, the loom operation parameter monitoring system and the loom operation parameter monitoring method provided in the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be described here.

[0130] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0131] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

Claims

1. A method for monitoring the operating parameters of a weaving machine, characterized in that, The method includes: The temperature data, humidity data, and mechanical parameter data of the environment in which the weaving machine is located are obtained. The mechanical parameter data includes tension data and motor power data. The time periods for the temperature data, humidity data, and mechanical parameter data are the same. Analyze the degree of data gradation of the temperature data and the humidity data respectively to determine the set of fiber water content variability values; The mechanical tension attention value set and the motor power attention value set are determined based on the tension data and the motor power attention value set, respectively, and the loom state parameter set is calculated based on the mechanical tension attention value set and the motor power attention value set; The set of fiber water content variation values ​​and the set of weaving machine state parameters are respectively input into the ARIMA model, and the ARIMA model outputs the temperature and humidity prediction sequence and the weaving machine state parameter sequence respectively. The Pearson correlation coefficient is used to calculate the synchronization rate of the changes in the temperature and humidity prediction sequence and the weaving machine state parameter sequence, and based on the synchronization rate, it is determined whether the operating environment of the weaving machine is abnormal.

2. The method for monitoring the operating parameters of a weaving machine according to claim 1, characterized in that, The step of analyzing the gradual changes in the temperature and humidity data to determine the set of fiber moisture content variation values ​​specifically includes: Calculate the set of temperature gradient values ​​and the set of humidity gradient values ​​based on the temperature data and the humidity data, respectively. The set of fiber water content variation values ​​is calculated based on the set of temperature gradient values ​​and the set of humidity gradient values.

3. The method for monitoring the operating parameters of a weaving machine according to claim 2, characterized in that, The step of calculating the set of temperature gradient values ​​and the set of humidity gradient values ​​based on the temperature data and the humidity data respectively specifically includes: The temperature data and humidity data are calculated using the LOF operator. Temperature outliers at time t and Outlier humidity values ​​at any given time; Obtain each Given temperature and humidity data within a preset range adjacent to each other at any given time, calculate the temperature data within the preset range and... The absolute values ​​of the differences in temperature data at different times are summed to obtain the result. The temperature difference at a given time is used to calculate the humidity data within the adjacent preset range, and... The absolute values ​​of the differences in humidity data at different times are summed to obtain the result. Humidity difference at different times; The Temperature outliers at time t and the stated Multiply the temperature differences at each moment to get The temperature gradient value at time, and the stated Humidity outlier at time and the stated Multiply the humidity differences at different times to get The degree of humidity change at any given time; calculate The degree of temperature change at any given time and The humidity gradient values ​​at each time point are used to obtain the temperature gradient value set and the humidity gradient value set, respectively.

4. The method for monitoring the operating parameters of a weaving machine according to claim 3, characterized in that, The calculation of the fiber water content variation value set based on the temperature gradient value set and the humidity gradient value set specifically includes: Obtain each The temperature and humidity data of the fabric sample produced at any given time during the production period are collected, and the temperature and humidity data are calculated respectively. The average temperature at time t and Average humidity at any given time; Seek the above The average temperature at time t and The absolute value of the difference between the mean temperatures at time points is obtained. The temperature difference value at time t is calculated. Average humidity at time and The absolute value of the difference between the mean humidity at time t is obtained. Humidity differences at different times; The mean of the set of temperature gradient values ​​and the Multiply the temperature difference values ​​at different times to obtain The first fiber water content variation value at time t, the mean of the set of humidity variation values ​​and the... Multiply the humidity difference values ​​at different times to obtain The time-varying value of water content in the second fiber at a given time; According to the above The first fiber water content variation value at time t and the stated The second fiber water content variation value at time t, determine the The changing value of fiber water content at any given time; calculate The set of fiber water content change values ​​is obtained by calculating the change value of fiber water content at a given time.

5. The method for monitoring the operating parameters of a weaving machine according to claim 1, characterized in that, The step of determining the mechanical tension attention value set and the motor power attention value set based on the tension data and the motor power data respectively specifically includes: Obtain each The tension data and motor power data of the fabric sample produced at each moment during the production time are obtained, and the isolated forest algorithm is used to obtain the isolated tension value and isolated motor power value of the fabric sample at each moment during the production time. The isolated tension values ​​at each time point are summed and averaged to obtain... The mechanical tension value of the fabric sample produced at each time point is calculated, and the isolated values ​​of motor power at each time point are summed and averaged to obtain the result. The motor power attention value of the fabric sample produced at any given time; Calculate separately The mechanical tension attention value of the fabric sample produced at any given time and The set of mechanical tension attention values ​​and the set of motor power attention values ​​are obtained by measuring the motor power attention values ​​of the fabric sample produced at each moment.

6. The method for monitoring the operating parameters of a weaving machine according to claim 1, characterized in that, The calculation of the loom state parameter set based on the mechanical tension attention value set and the motor power attention value set specifically includes: Will The mechanical tension attention value of the fabric sample produced at any given time and The motor power values ​​of the fabric samples produced at each time point are summed, averaged, and normalized to obtain the following result. The state parameters of the first loom for the fabric sample produced at any given moment; Calculate separately The first weaving machine state parameters of the fabric sample produced at any given time are used to obtain the set of weaving machine state parameters.

7. The method for monitoring the operating parameters of a weaving machine according to claim 6, characterized in that, The function used for normalization is: function.

8. The method for monitoring the operating parameters of a weaving machine according to claim 1, characterized in that, The calculation of the synchronization rate between the temperature and humidity prediction sequence and the weaving machine state parameter sequence using the Pearson correlation coefficient specifically includes: The correlation coefficient between the temperature and humidity prediction sequence and the weaving machine state parameter sequence is calculated using the Pearson correlation coefficient, and the correlation coefficient is used as the change synchronization rate.

9. The method for monitoring the operating parameters of a weaving machine according to claim 1, characterized in that, The step of determining whether the operating environment of the weaving machine is abnormal based on the change synchronization rate specifically includes: When the change synchronization rate is greater than a preset safety threshold, it is determined that the operating environment of the weaving machine is in an abnormal state. When the change synchronization rate is less than or equal to a preset safety threshold, it is determined that the operating environment of the weaving machine is in a safe state.

10. A weaving machine operating parameter monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for monitoring the operating parameters of a weaving machine as described in any one of claims 1-9.

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