Abnormality monitoring method and device for textile equipment and storage medium

Abstract

The invention relates to the technical field of textile data processing, and particularly discloses an anomaly detection method and device for textile equipment and a storage medium, and the method comprises the steps: collecting basic representation data of the textile equipment, and obtaining a representation anomaly evaluation value of the textile equipment; the method comprises the following steps: acquiring production data of textile equipment to obtain a production data flow rate evaluation value, detecting output quality to obtain a quality inspection qualification evaluation value, and obtaining an output stability degree evaluation value according to the production data flow rate evaluation value and the quality inspection qualification evaluation value; and analyzing the characterization abnormity evaluation value and the output stability degree evaluation value to obtain an equipment health evaluation index, and performing equipment abnormity early warning control according to the equipment health evaluation index. According to the invention, the characterization data and the production data in the operation process of the textile equipment are collected and analyzed, so that the operation state of the textile equipment can be accurately reflected, real-time monitoring in the operation process of the textile equipment is realized, and abnormal conditions of the textile equipment can be timely processed.

Description

Technical Field

[0001] The present invention relates to the technical field of textile data processing, and in particular to an abnormality detection method, device and storage medium for textile equipment. Background Art

[0002] As an important part of the traditional manufacturing industry, the production efficiency and product quality of the textile industry are directly related to the competitiveness and market position of enterprises. With the rapid development of intelligent manufacturing and industrial Internet technology, abnormal detection of textile equipment has become a key link in improving production efficiency, reducing maintenance costs and ensuring product quality.

[0003] For example, the invention patent with announcement number CN114676593B is a method and related device for detecting abnormalities in textile equipment, which involves the field of textile technology. This method develops an input division method focusing on three different data relationships, namely time dependence, cross-variable correlation and combination relationship, for the monitoring data of multivariate state parameters, and reconstructs the aggregation in an integrated form according to these three input division methods. The aggregation strategy uses a weighted summation method according to the size of each reconstruction error, outputs the final integrated reconstruction value and reconstruction error, and then judges whether there is an abnormal event in the textile equipment based on the final reconstruction error. This method can reduce the possibility of false correlation, reduce noise abnormal detection behavior, and improve the reliability of abnormal detection results by dynamically identifying the importance of data relationships.

[0004] For example, the invention patent with announcement number CN117171670B is a method, device and system for monitoring faults in the textile production process, which involves the field of data processing technology. It mainly includes: comparing each value in the time series of the monitoring parameter with the normal value to obtain the fluctuation degree of the monitoring parameter; using the hierarchical analysis method to obtain the priority of the monitoring parameter, and combining the fluctuation degree to obtain the fault degree of the monitoring parameter; obtaining the first correlation coefficient between any two monitoring parameters through correlation analysis, and obtaining the second correlation coefficient between the two monitoring parameters according to the probability of any two monitoring parameters being abnormal at the same time; obtaining the fault value of the monitoring parameter; when the fault value of the monitoring parameter is greater than the preset first threshold, the production equipment corresponding to the monitoring parameter is repaired.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it was found that the above-mentioned technology has at least the following technical problems: At present, abnormality detection of textile equipment mostly relies on fixed data models. These models are often built based on the historical operation data of the equipment or expert experience. Although they can identify common failure modes to a certain extent, they are difficult to adapt to the dynamic changes caused by environmental changes, material differences, process adjustments, etc. during the operation of the equipment. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an abnormality detection method, device and storage medium for textile equipment, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an abnormality detection method for textile equipment, including: collecting basic characterization data of textile equipment, processing to obtain a characterization abnormality evaluation value of the textile equipment; acquiring production data of the textile equipment, processing to obtain a production data flow rate evaluation value of the textile equipment, and detecting the output quality of the textile equipment, processing to obtain a quality inspection qualified evaluation value of the textile equipment, and comprehensively analyzing the production data flow rate evaluation value and the quality inspection qualified evaluation value of the textile equipment to obtain an output stability evaluation value of the textile equipment; comprehensively analyzing the characterization abnormality evaluation value and the output stability evaluation value of the textile equipment to obtain an equipment health evaluation index of the textile equipment, and performing equipment abnormality early warning control based on the equipment health evaluation index of the textile equipment.

[0008] As a further method, the processing obtains the characterization abnormality evaluation value of the textile equipment. The specific process is: deploy a number of data monitoring points, obtain the basic characterization data at each data monitoring point, including the temperature and vibration frequency of the textile equipment at each data monitoring point, and comprehensively analyze to obtain the characterization abnormality evaluation value of the textile equipment.

[0009] As a further method, the production data flow rate evaluation value of the textile equipment is obtained after processing. The specific process is: divide the time periods according to the preset time period, mark them as historical time periods, obtain the output, average rate and operating energy consumption of the textile equipment in each historical time period, and comprehensively analyze to obtain the production data flow rate evaluation value of the textile equipment.

[0010] As a further method, the quality inspection qualified evaluation value of the textile equipment is obtained through processing, and the specific process is: obtaining the fabric density, color fastness and strength of each textile produced by the textile equipment in each historical time period, and comprehensively analyzing to obtain the quality inspection qualified evaluation value of the textile equipment.

[0011] As a further method, the equipment health assessment index of the textile equipment, the specific process is: Based on the production data flow rate assessment value and quality inspection qualification assessment value of textile equipment, a comprehensive analysis is conducted to obtain the output stability assessment value of the textile equipment; based on the characterization abnormality assessment value and output stability assessment value of the textile equipment, a comprehensive analysis is conducted to obtain the equipment health assessment index of the textile equipment.

[0012] As a further method, the equipment abnormality early warning control specifically comprises the following process: extracting the equipment health assessment index threshold of the textile equipment from the textile database, comparing the equipment health assessment index with the equipment health assessment index threshold; if the equipment health assessment index is greater than or equal to the equipment health assessment index threshold, it indicates that the textile equipment has no abnormality; if the equipment health assessment index is less than the equipment health assessment index threshold, it indicates that the textile equipment has an abnormality, and an equipment abnormality early warning is issued.

[0013] As a further method, the characterization abnormality evaluation value of the textile equipment is specifically expressed as: ; In the formula, is the number of each data monitoring point, , is the total number of data monitoring points, Indicates the characteristic abnormal evaluation value of textile equipment, Indicates The temperature of each data monitoring point, Indicates standard temperature, Indicates the set allowable deviation temperature. Indicates The vibration frequency of each data monitoring point, Indicates the standard vibration frequency, Indicates the set allowable deviation vibration frequency, Indicates the abnormal impact factor corresponding to the set temperature, Indicates the abnormal impact factor corresponding to the set vibration frequency.

[0014] As a further method, the production data flow rate evaluation value of the textile equipment is specifically expressed as: ; In the formula, is the number of each historical time period, , is the total number of historical time periods, Indicates the estimated value of the production data flow rate of textile equipment, Indicates Production in historical time periods, represents the critical yield, Indicates The average rate of the historical time period, represents the critical rate, Indicates The operating energy consumption of each historical period, represents the critical operating energy consumption, Indicates the production data flow rate impact factor corresponding to the set output, Indicates the production data flow rate impact factor corresponding to the set average rate, Indicates the impact factor of production data flow rate corresponding to the set operating energy consumption.

[0015] The second aspect of the present invention provides an abnormality detection device for textile equipment, comprising: a processor and a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; when the processor is running, it calls a computer program from the non-volatile memory through the network interface and runs the computer program through the memory to execute any one of the methods described above.

[0016] A third aspect of the present invention provides a storage medium for abnormality detection of textile equipment, on which a computer program is stored, and when the program is executed by a processor, the corresponding steps involved in any of the methods described above are implemented.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides a method, device and storage medium for detecting abnormalities of textile equipment. By collecting characterization data and production data during the operation of the textile equipment, the present invention can accurately reflect the operating status of the textile equipment, realize real-time monitoring during the operation of the textile equipment, and help to promptly handle abnormal situations occurring in the textile equipment.

[0018] (2) The present invention can monitor and analyze the characterization data of textile equipment to immediately detect abnormal conditions in the operation of the equipment. Through immediate warning, measures can be taken quickly to avoid production interruptions and losses caused by equipment failure. At the same time, the fault can be accurately located by combining the operation history and current status of the equipment, which helps to quickly find the fault point, reduce troubleshooting time, and improve maintenance efficiency. Real-time characterization data analysis can also help to promptly discover safety hazards of equipment and ensure safety and stability in the production process.

[0019] (3) The present invention can immediately reflect abnormal conditions in the production process by monitoring and analyzing product data of textile equipment, trace each step of the production process, help to quickly find the root cause of the problem, reduce troubleshooting time, and take measures to solve the problem to avoid further expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0021] Figure 1It is a schematic diagram of the method flow of the present invention.

[0022] Figure 2 Schematic diagram of the functional relationship between temperature and abnormality assessment value involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] Reference Figure 1 As shown, the first aspect of the present invention provides a method for detecting abnormalities of textile equipment, including: collecting basic characterization data of the textile equipment, and processing to obtain a characterization abnormality evaluation value of the textile equipment.

[0025] Specifically, several data monitoring points are deployed to obtain basic characterization data at each data monitoring point, including the temperature and vibration frequency of the textile equipment at each data monitoring point, and a comprehensive analysis is performed to obtain the characterization abnormality assessment value of the textile equipment.

[0026] In a specific embodiment, the characteristic abnormality evaluation value of the textile equipment is specifically expressed as: ; In the formula, is the number of each data monitoring point, , is the total number of data monitoring points, Indicates the characteristic abnormal evaluation value of textile equipment, Indicates The temperature of each data monitoring point, Indicates standard temperature, Indicates the set allowable deviation temperature. Indicates The vibration frequency of each data monitoring point, Indicates the standard vibration frequency, Indicates the set allowable deviation vibration frequency, Indicates the abnormal impact factor corresponding to the set temperature, Indicates the abnormal impact factor corresponding to the set vibration frequency.

[0027] In this embodiment, the value range of the abnormality influence factor corresponding to the preset temperature and vibration frequency obtained from the textile database is [0,1]. Through the relationship between the temperature and vibration frequency in the historical data and the abnormality evaluation value, a mapping set of temperature and vibration frequency and their corresponding abnormality influence factors is constructed. The real-time temperature and vibration frequency are input, and the abnormality influence factor corresponding to the temperature and the abnormality influence factor corresponding to the vibration frequency are obtained from the mapping table.

[0028] It should be understood that the standard temperature, the allowable deviation temperature, the standard vibration frequency and the allowable deviation vibration frequency are directly obtained from the textile database.

[0029] It should be understood that the abnormal evaluation value is a value calculated by combining the temperature, vibration frequency and its deviation from the standard value, with the weight or influencing factor corresponding to each parameter, and is used to quantitatively evaluate the abnormal state of the textile equipment.

[0030] It should be understood that changes in temperature and vibration frequency will directly affect the evaluation parameters characterizing abnormalities. If the temperature deviates from the standard value or the vibration frequency exceeds the normal range, these deviations will be weighted and added to the evaluation parameters respectively. If the temperature deviation or the vibration frequency deviation increases, the value of the evaluation parameter characterizing abnormalities will increase, indicating that the textile equipment is experiencing an abnormal state.

[0031] like Figure 2 As shown, the horizontal axis represents the temperature of each data monitoring point, in degrees Celsius, and the vertical axis represents the abnormal evaluation value of the textile equipment. In a specific embodiment, =25℃, =5℃, =50Hz, =10Hz, = =1, when =10℃, the functional relationship between temperature and abnormal evaluation value is shown in curve a. = 8℃, the functional relationship between temperature and abnormal evaluation value is shown in curve b. =6°C, the functional relationship between temperature and the abnormal evaluation value is shown in curve c.

[0032] Table 1 Example of data on abnormal evaluation values ​​of textile equipment

[0033] As shown in Table 1, the characterization abnormality assessment value is determined by temperature and vibration frequency. The more the temperature and vibration frequency deviate from the standard value, the larger the characterization abnormality assessment value is, indicating that the textile equipment is experiencing an abnormality. It should be understood that, in order to simplify the calculation, it is assumed that there are 5 groups of data monitoring points, that is, n=5, and the values ​​of each group of data monitoring points are the same, and the characterization abnormality assessment value of the textile equipment is calculated.

[0034] The formula comprehensively considers two key parameters, temperature and vibration frequency, to evaluate the abnormal state of textile equipment. It is more comprehensive and accurate than single parameter evaluation. and The value of can flexibly set the weights of different parameters in the evaluation according to actual conditions to adapt to different application scenarios and needs. The deviations of temperature and vibration frequency are normalized so that data of different dimensions and ranges can be compared and calculated in the same framework, which enhances the versatility and robustness. The hyperbolic tangent function is used as the output function to limit the evaluation value to the interval [0,1], which is convenient for understanding and interpreting the evaluation results. By real-time monitoring and evaluating the operating status of textile equipment, early warning information can be provided before abnormal conditions occur in the equipment, which helps to take timely measures to avoid the occurrence or expansion of failures.

[0035] In a specific embodiment, the present invention can more accurately reflect the operating status and production efficiency of the equipment, detect abnormal situations in time, and perform early warning processing by analyzing the basic characterization data of textile equipment, production data flow rate and output quality.

[0036] The production data of the textile equipment is obtained, and the production data flow rate assessment value of the textile equipment is obtained after processing. The output quality of the textile equipment is tested and the quality inspection qualified assessment value of the textile equipment is obtained after processing. According to the production data flow rate assessment value and the quality inspection qualified assessment value of the textile equipment, a comprehensive analysis is performed to obtain the output stability assessment value of the textile equipment.

[0037] Specifically, the time periods are divided according to the preset time period and marked as historical time periods. The output, average rate and operating energy consumption of the textile equipment in each historical time period are obtained, and the production data flow rate evaluation value of the textile equipment is obtained through comprehensive analysis.

[0038] In a specific embodiment, the production data flow rate evaluation value of the textile equipment is specifically expressed as follows: ; In the formula, is the number of each historical time period, , is the total number of historical time periods, Indicates the estimated value of the production data flow rate of textile equipment, Indicates Production in historical time periods, represents the critical yield, Indicates The average rate of the historical period, represents the critical rate, Indicates The operating energy consumption of each historical period, represents the critical operating energy consumption, Indicates the production data flow rate impact factor corresponding to the set output, Indicates the production data flow rate impact factor corresponding to the set average rate, Indicates the impact factor of production data flow rate corresponding to the set operating energy consumption.

[0039] It should be understood that the critical output, critical rate and critical operating energy consumption are directly obtained from the textile database.

[0040] In this embodiment, the value range of the production data flow rate influencing factor corresponding to the preset output, average rate and operating energy consumption obtained from the textile database is [0,1]. Through the relationship between the output, average rate and operating energy consumption in the historical data and the production data flow rate evaluation value, a mapping set of the output, average rate and operating energy consumption and their corresponding production data flow rate influencing factors is constructed, and the real-time output, average rate and operating energy consumption are input to obtain the production data flow rate influencing factor corresponding to the output, the production data flow rate influencing factor corresponding to the average rate and the production data flow rate influencing factor corresponding to the operating energy consumption from the mapping table.

[0041] It should be understood that the production data flow rate of textile equipment covers output, average rate and operating energy consumption, which measures the production efficiency of production equipment within a certain period of time. The output reflects the actual production volume, the average rate represents the operating speed of the equipment, and the operating energy consumption reflects the energy consumption in the production process.

[0042] It should be understood that if the output increases and this increase exceeds the critical output, it will push up the production data flow rate assessment value, indicating that the increase in output helps to speed up the flow of production data and improve production efficiency. Conversely, it will lead to a decrease in the production data flow rate assessment value, reflecting a decrease in production efficiency.

[0043] It should be understood that if the average rate increases and exceeds the critical rate, it will have a positive impact on the production data flow rate evaluation value, indicating that the increase in equipment operating speed has accelerated the flow of production data. If the average rate decreases and is lower than the critical rate, it will cause the production data flow rate evaluation value to decrease, reflecting a decrease in equipment operating efficiency.

[0044] It should be understood that if the operating energy consumption increases and exceeds the critical operating energy consumption, the production data flow rate evaluation value rises, indicating that the increase in row energy consumption helps to speed up the flow of production data. If the operating energy consumption decreases and is lower than the critical operating energy consumption, it indicates that the reduction in row energy consumption reduces the flow of production data.

[0045] The formula comprehensively considers three key factors: output, average rate and operating energy consumption. It can more comprehensively and accurately evaluate the production data flow rate of textile equipment and reflect the overall production efficiency of the equipment. By setting different influencing factors, the influence of each factor on the evaluation value can be flexibly adjusted according to actual conditions, so that the evaluation results are more in line with actual needs. By calculating the evaluation values ​​of multiple historical time periods, comparative analysis can be carried out to understand the production efficiency change trend of the equipment in different time periods, and provide data support for equipment management and maintenance.

[0046] Specifically, the fabric density, color fastness and strength of each textile produced by the textile equipment in each historical time period are obtained, and a comprehensive analysis is performed to obtain the quality inspection qualification assessment value of the textile equipment.

[0047] It should be understood that the strength of a textile refers to the tensile strength.

[0048] In a specific embodiment, the quality inspection qualification evaluation value of the textile equipment is specifically expressed as follows: ; In the formula, is the number of each historical time period, , is the total number of historical time periods, is the number of each textile, , is the total amount of textiles, Indicates the quality inspection qualification evaluation value of textile equipment. represents the textile density of the jth textile in the tth historical period, Indicates standard textile density, Indicates the set allowable textile density deviation, represents the color fastness of the jth textile in the tth historical period, Indicates standard color fastness, Indicates the set allowable color fastness deviation, represents the strength of the jth textile in the tth historical period, Indicates standard strength, Indicates the set allowable strength deviation, Indicates the quality inspection qualification factor corresponding to the set textile density, Indicates the quality inspection qualification factor corresponding to the set color fastness, Indicates the quality inspection pass influence factor corresponding to the set intensity.

[0049] In this embodiment, the value range of the quality inspection qualified influencing factors corresponding to the preset textile density, color fastness and strength obtained from the textile database is [0,1]. Through the relationship between the textile density, color fastness and strength in the historical data and the quality inspection qualified evaluation value, a mapping set of textile density, color fastness and strength and their corresponding quality inspection qualified influencing factors is constructed, and the real-time textile density, color fastness and strength are input to obtain the quality inspection qualified influencing factors corresponding to the textile density, the quality inspection qualified influencing factors corresponding to the color fastness and the quality inspection qualified influencing factors corresponding to the strength from the mapping table.

[0050] It should be understood that the quality inspection and acceptance assessment value of textile equipment is calculated by comprehensively considering key quality parameters, including textile density, color fastness and strength of textiles. The deviations between each of them and the set standard values ​​are quantified and standardized, and different quality inspection and acceptance influencing factors are assigned to reflect their relative importance.

[0051] It should be understood that the closer the textile density is to the standard value, the smaller the negative impact on the quality inspection qualification evaluation value will be, conversely, the greater the deviation, the lower the evaluation value will be, indicating a decrease in the quality inspection qualification; the closer the color fastness is to the standard value, the higher the quality inspection qualification evaluation value will be, conversely, the greater the deviation, the lower the quality inspection qualification evaluation value will be; the closer the strength is to the standard value, the higher the quality inspection qualification evaluation value will be, conversely, the greater the deviation, the lower the quality inspection qualification evaluation value will be.

[0052] The formula comprehensively considers the textile density, color fastness and strength of the textile, and gives a comprehensive quality inspection qualified evaluation value. By adjusting the influencing factors, the weights of different quality indicators in the evaluation can be adjusted according to actual needs, so that the evaluation is more in line with the actual situation. By comparing the actual deviation with the allowable deviation, the results are standardized, so that different quality indicators can be compared and weighted. The natural logarithm function is used. When the comprehensive deviation is large, The value increases rapidly, thus increasing the sensitivity of the evaluation results to quality deviations.

[0053] Specifically, based on the production data flow rate assessment value and quality inspection qualification assessment value of the textile equipment, a comprehensive analysis is performed to obtain the output stability assessment value of the textile equipment; In a specific embodiment, the output stability evaluation value of the textile equipment is specifically expressed as: ; In the formula, Indicates the output stability evaluation value of textile equipment. Indicates the estimated value of the production data flow rate of textile equipment, Indicates the quality inspection qualification evaluation value of textile equipment. Indicates the output stability impact factor corresponding to the set production data flow rate assessment value, Indicates the output stability influencing factor corresponding to the set quality inspection qualified evaluation value.

[0054] In this embodiment, the preset production data flow rate assessment value and the output stability influencing factor corresponding to the production data flow rate assessment value are obtained from the textile database, and the value range is [0,1]. Through the relationship between the production data flow rate assessment value and the quality inspection qualified assessment value in the historical data and the output stability assessment value, a mapping set of the production data flow rate assessment value and the quality inspection qualified assessment value and their corresponding output stability influencing factors is constructed, and the real-time production data flow rate assessment value and the quality inspection qualified assessment value are input. The output stability influencing factor corresponding to the production data flow rate assessment value and the output stability influencing factor corresponding to the quality inspection qualified assessment value are obtained from the mapping table.

[0055] It should be understood that if the production data flow rate evaluation value increases, it means that the data processing capacity and efficiency of the textile equipment in the production process are improved; if the production data flow rate evaluation value decreases, it means that the data processing capacity and efficiency of the textile equipment in the production process are reduced; if the quality inspection qualification evaluation value increases, it means that the output stability evaluation value becomes larger, and the quality qualification rate of the products produced by the textile equipment is improved; if the quality inspection qualification evaluation value decreases, it means that the output stability evaluation value becomes smaller, and the quality qualification rate of the products produced by the textile equipment is reduced.

[0056] The formula comprehensively considers the evaluation values ​​of production data flow rate and quality inspection pass, and can more comprehensively reflect the output stability of textile equipment. This comprehensive evaluation method is more accurate and reliable than a single indicator evaluation. The use of hyperbolic sine function for nonlinear transformation can make the evaluation results more sensitive to changes in the evaluation value. When the evaluation value changes slightly, the output stability evaluation value may also change significantly, thereby improving the accuracy and sensitivity of the evaluation. By adjusting the output stability influencing factor, the evaluation model can be flexibly adjusted according to different production needs and quality control requirements, making the formula more adaptable and practical.

[0057] A comprehensive analysis is conducted on the characterization abnormality assessment value and output stability assessment value of the textile equipment to obtain the equipment health assessment index of the textile equipment, and equipment abnormality early warning control is performed based on the equipment health assessment index of the textile equipment.

[0058] Specifically, based on the characteristic abnormality assessment value and output stability assessment value of the textile equipment, a comprehensive analysis is performed to obtain the equipment health assessment index of the textile equipment.

[0059] Specifically, the equipment health assessment index threshold of the textile equipment is extracted from the textile database, and the equipment health assessment index is compared with the equipment health assessment index threshold. If the equipment health assessment index is greater than or equal to the equipment health assessment index threshold, it indicates that there is no abnormality in the textile equipment. If the equipment health assessment index is less than the equipment health assessment index threshold, it indicates that the textile equipment is abnormal, and an equipment abnormality warning is issued.

[0060] In a specific embodiment, the equipment health assessment index of the textile equipment is specifically expressed as: ; In the formula, Represents the equipment health assessment index of textile equipment, Indicates the characteristic abnormal evaluation value of textile equipment, Indicates the output stability evaluation value of textile equipment. Indicates the equipment health impact factor corresponding to the set abnormal assessment value. Indicates the equipment health impact factor corresponding to the set output stability assessment value.

[0061] In this embodiment, the value range of the equipment health impact factor corresponding to the preset characterization abnormality assessment value and the output stability assessment value obtained from the textile database is [0,1]. Through the relationship between the characterization abnormality assessment value and the output stability assessment value in the historical data and the equipment health assessment index, a mapping set of the characterization abnormality assessment value and the output stability assessment value and their corresponding equipment health impact factors is constructed, and the real-time characterization abnormality assessment value and the output stability assessment value are input to obtain the equipment health impact factor corresponding to the characterization abnormality assessment value and the equipment health impact factor corresponding to the output stability assessment value from the mapping table.

[0062] It should be understood that the equipment health assessment index is obtained by comprehensive analysis of the characterization abnormality assessment value and output stability assessment value of the textile equipment. It can quantitatively reflect the overall health status of the equipment and provide strong support for equipment management and maintenance.

[0063] It should be understood that if the abnormal evaluation value increases, it means that the number of abnormal phenomena in the equipment has increased, resulting in a decrease in the equipment health evaluation index, indicating that the health status of the equipment has deteriorated. If the abnormal evaluation value decreases, it means that the equipment is running smoothly and the abnormal phenomena have decreased, which will cause the equipment health evaluation index to increase, indicating that the health status of the equipment has improved.

[0064] It should be understood that if the output stability assessment value increases, it means that the device shows higher stability and consistency when outputting products, resulting in an increase in the equipment health assessment index, indicating that the equipment is in good health. If the output stability assessment value decreases, it means that there are large fluctuations and instability factors in the device when outputting products, resulting in a decrease in the equipment health assessment index, indicating that the equipment is in poor health.

[0065] The formula comprehensively considers the abnormality evaluation value and the output stability evaluation value, and provides a comprehensive and integrated equipment health evaluation index. This comprehensive evaluation method can more accurately reflect the overall health status of the equipment than a single indicator. By introducing influencing factors, the weights of these two evaluation values ​​in the final evaluation index can be flexibly adjusted according to the characteristics and importance of different equipment. The hyperbolic secant function is used as the conversion function to make the change of the evaluation index smoother and continuous. The hyperbolic secant function has a faster rate of change near the origin, and the rate of change gradually decreases when it is far away from the origin, which helps to maintain the stability of the evaluation index when the evaluation value changes greatly, and maintain a higher sensitivity when the evaluation value changes slightly.

[0066] In a specific embodiment, the present invention combines the abnormality evaluation value and the output stability evaluation value to obtain an equipment health evaluation index through comprehensive analysis, which can more comprehensively reflect the overall health status of textile equipment, including the abnormal state of the equipment itself, and also taking into account the stability of the production process and product quality.

[0067] The second aspect of the present invention provides an abnormality detection device for textile equipment, comprising: a processor and a memory and a network interface connected to the processor; the network interface is connected to a non-volatile memory in a server; when the processor is running, it calls a computer program from the non-volatile memory through the network interface and runs the computer program through the memory to execute any one of the methods described above.

[0068] A third aspect of the present invention provides a storage medium for abnormality detection of textile equipment, on which a computer program is stored, and when the program is executed by a processor, the corresponding steps involved in any of the methods described above are implemented.

[0069] In a specific embodiment, the present invention provides an abnormality detection method, device and storage medium for textile equipment, and by collecting characterization data and production data during the operation of the textile equipment, it can accurately reflect the operating status of the textile equipment, realize real-time monitoring during the operation of the textile equipment, and help to promptly handle abnormal situations that occur in the textile equipment.

[0070] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for detecting abnormality of textile equipment, characterized in that: include: Collect basic characterization data of textile equipment and process them to obtain characterization abnormality evaluation values ​​of textile equipment; Acquire the production data of the textile equipment, obtain the production data flow rate assessment value of the textile equipment through processing, and detect the output quality of the textile equipment to obtain the quality inspection qualified assessment value of the textile equipment through processing, and obtain the output stability assessment value of the textile equipment through comprehensive analysis based on the production data flow rate assessment value and the quality inspection qualified assessment value of the textile equipment; A comprehensive analysis is conducted on the characterization abnormality assessment value and output stability assessment value of the textile equipment to obtain the equipment health assessment index of the textile equipment, and equipment abnormality early warning control is performed based on the equipment health assessment index of the textile equipment.

2. The abnormality detection method for textile equipment according to claim 1, characterized in that: The processing obtains the abnormality evaluation value of the textile equipment, and the specific process is as follows: Deploy several data monitoring points to obtain basic characterization data at each data monitoring point, including the temperature and vibration frequency of the textile equipment at each data monitoring point, and conduct comprehensive analysis to obtain the characterization abnormality assessment value of the textile equipment.

3. The abnormality detection method for textile equipment according to claim 1, characterized in that: The production data flow rate evaluation value of the textile equipment is obtained through processing, and the specific process is as follows: The time periods are divided according to the preset time period and marked as historical time periods. The output, average rate and operating energy consumption of the textile equipment in each historical time period are obtained, and the production data flow rate evaluation value of the textile equipment is obtained through comprehensive analysis.

4. The abnormality detection method for textile equipment according to claim 1, characterized in that: The quality inspection qualified evaluation value of the textile equipment is obtained through processing, and the specific process is: The fabric density, color fastness and strength of each textile produced by the textile equipment in each historical time period are obtained, and the quality inspection qualification evaluation value of the textile equipment is obtained through comprehensive analysis.

5. The abnormality detection method for textile equipment according to claim 1, characterized in that: The equipment health assessment index of the textile equipment, the specific process is: Based on the production data flow rate assessment value and quality inspection qualification assessment value of textile equipment, the output stability assessment value of textile equipment is obtained through comprehensive analysis; According to the characteristic abnormality assessment value and output stability assessment value of textile equipment, the equipment health assessment index of textile equipment is obtained through comprehensive analysis.

6. The abnormality detection method for textile equipment according to claim 5, characterized in that: The specific process of the equipment abnormality early warning control is as follows: The equipment health assessment index threshold of textile equipment is extracted from the textile database, and the equipment health assessment index is compared with the equipment health assessment index threshold. If the equipment health assessment index is greater than or equal to the equipment health assessment index threshold, it indicates that there is no abnormality in the textile equipment. If the equipment health assessment index is less than the equipment health assessment index threshold, it indicates that the textile equipment is abnormal, and an equipment abnormality warning is issued.

7. The abnormality detection method for textile equipment according to claim 2, characterized in that: The specific numerical expression of the abnormal evaluation value of the textile equipment is: ； In the formula, is the number of each data monitoring point, , is the total number of data monitoring points, Indicates the characteristic abnormal evaluation value of textile equipment, Indicates The temperature of each data monitoring point, Indicates standard temperature, Indicates the set allowable deviation temperature. Indicates The vibration frequency of each data monitoring point, Indicates the standard vibration frequency, Indicates the set allowable deviation vibration frequency, Indicates the abnormal impact factor corresponding to the set temperature, Indicates the abnormal impact factor corresponding to the set vibration frequency.

8. The abnormality detection method for textile equipment according to claim 3, characterized in that: The production data flow rate evaluation value of the textile equipment is specifically expressed as: ； In the formula, is the number of each historical time period, , is the total number of historical time periods, Indicates the estimated value of the production data flow rate of textile equipment, Indicates Production in historical time periods, represents the critical yield, Indicates The average rate of the historical period, represents the critical rate, Indicates The operating energy consumption of each historical period, represents the critical operating energy consumption, Indicates the production data flow rate impact factor corresponding to the set output, Indicates the production data flow rate impact factor corresponding to the set average rate, Indicates the impact factor of the production data flow rate corresponding to the set operating energy consumption.

9. An abnormality detection device for textile equipment, characterized in that: include: Processor and memory and network interfaces connected to the processor; The network interface is connected to a non-volatile memory in the server; When running, the processor retrieves a computer program from the non-volatile memory through the network interface, and runs the computer program through the memory to execute the method described in any one of claims 1 to 8.

10. A storage medium for detecting abnormality of textile equipment, on which a computer program is stored, characterized in that: When the program is executed by a processor, the corresponding steps involved in the method according to any one of claims 1 to 8 are implemented.

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