Online monitoring system for special steel equipment

Abstract

The invention relates to the technical field of online monitoring of equipment, and particularly discloses an online monitoring system for special steel equipment, which comprises a data acquisition module, a load data evaluation module, an operation data evaluation module, a correlation analysis module and an adjustment module, the data acquisition module is responsible for acquiring load data and operation data of each special steel device during working; the load data evaluation module processes the load data to obtain a load performance value, and compares the load performance value with a preset threshold to generate a load signal; the operation data evaluation module processes the operation data to obtain an operation score, and compares the operation score with a preset threshold to generate an operation signal; the correlation analysis module is used for analyzing the correlation between the load performance value and the operation score and judging whether an influence relationship exists between the load performance value and the operation score; and the adjustment module simulates the historical data to obtain a normal operation data interval of the special steel equipment, and adjusts the current operation data according to the historical operation data interval to ensure the stability and safety of the special steel equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment online monitoring, and in particular to an online monitoring system for special steel equipment. Background Art

[0002] With the continuous development of industrial production, special steel as an important raw material is widely used in many fields such as automobile manufacturing, aerospace, petrochemicals, etc. Special steel equipment is subjected to high-intensity workloads during the production process, and its operating status is directly related to production efficiency and product quality. However, traditional special steel equipment monitoring methods often rely on manual inspections and post-maintenance, which is not only inefficient, but also difficult to detect potential problems in a timely manner, which can easily lead to equipment failures and even safety accidents.

[0003] Most existing monitoring systems do not have the ability to monitor in real time and are unable to detect abnormal conditions of equipment in a timely manner; traditional methods rely on regular maintenance and inspections, lack effective prediction models, and are unable to provide early warning of possible equipment failures; traditional monitoring systems fail to fully utilize the large amount of data generated by the equipment and are unable to conduct a comprehensive and in-depth analysis of the equipment's status; frequent equipment failures lead to high maintenance costs and production delays, affecting the company's economic benefits.

[0004] Existing technologies lack the ability to collect and process equipment load and operation data in real time, analyze data to evaluate the equipment's operating status, and detect anomalies in a timely manner; provide predictive maintenance recommendations, reduce failure rates and maintenance costs, and optimize equipment operating parameters in a data-driven manner to improve production efficiency and safety. Summary of the invention

[0005] The object of the present invention is to provide an online monitoring system for special steel equipment, which mainly includes a data acquisition module, a load data evaluation module, an operation data evaluation module, a correlation analysis module and an adjustment module. The data acquisition module is responsible for obtaining the load data (including load times and load time) and operation data (including temperature and pressure values ​​when the equipment is running) of each special steel equipment at work. The load data evaluation module obtains a load performance value by processing the load data, and compares the value with a preset threshold to generate a load abnormality signal or a load normal signal. The operation data evaluation module obtains an operation score by processing the operation data, and then compares the operation score with a preset threshold to generate an operation abnormality signal or a normal operation signal. The correlation analysis module is used to analyze the correlation between the load performance value and the operation score, and to determine whether there is an influence relationship between the two. The adjustment module obtains the normal operation data interval of the special steel equipment by simulating historical data, and adjusts the current operation data according to the historical operation data interval, so as to maintain the stable operation of the equipment to solve the problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An online monitoring system for special steel equipment includes a data acquisition module, a load data evaluation module, an operation data evaluation module, a correlation analysis module and an adjustment module;

[0008] A data acquisition module, wherein the data acquisition module is used to obtain load data and operation data of each special steel equipment during operation;

[0009] The load data includes the load times and load time, and the operation data includes the temperature and pressure values ​​when the equipment is running;

[0010] A load data evaluation module, wherein the load data evaluation module obtains a load performance value by processing the load data, and compares the load performance value with a preset threshold value to generate a load abnormality signal and a load normal signal;

[0011] Operation data evaluation, wherein the operation data evaluation obtains an operation score by processing the operation data, compares the operation score with a preset threshold, and generates an operation abnormality signal and an operation abnormality signal;

[0012] A correlation analysis module, wherein the correlation analysis module is used to analyze the correlation between the load performance value and the operation score, and determine whether there is an influence relationship between the load performance value and the operation score;

[0013] The adjustment module obtains the normal operation data interval of the special steel equipment by simulating the historical data, and adjusts the current operation data according to the historical operation data interval.

[0014] As a further solution of the present invention: the process of obtaining the load performance value is as follows:

[0015] Obtain the load times and load time of each special steel equipment, divide the load times by the load reference times to obtain the load times ratio, divide the load time by the load reference time to obtain the load time ratio; add the load times ratio and the load time ratio to calculate the load performance value of each special steel equipment, where i represents the number of special steel equipment collected, i=1, 2,...n.

[0016] As a further solution of the present invention: the load reference number is: the total number of times the load signal appears in all special steel equipment collected in the historical time, and then the total number of times the load signal appears is divided by the number of special steel equipment to obtain the load reference number;

[0017] The reference time for use is: the total time collected during the historical period for all special steel equipment to complete their work, and then the total working time of the special steel equipment is divided by the number of special steel equipment to obtain the reference time for use.

[0018] As a further solution of the present invention: the process of obtaining the running score Qi is:

[0019] Process the temperature data and pressure data of each special steel equipment to obtain the temperature anomaly factor MTi and the pressure anomaly factor MFi;

[0020] Combine the temperature anomaly factor MTi and the pressure anomaly factor MFi with the corresponding weight factors W1 and W2 respectively;

[0021] The operation score Qi of each special steel equipment is calculated by the formula Qi=MTi*W1+MFi*W2.

[0022] As a further solution of the present invention: the process of obtaining the temperature anomaly factor MTi is as follows:

[0023] The real-time temperature value Tij of each special steel equipment is collected in real time according to a time series (such as every minute) through temperature sensing;

[0024] Wherein, i represents the number of special steel collection equipment, i=1, 2, ..., n; j represents the number of each special steel collection point, j=1, 2, ..., m;

[0025] Integrate the real-time temperature value Tij of each special steel equipment to obtain a temperature value group;

[0026] By formula Calculate the temperature anomaly factor MTi for each special steel equipment;

[0027] Among them, b1 and b2 are preset proportional factors, and b1 and b2 are both greater than 0, T i max is the maximum temperature value in the temperature value group, T imin is the minimum temperature value in the temperature value group, T iy is the average temperature value in the temperature value group, and αi is the standard deviation value of the temperature value group.

[0028] As a further solution of the present invention: the process of obtaining the pressure abnormality factor MFi is:

[0029] The real-time pressure F ij of each special steel equipment is collected in real time according to a time series (such as every minute) through pressure sensing;

[0030] Integrate the real-time pressure value F ij of each special steel equipment to obtain a pressure value group;

[0031] By formula The pressure abnormality factor MFi is calculated;

[0032] Wherein, b3 and b4 are preset proportional factors, and both b3 and b4 are greater than 0, Fiy is the average pressure value of the pressure value group, and Si is the variance value of the pressure value group.

[0033] As a further solution of the present invention: the process of obtaining the correlation coefficient ρ is:

[0034] Obtain the load performance value Ri of each special steel equipment, and obtain the operation score Qi of each special steel equipment;

[0035] Sort the load performance values ​​Ri and assign a ranking Ri′ to each load performance value Ri, sort the operation scores Qi and assign a ranking Qi′ to each operation score Qi;

[0036] Sort the running scores Qi and assign a rank Qi′ to each running score Qi;

[0037] The ranking difference di of each collection point is calculated by the formula di = |Ri′-Qi′|;

[0038] By formula The correlation coefficient ρ was calculated.

[0039] As a further solution of the present invention: the process of obtaining the normal pressure interval is:

[0040] By formula The pressure value fluctuation ratio WFy is calculated;

[0041] The normal pressure upper limit threshold Fs is calculated by the formula Fs=Fq*(1+WFy);

[0042] The normal pressure lower limit threshold Fm is calculated by the formula Fm=Fq*(1-WFy);

[0043] Among them, Fq is the preset optimal pressure value, which is usually set according to the staff's experience, Fy is the comprehensive historical average pressure value, Fymax is the historical average maximum pressure value, and Fymin is the historical average minimum pressure value;

[0044] Then the normal pressure range is [Fm, Fs].

[0045] As a further solution of the present invention: the process of obtaining the normal temperature range is:

[0046] By formula The temperature fluctuation ratio TFy is calculated;

[0047] The normal temperature upper limit threshold Fs is calculated by the formula Ts=Tq*(1+TFy);

[0048] The normal temperature lower limit threshold Tm is calculated by the formula Tm=Tq*(1-TFy);

[0049] Among them, Tq is the preset optimal steel pipe temperature value, which is usually set according to the staff's experience, Ty is the comprehensive historical average temperature value, Tymax is the maximum average temperature, and Ty is the minimum average temperature;

[0050] Then the normal temperature range is [Tm, Ts].

[0051] As a further solution of the present invention: according to the normal temperature interval [Tm, Ts], the temperature of the special steel equipment is adjusted within the normal temperature interval [Tm, Ts];

[0052] According to the normal pressure range [Fm, Fs], adjust the pressure of the special steel equipment and keep it within the normal pressure range [Fm, Fs].

[0053] After the adjustment is completed, the load performance value and operation score are recalculated to determine whether the abnormal state of the special steel equipment after the adjustment has been resolved. If the abnormality still occurs, the external influencing factors of the special steel equipment are analyzed.

[0054] Beneficial effects of the present invention:

[0055] (1) Through the real-time data acquisition module, the system can continuously monitor the load data (including load times and load time) and operation data (including temperature and pressure values ​​during equipment operation) of special steel equipment; these data are crucial for understanding the working status of the equipment; the load data evaluation module can calculate the load performance value and compare it with the preset threshold value, so as to quickly identify whether the equipment has an overload risk; this helps to prevent failures caused by overload, reduce unplanned downtime, and improve production efficiency; at the same time, the operation data evaluation module can accurately evaluate the equipment operation status and determine whether the equipment operation is stable by calculating the operation score; when the operation score exceeds the preset threshold, the system will issue an operation abnormality signal to prompt the operator to take timely measures to prevent possible safety accidents; in summary, the present invention can achieve comprehensive monitoring of special steel equipment through intelligent means, ensure its safe operation in an efficient state, reduce losses caused by equipment failures, and improve the reliability of the overall production line;

[0056] (2) The present invention can deeply explore the relationship between the load performance value and the operation score through the correlation analysis module, and determine whether the two have an impact on each other. This analysis capability is crucial to understanding the health status of the equipment; for example, if it is found that there is an obvious positive correlation between the load performance value of the equipment and the operation score, then this means that the equipment is more likely to have abnormal operation when it is under a higher load; based on this discovery, the system can simulate historical normal operation data through the adjustment module, determine a reasonable temperature and pressure range, and dynamically adjust the operating parameters of the equipment; in this way, not only can the damage caused by overheating or excessive pressure of the equipment be reduced, but also the service life of the equipment can be effectively extended; in addition, by regularly checking the working status of the equipment and performing necessary maintenance according to actual conditions, the maintenance cost of the equipment can be further reduced, and unnecessary expenses can be reduced. The present invention can not only help the factory to achieve refined management of equipment, but also minimize the equipment failure rate through preventive maintenance strategies, ensure the long-term stable operation of special steel equipment, and thus bring long-term economic and social benefits to the factory;

[0057] (3) By real-time monitoring of the load and operation data of special steel equipment, the system can generate detailed analysis reports to help managers quickly identify potential problems and respond in a timely manner; for example, when an abnormal signal appears in the equipment, the system will automatically trigger an alarm mechanism to notify relevant personnel to conduct inspections and repairs, thereby avoiding production interruptions caused by equipment failures; in addition, through in-depth analysis of historical data, the trend of equipment performance changes over time can be discovered, which is helpful for formulating a more scientific maintenance plan; the adjustment module mentioned in the embodiment can simulate the normal operating range based on historical data to ensure that the equipment operates in the best state; this plays an important role in improving production efficiency, reducing costs, and improving product quality; in addition, through continuous monitoring and analysis, managers can better understand the working principle of the equipment and provide strong data support for future equipment selection and upgrading; therefore, the present invention not only improves the reliability and production efficiency of special steel equipment, but also improves the management level of the factory by providing rich data support, promotes the intelligence and automation of the entire production process, and lays a solid foundation for the sustainable development of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below in conjunction with the accompanying drawings.

[0059] Figure 1 It is a flow chart of an online monitoring system for special steel equipment of the present invention;

[0060] Figure 2 It is a flowchart of comparing the load performance value with the preset threshold value in the present invention;

[0061] Figure 3It is a flowchart of comparing the running score with the preset threshold in the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] See also Figure 1 As shown, the present invention is an online monitoring system for special steel equipment, including a data acquisition module, a load data evaluation module, an operation data evaluation, a correlation analysis module and an adjustment module;

[0064] A data acquisition module, wherein the data acquisition module is used to obtain load data and operation data of each special steel equipment during operation;

[0065] The load data includes the load times and load time, and the operation data includes the temperature and pressure values ​​when the equipment is running;

[0066] A load data evaluation module, wherein the load data evaluation module obtains a load performance value by processing the load data, and compares the load performance value with a preset threshold value to generate a load abnormality signal and a load normal signal;

[0067] Operation data evaluation, wherein the operation data evaluation obtains an operation score by processing the operation data, compares the operation score with a preset threshold, and generates an operation abnormality signal and an operation abnormality signal;

[0068] A correlation analysis module, wherein the correlation analysis module is used to analyze the correlation between the load performance value and the operation score, and determine whether there is an influence relationship between the load performance value and the operation score;

[0069] The adjustment module obtains the normal operation data interval of the special steel equipment by simulating the historical data, and adjusts the current operation data according to the historical operation data interval.

[0070] Example 2

[0071] See also Figure 2As shown, on the basis of Example 1, the load data of each special steel equipment during operation is obtained, wherein the load data includes load times and load time; the load times and load time of each special steel equipment are obtained, the load times are divided by the load reference times to obtain the load times ratio, and the load time is divided by the load reference time to obtain the load time ratio; the load times ratio and the load time ratio are added and summed to calculate the load performance value of each special steel equipment, wherein i represents the number of special steel equipment collected, i=1, 2, ... n;

[0072] comparing the load performance value with a preset load performance value threshold;

[0073] If the load performance value is greater than or equal to the preset load performance value threshold, it means that the load state of the corresponding special steel equipment is abnormal, and a load abnormality signal is generated;

[0074] If the load performance value is less than the preset load performance value threshold, it means that the load state of the corresponding special steel equipment is normal, and a load normal signal is generated;

[0075] It should be noted that: the load reference number is: the total number of times load signals appear in all special steel equipment collected within the historical time, and then the total number of times load signals appear is divided by the number of special steel equipment to obtain the load reference number; the usage reference time is: the total time it takes for all special steel equipment collected within the historical time to complete the work, and then the total working time of the special steel equipment is divided by the number of charging piles to obtain the usage reference time.

[0076] Example 3

[0077] See also Figure 3 As shown, on the basis of Example 2, based on the load abnormality signal, since the abnormal operation data of the special steel equipment when working will cause the load abnormality of the special steel equipment when working, it is analyzed and judged whether the equipment load abnormality is related to the operation data of the special steel equipment when it is used when the special steel equipment is working. If there is an influence relationship, it is convenient to adjust the operation data of the special steel equipment in time when the special steel equipment is used in the subsequent use, reduce the influence of the operation data on the stability of the special steel equipment, effectively maintain the load stability of the special steel equipment when it is used, and then improve the safety of the special steel equipment;

[0078] Among them, the operation data of special steel equipment includes: temperature data and pressure data during the operation of special steel equipment;

[0079] Specific:

[0080] Install temperature sensors and pressure sensors on special steel equipment and calibrate them to ensure the accuracy of collected data;

[0081] The real-time temperature value Tij of each special steel equipment is collected in real time according to a time series (such as every minute) through temperature sensing;

[0082] Wherein, i represents the number of special steel collection equipment, i=1, 2, ..., n; j represents the number of each special steel collection point, j=1, 2, ..., m;

[0083] Integrate the real-time temperature value Tij of each special steel equipment to obtain a temperature value group;

[0084] Get the maximum temperature value T imax in the temperature value group;

[0085] Get the minimum temperature value Timin in the temperature value group;

[0086] The average temperature value Tiy of the temperature value group is calculated by the mean calculation formula;

[0087] The standard deviation value αi within the temperature value group is calculated by the standard deviation calculation formula;

[0088] By formula Calculate the temperature anomaly factor MTi for each special steel equipment;

[0089] Wherein, b1 and b2 are preset scaling factors, and both b1 and b2 are greater than 0.

[0090] The real-time pressure F ij of each special steel equipment is collected in real time according to a time series (such as every minute) through pressure sensing;

[0091] Integrate the real-time pressure value F ij of each special steel equipment to obtain a pressure value group;

[0092] The average pressure value F iy of the pressure value group is calculated by the mean calculation formula;

[0093] The variance value Si of the pressure value group is calculated by the variance calculation formula;

[0094] By formula The pressure abnormality factor MFi is calculated;

[0095] Among them, b3 and b4 are preset proportional factors, and b3 and b4 are both greater than 0.

[0096] The weight of the temperature anomaly factor MTi is assigned to W1, and the weight of the pressure anomaly factor MFi is assigned to W2;

[0097] The operation score Qi of each special steel equipment is calculated by the formula Qi=MTi*W1+MFi*W2;

[0098] Compare the running score Qi with a preset running score threshold;

[0099] If the operation score Qi is greater than or equal to the preset operation score threshold, it means that the operation data of the special steel equipment is abnormal, and an operation abnormality signal is generated;

[0100] If the operation score Qi is less than the preset operation score threshold, it means that the operation data of the special steel equipment is normal, and a normal operation signal is generated;

[0101] It should be noted that the operation score is the equipment operation evaluation value under the combined influence of temperature and pressure during the operation of the special steel equipment. The larger the operation score, the more unstable the operation of the special steel equipment, and it needs to be dealt with in a timely manner to avoid safety accidents.

[0102] Example 4

[0103] Based on Embodiment 2 and Embodiment 3, the load performance value of each device is recorded as Ri;

[0104] Obtain the load performance value Ri of each special steel equipment, and obtain the operation score Qi of each special steel equipment;

[0105] Integrate the collected load performance values ​​Ri of all special steel equipment to obtain a load performance value group;

[0106] Sort the load performance values ​​Ri and assign a ranking Ri′ to each load performance value Ri;

[0107] Integrate the collected operation scores Qi of all special steel equipment to obtain an operation score group;

[0108] Sort the running scores Qi and assign a rank Qi′ to each running score Qi;

[0109] The ranking difference di of each collection point is calculated by the formula di = |Ri′-Qi′|;

[0110] By formula The correlation coefficient ρ is calculated;

[0111] Compare the correlation coefficient ρ with a preset correlation coefficient threshold;

[0112] If the correlation coefficient ρ is greater than or equal to the preset correlation coefficient threshold, it means that there is an influence relationship between the load performance value Ri and the operation score Qi, and an influence signal is generated;

[0113] If the correlation coefficient ρ is less than the preset correlation coefficient threshold, it means that there is no influence relationship between the load performance value Ri and the operation score Qi, and a non-influence signal is generated;

[0114] It should be noted that the correlation coefficient ρ is a criterion for determining whether there is a relationship between the load performance value Ri and the operation score Qi. The larger the correlation coefficient ρ, the higher the correlation between the two.

[0115] Example 5

[0116] On the basis of Example 4, based on the influencing signal, the operating data is adjusted so that the operating score is kept within the normal range, so that the load performance value is normal, and thus the special steel equipment is normal.

[0117] Specific:

[0118] Obtain h groups of historical normal special steel equipment pressure data, h = 1, 2, ..., n;

[0119] Each group of pressure data is calculated using the mean calculation formula to obtain the average pressure value Fhy of each group of data;

[0120] The average pressure values ​​Fhy of all groups are averaged to obtain the comprehensive average pressure value Fy;

[0121] Get the maximum average pressure value Fymax;

[0122] Get the minimum average pressure value Fymin;

[0123] By formula The pressure value fluctuation ratio WFy is calculated;

[0124] The normal pressure upper limit threshold Fs is calculated by the formula Fs=Fq*(1+WFy);

[0125] The normal pressure lower limit threshold Fm is calculated by the formula Fm=Fq*(1-WFy);

[0126] Among them, Fq is the preset optimal pressure value, which is usually set according to the experience of the staff;

[0127] Then the normal pressure range is [Fm, Fs];

[0128] Obtain h groups of historical normal temperature data of special steel equipment, h = 1, 2, ..., n;

[0129] Calculate each set of temperature data using the mean calculation formula to obtain the average temperature value Thy of each set of data;

[0130] Calculate the average of all average temperature values ​​Thy to obtain the comprehensive average temperature value Ty;

[0131] Get the maximum average temperature value Tymax;

[0132] Get the minimum average temperature value Tymin;

[0133] By formula The temperature fluctuation ratio TFy is calculated;

[0134] The normal temperature upper limit threshold Fs is calculated by the formula Ts=Tq*(1+TFy);

[0135] The normal temperature lower limit threshold Tm is calculated by the formula Tm=Tq*(1-TFy);

[0136] Among them, Tq is the preset optimal steel pipe temperature value, which is usually set according to the staff's experience;

[0137] Then the normal temperature range is [Tm, Ts];

[0138] According to the normal temperature range [Tm, Ts], adjust the temperature of the special steel equipment so that the temperature of the special steel equipment remains stable and within the normal temperature range [Tm, Ts] during operation;

[0139] According to the normal pressure range [Fm, Fs], adjust the pressure of the special steel equipment so that the pressure remains stable and within the normal pressure range [Fm, Fs] during the operation of the special steel equipment.

[0140] After the adjustment is completed, the load performance value and operation score are recalculated to determine whether the abnormal state of the special steel equipment after the adjustment has been resolved. If the abnormality still occurs, the external influencing factors of the special steel equipment are analyzed.

[0141] Working principle of the present invention: The present invention discloses an online monitoring system for special steel equipment, which mainly includes a data acquisition module, a load data evaluation module, an operation data evaluation module, a correlation analysis module and an adjustment module. The data acquisition module is responsible for obtaining the load data (including load times and load time) and operation data (including temperature and pressure values ​​when the equipment is running) of each special steel equipment when it is working. The load data evaluation module obtains a load performance value by processing the load data, and compares the value with a preset threshold to generate a load abnormality signal or a load normal signal. The operation data evaluation module obtains an operation score by processing the operation data, and then compares the operation score with a preset threshold to generate an operation abnormality signal or a normal operation signal. The correlation analysis module is used to analyze the correlation between the load performance value and the operation score, and to determine whether there is an influence relationship between the two. The adjustment module obtains the normal operation data interval of the special steel equipment by simulating historical data, and adjusts the current operation data according to the historical operation data interval, so as to maintain the stable operation of the equipment.

[0142] Specifically, in the embodiment, the load data evaluation module calculates the load performance value of each special steel equipment, obtains the load frequency ratio by dividing the load times by the load reference times, obtains the load time ratio by dividing the load time by the load reference time, and then adds the two ratios to finally obtain the load performance value. If the load performance value is greater than or equal to the preset load performance value threshold, it is considered that the load state of the corresponding special steel equipment is abnormal; otherwise, it is considered that the load state is normal.

[0143] In the embodiment, based on the load abnormality signal, the operating data of the special steel equipment during operation, namely the temperature data and pressure data, is further analyzed to determine whether the equipment load abnormality is related to the operating data. The temperature value and pressure value of the special steel equipment are collected in real time by installing a temperature sensor and a pressure sensor, and the temperature value group and the pressure value group are integrated. Then, by calculating the mean value, standard deviation (or variance) of the temperature value group and the pressure value group, as well as the temperature abnormality factor and the pressure abnormality factor, the operating score of each special steel equipment is finally obtained. If the operating score exceeds the preset threshold, the equipment operating data is considered abnormal; otherwise, the operating data is normal.

[0144] In the embodiment, the average values ​​of the load performance values ​​and the operation scores of all special steel equipment are calculated, and the correlation coefficient between the load performance values ​​and the operation scores is calculated using the correlation coefficient formula to determine whether there is an influence relationship between the two. If the correlation coefficient is greater than or equal to the preset correlation coefficient threshold, it is considered that there is an influence relationship between the two; otherwise, there is no influence relationship.

[0145] Finally, in the embodiment, based on the impact signal, by analyzing the temperature and pressure data under historical normal operating conditions, the normal operating temperature and pressure range is calculated, and then the operating data of the special steel equipment is adjusted to ensure that it works within the normal range. After the adjustment, the system will recalculate the load performance value and operation score to determine whether the abnormal state of the equipment has been resolved. If the abnormality still exists, it is necessary to further analyze the external influencing factors and take corresponding measures.

[0146] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A special steel equipment online monitoring system, characterized in that: It includes data acquisition module, load data evaluation module, operation data evaluation, correlation analysis module and adjustment module; A data acquisition module, wherein the data acquisition module is used to obtain load data and operation data of each special steel equipment during operation; The load data includes the load times and load time, and the operation data includes the temperature and pressure values ​​when the equipment is running; A load data evaluation module, wherein the load data evaluation module obtains a load performance value by processing the load data, and compares the load performance value with a preset threshold value to generate a load abnormality signal and a load normal signal; Operation data evaluation, wherein the operation data evaluation obtains an operation score by processing the operation data, compares the operation score with a preset threshold, and generates an operation abnormality signal and an operation abnormality signal; A correlation analysis module, wherein the correlation analysis module is used to analyze the correlation between the load performance value and the operation score, and determine whether there is an influence relationship between the load performance value and the operation score; The adjustment module obtains the normal operation data interval of the special steel equipment by simulating the historical data, and adjusts the current operation data according to the historical operation data interval.

2. The online monitoring system for special steel equipment according to claim 1, characterized in that: The process of obtaining the load performance value is as follows: Obtain the load times and load time of each special steel equipment, divide the load times by the load reference times to obtain the load times ratio, divide the load time by the load reference time to obtain the load time ratio; add the load times ratio and the load time ratio to calculate the load performance value of each special steel equipment, where i represents the number of special steel equipment collected, i=1, 2,...n.

3. A special steel equipment online monitoring system according to claim 2, characterized in that: The load reference times are: the total number of times the load signals of all special steel equipment collected in the historical time are generated, and then the total number of times the load signals are generated is divided by the number of special steel equipment to obtain the load reference times; The reference time for use is: the total time collected during the historical period for all special steel equipment to complete their work, and then the total working time of the special steel equipment is divided by the number of special steel equipment to obtain the reference time for use.

4. The online monitoring system for special steel equipment according to claim 1, characterized in that: The process of obtaining the running score Qi is: Process the temperature data and pressure data of each special steel equipment to obtain the temperature anomaly factor MTi and the pressure anomaly factor MFi; Combine the temperature anomaly factor MTi and the pressure anomaly factor MFi with the corresponding weight factors W1 and W2 respectively; The operation score Qi of each special steel equipment is calculated by the formula Qi=MTi*W1+MFi*W2.

5. The online monitoring system for special steel equipment according to claim 4, characterized in that: The process of obtaining the temperature anomaly factor MTi is as follows: The real-time temperature value Tij of each special steel equipment is collected in real time according to the time series through temperature sensing; Wherein, i represents the number of special steel collection equipment, i=1, 2, ..., n; j represents the number of each special steel collection point, j=1, 2, ..., m; Integrate the real-time temperature value Tij of each special steel equipment to obtain a temperature value group; By formula Calculate the temperature anomaly factor MTi for each special steel equipment; Among them, b1 and b2 are preset proportional factors, and b1 and b2 are both greater than 0, Timax is the maximum temperature value in the temperature value group, Timin is the minimum temperature value in the temperature value group, Tiy is the average temperature value in the temperature value group, and αi is the standard deviation value of the temperature value group.

6. The online monitoring system for special steel equipment according to claim 4, characterized in that: The process of obtaining the pressure abnormality factor MFi is as follows: The real-time pressure Fij of each special steel equipment is collected in real time according to the time series through pressure sensing; The real-time pressure value Fij of each special steel equipment is integrated to obtain a pressure value group; By formula The pressure abnormality factor MFi is calculated; Wherein, b3 and b4 are preset proportional factors, and both b3 and b4 are greater than 0, Fiy is the average pressure value of the pressure value group, and Si is the variance value of the pressure value group.

7. The online monitoring system for special steel equipment according to claim 1, characterized in that: The process of obtaining the correlation coefficient ρ is: Obtain the load performance value Ri of each special steel equipment, and obtain the operation score Qi of each special steel equipment; Sort the load performance values ​​Ri and assign a ranking Ri′ to each load performance value Ri, sort the operation scores Qi and assign a ranking Qi′ to each operation score Qi; Sort the running scores Qi and assign a ranking Qi′ to each running score Qi; The ranking difference di of each collection point is calculated by the formula di = |Ri′-Qi′|; By formula The correlation coefficient ρ was calculated.

8. The online monitoring system for special steel equipment according to claim 1, characterized in that: The process of obtaining the normal pressure range is as follows: By formula The pressure value fluctuation ratio WFy is calculated; The normal pressure upper limit threshold Fs is calculated by the formula Fs=Fq*(1+WFy); The normal pressure lower limit threshold Fm is calculated by the formula Fm=Fq*(1-WFy); Among them, Fq is the preset optimal pressure value, which is usually set according to the staff's experience, Fy is the comprehensive historical average pressure value, Fymax is the historical average maximum pressure value, and Fymin is the historical average minimum pressure value; Then the normal pressure range is [Fm, Fs].

9. The online monitoring system for special steel equipment according to claim 1, characterized in that: The process of obtaining the normal temperature range is as follows: By formula The temperature fluctuation ratio TFy is calculated; The normal temperature upper limit threshold Fs is calculated by the formula Ts=Tq*(1+TFy); The normal temperature lower limit threshold Tm is calculated by the formula Tm=Tq*(1-TFy); Among them, Tq is the preset optimal steel pipe temperature value, Ty is the comprehensive historical average temperature value, Tymax is the maximum average temperature, and Ty is the minimum average temperature; Then the normal temperature range is [Tm, Ts].

10. The online monitoring system for special steel equipment according to claim 1, characterized in that: According to the normal temperature range [Tm, Ts], adjust the temperature of the special steel equipment within the normal temperature range [Tm, Ts]; according to the normal pressure range [Fm, Fs], adjust the pressure of the special steel equipment within the normal pressure range [Fm, Fs].

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