Crane stress monitoring data processing method and system

Abstract

The invention discloses a crane stress monitoring data processing method and system, and solves the technical problem that real-time online analysis of stress monitoring big data is difficult to realize due to lack of a crane intermittent stress monitoring big data processing method in the prior art. The method comprises the following steps: acquiring stress monitoring data through a crane stress monitoring system; eliminating stress abnormal values; extracting and separating trend terms of the monitoring data to obtain new monitoring data; segmenting the new monitoring data according to the time of one working cycle of the crane to obtain segmented monitoring data; calculating the difference value between the maximum stress value and the minimum stress value of the segmented monitoring data, and assuming that the fatigue limit of the material is Sf, and the threshold value of the stress amplitude Sa is 5% of Sf; and judging whether the stress monitoring data of the section needs to be stored / remotely transmitted or not. According to the method, the calculation requirement is low, whether the crane causes the fatigue accumulated damage state or not can be effectively judged, and the stress monitoring big data storage / transmission capacity is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of crane structure health monitoring, and in particular to a crane stress monitoring data processing method and system. Background Art

[0002] Hoisting machinery is widely used in various industries of the national economy, such as metallurgy, electricity, ports, logistics, machinery manufacturing, construction and marine engineering. It is an important process equipment for industrial production and is related to the protection of people's lives. Traditional hoisting machinery management is mainly based on safety management, which is mainly carried out according to the regular inspection procedures of cranes, and then draws conclusions or conducts some safety evaluation work based on the test results. This management model can usually only reflect the current safety status of the hoisting machinery, and cannot truly reflect the health status of the hoisting machinery system. With the rapid development of information technology, especially the rise of big data technology, the safety management of hoisting machinery will increasingly be based on monitoring big data and analysis.

[0003] Structural damage failure caused by crane fatigue is one of the most important failure modes of crane structures. Fatigue damage failure has a very high potential danger. When the key structure of the crane reaches or approaches its fatigue life, the key structure may suddenly develop into serious accidents such as fracture, causing heavy casualties. According to statistics, in the modern industrial field, more than 80% of structural failures are caused by the fatigue failure mode of the structure. At the same time, with the development of economy and technology, compared with the past, the working environment of cranes is becoming more and more harsh, and the external loads they need to bear are becoming more and more complex, so the fatigue damage caused by this is becoming more and more serious and frequent.

[0004] Hoisting machinery is a material handling machinery that lifts, lowers and moves materials horizontally in an intermittent operation mode. The stress amplitude caused by some working states is small, and basically no fatigue cumulative damage is generated. In addition, the crane is not in working state all the time. The stress amplitude of the crane in the non-working state is very small, and basically no fatigue cumulative damage is generated. Therefore, real-time stress monitoring big data is intermittent big data. Most of its stress monitoring data belongs to invalid non-working state data. With the rapid development of monitoring technology, it has been widely used in the health monitoring of hoisting machinery. The amount of health monitoring data of hoisting machinery is growing exponentially. At present, the stress monitoring data of cranes reaches 100G / day, and with the improvement of safety management requirements, the monitoring data will increase sharply, and the real-time analysis task will become more and more onerous, so offline analysis is generally used, and then reports are provided to users.

[0005] The present invention provides a crane stress monitoring data processing method and system, which provide method and technical support for real-time analysis of stress monitoring big data. Summary of the invention

[0006] The purpose of the present invention is to provide a crane stress monitoring data processing method and system to solve the technical problem that the prior art lacks a crane intermittent stress monitoring big data processing method, and thus it is difficult to realize real-time online analysis of stress monitoring big data.

[0007] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides a crane stress monitoring data processing method, comprising the following steps: Step 1: Obtain stress monitoring data of a certain period of time in real time through the crane stress monitoring system; Step 2: Eliminate stress anomalies in monitoring data; Step 3: Extract and separate the trend items of the monitoring data to obtain new monitoring data; Step 4: segment the new monitoring data according to the time of a working cycle of the crane to obtain segmented monitoring data; Step 5: Calculate the difference between the maximum stress and the minimum stress of the segmented monitoring data, assuming that the fatigue limit of the material is S f The threshold value of stress amplitude Sa is 5% of S f , that is, Sa_threshold=0.05*S f ; Step 6: Determine whether the stress monitoring data of this section needs to be stored / remotely transmitted. The specific determination method is: Definition Criteria 1: (1); Definition Criterion 2: Sa <Sa_threshold(2); in, is the maximum stress value, is the minimum stress value; If criterion 1 is met, the stress monitoring data in this section does not need to be stored / transmitted remotely; if >Sa_threshold, if criterion 1 is not met, the stress amplitude Sa is calculated using the cycle counting method. If criterion 2 is met, the stress monitoring data of this section does not need to be stored / remotely transmitted; If Sa>Sa_threshold and criterion 2 is not met, the stress monitoring data of this section and the calculated Sa are stored / remotely transmitted and subsequently used for calculation and analysis of structural fatigue damage.

[0008] Furthermore, in step 2, the calculation formula for removing stress outliers from monitoring data is: (3); Among them, x i is the stress monitoring data of a certain period of time, i=1, 2,…, n, For x i The mean of for x i The standard deviation of .

[0009] Furthermore, in step 3, the extracting and separating trend items of monitoring data specifically includes the following steps: Assume that the stress monitoring data is x(n) (n=1,2,…,N). Since the sampling data is at equal time intervals, a polynomial function is assumed: (4); To determine The undetermined coefficient (j=0,1,…,m), so that the function The sum of squares of the error with discrete data x(n) is minimized, that is: (5); The conditions for E to have an extreme value are: (6); Take E pairs in turn Find the partial derivative and get m+1 linear equations: (7); Solve equation (7) to obtain m+1 unknown coefficients (j=0,1,…,m); The formula for eliminating the linear trend term is: (8); when When is a quadratic / higher order curve trend item.

[0010] Furthermore, in step 5, the calculation steps of the stress amplitude Sa are as follows: (1) Eliminate data points that are neither peaks nor valleys, and convert the time history records into peak-valley value sequences; (2) For the peak-to-valley value sequence, the two-parameter rainflow counting method is used to count the cycles and extract and count the amplitude and mean.

[0011] In a second aspect, the present invention provides a crane stress monitoring data processing system, which is used to implement a crane stress monitoring data processing method, including: Stress monitoring data acquisition module, used to obtain crane stress monitoring data in real time; An outlier removal module is used to remove outliers from stress monitoring data; A trend item extraction and separation module is used to extract and separate trend items from the monitoring data after removing abnormal values; A data segmentation module, used to segment the monitoring data after trend item extraction and separation; The stress amplitude calculation and judgment module is used to calculate the stress amplitude of each segment of data and judge whether the segment of data is valid data and whether it needs to be stored or remotely transmitted according to preset criteria.

[0012] In a third aspect, the present invention provides an electronic device comprising a processor and a memory; the memory stores computer instructions, and the processor is used to run the computer instructions stored in the memory to execute the steps of the crane stress monitoring data processing method disclosed in any one of the above aspects.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores programs or instructions, and the programs or instructions enable a computer to execute the steps of the crane stress monitoring data processing method disclosed in any one of the above aspects.

[0014] Based on the above technical solution, the embodiments of the present invention can at least produce the following technical effects: The present invention is based on Criteria 1 and Criteria 2. Criteria 1 and Criteria 2 are relatively simple and have low calculation requirements. They can effectively determine whether the crane has caused fatigue cumulative damage, greatly reducing the storage / transmission capacity of stress monitoring big data. The analysis work only needs to carry out analysis and calculation on the monitoring data of the "effective operating working status", which can quickly and efficiently complete the analysis tasks based on stress monitoring big data, save manpower and material resources, and achieve controllable and efficient overall links. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of the crane stress monitoring data processing method of the present invention; Figure 2 These are seven peak-to-valley value form diagrams that meet counting conditions of the present invention; Figure 3 It is the original monitoring data graph of the present invention; Figure 4 is the monitoring data with outliers removed by the present invention; Figure 5It is the monitoring data from which the trend is removed in the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in 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. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0018] A crane stress monitoring data processing method, such as Figure 1 As shown, the following steps are included: Step 1: Obtain the stress monitoring data Data_01 of a certain period of time in real time through the crane stress monitoring system; Step 2: Eliminate stress outliers; Step 3: Extract and separate the trend items of the monitoring data to obtain new monitoring data Data_02; Step 4: Generally speaking, Data_02 can be segmented according to the time of a working cycle of the crane. The segmented data are: Data_03_01, Data_03_02, ..., Data_03_0X (X=1, 2, ...N); Step 5: Calculate the maximum stress value of Data_03_0X and the minimum stress The difference ; Assume that the fatigue limit of the material is S f The threshold value of stress amplitude Sa can be taken as 5% of S f , that is, Sa_threshold=0.05*S f ; Step 6: Determine whether the stress monitoring data of this section needs to be stored / remotely transmitted. The specific determination method is: Definition Criteria 1: (1); Definition Criterion 2: Sa <Sa_threshold(2); If criterion 1 is met, the stress monitoring data in this section does not need to be stored / transmitted remotely; if >Sa_threshold, if criterion 1 is not met, the stress amplitude Sa is calculated using the cycle counting method. Furthermore, if criterion 2 is met, the stress monitoring data of this section does not need to be stored / remotely transmitted; If Sa>Sa_threshold and criterion 2 is not met, the stress monitoring data of this section and the calculated Sa are stored / remotely transmitted and can be used for calculation and analysis of structural fatigue damage later.

[0019] To summarize steps 5-6, first, criterion 1 is used for judgment. If criterion 1 is met, the stress monitoring data of this section does not need to be stored / remotely transmitted. If criterion 1 is not met, Sa is calculated. If criterion 2 is met, the stress monitoring data of this section still does not need to be stored / remotely transmitted. On the contrary, if neither criterion 1 nor criterion 2 is met, the stress monitoring data of this section and the calculated Sa are stored / remotely transmitted, and can be used for calculation and analysis of structural fatigue damage later.

[0020] In the specific implementation process, outlier removal is the process of lifting equipment health monitoring system collecting and transmitting data. Due to environmental factors or human factors, some data values ​​are inconsistent with reality and are obviously far away from most monitoring values. Such data are called outliers. (3); Among them, x i is the stress monitoring data of a certain period of time, i=1, 2,…, n, For x i The mean of for x i The standard deviation of .

[0021] In the specific implementation process, the trend item extraction and separation specifically includes the following steps: Stress monitoring data will be affected by loads and external environments such as temperature and humidity, and will show a certain trend of change. Trend items are extracted and separated using the least squares method or Savitzky-Golay smoothing filter method.

[0022] The monitoring data will show a certain trend of change due to the influence of load and external environment. We can extract the trend items of the data, observe the changing rules of the trend items, and predict the subsequent data.

[0023] Assume that the stress monitoring data is x(n) (n=1,2,…,N). Since the sampling data is at equal time intervals, a polynomial function is assumed: (4); To determine The undetermined coefficient (j=0,1,…,m), so that the function The sum of squares of the error with discrete data x(n) is minimized, that is: (5); The conditions for E to have an extreme value are: (6); Take E pairs in turn Taking partial derivatives, we can get m+1 linear equations: (7); Solve equation (7) to obtain m+1 unknown coefficients (j=0,1,…,m).

[0024] The formula for eliminating the linear trend term is: (8); when When is the quadratic / higher order curve trend term. In the actual stress monitoring data processing, the value of m is usually taken to be relatively small. When the value of m is large, an ill-conditioned equation group is prone to occur.

[0025] In the specific implementation process, the calculation steps of the stress state monitoring data amplitude Sa are as follows: (1) Eliminate data points that are neither peaks nor valleys, and convert the time history records into peak-valley value sequences; (2) For the peak-to-valley value sequence, the two-parameter rainflow counting method is used to count the cycles and extract and count the amplitude and mean.

[0026] like Figure 2 As shown, for 4 consecutive peak-valley points P1, P2, P3, P4, as long as they meet Figure 2 Any of the seven peak-valley forms shown can be used to extract a complete cycle. Let the values ​​corresponding to the four consecutive peak-valley points P1, P2, P3, and P4 be V1, V2, V3, and V4 respectively, then the amplitude is , the mean is .

[0027] Combining these 7 peak-valley forms, we can get the judgment condition for rain flow counting for 4 consecutive peak-valley value points P1, P2, P3, P4 (from left to right in the figure): (9).

[0028] like Figure 3 As shown, stress monitoring data is extracted from the crane structure strain monitoring sensor in real time to obtain initial monitoring data.

[0029] like Figure 4 As shown, some stress monitoring abnormal values ​​caused by environmental factors or human factors are eliminated.

[0030] Stress monitoring data will be affected by loads and external environments such as temperature and humidity, and will show a certain trend of change. Trend items are extracted and separated by the least squares method or Savitzky-Golay smoothing filter method. Separate (remove) trend items from monitoring data such as Figure 5 shown.

[0031] In the specific implementation process, the maximum and minimum values ​​of the data segment are calculated: Execute step 5 to calculate the maximum stress value of Data_03_0X and the minimum stress The difference ; Assume that the fatigue limit of the material is S f The threshold value of stress amplitude Sa can be taken as 5% of S f , that is, Sa_threshold=0.05*S f ; Taking Q345 steel, a common material for cranes, as an example, its fatigue limit S f It is generally 240MPa, so Sa_threshold = 12Mpa.

[0032] According to the results in Table 1, data segments 04-09, 12-14 (a total of 9 segments) meet criterion 1, and the stress monitoring data of this segment does not need to be stored / remotely transmitted; data segments 16-17, 22-23 (a total of 4 segments) do not meet criterion 1, but meet criterion 2, and the stress monitoring data of this segment does not need to be stored / remotely transmitted; data segments 01-03, 10-11, 15, 18-21 and 24 (a total of 11 segments) neither meet criterion 1 nor criterion 2, so the stress monitoring data of this segment and the calculated Sa are stored / remotely transmitted, and can be used for calculation and analysis of structural fatigue damage later.

[0033] Table 1 Data segment Calculation results with Sa

[0034] The present invention is based on Criteria 1 and Criteria 2. Criteria 1 and Criteria 2 are relatively simple and have low calculation requirements. They can effectively determine whether the crane has caused fatigue cumulative damage, greatly reducing the storage / transmission capacity of stress monitoring big data. The analysis work only needs to carry out analysis and calculation on the monitoring data of the "effective operating working status", which can quickly and efficiently complete the analysis tasks based on stress monitoring big data, save manpower and material resources, and achieve controllable and efficient overall links.

[0035] According to the case, it can be seen that 13 / 24 is equivalent to approximately 54% of the data does not need to be stored / transmitted, which is significantly more efficient.

[0036] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of the present invention to be protected is defined by the attached claims and their equivalents.

Claims

1. A crane stress monitoring data processing method, characterized in that: The following steps are involved: Step 1: Obtain stress monitoring data of a certain period of time in real time through the crane stress monitoring system; Step 2: Eliminate stress anomalies in monitoring data; Step 3: Extract and separate the trend items of the monitoring data to obtain new monitoring data; Step 4: segment the new monitoring data according to the time of a working cycle of the crane to obtain segmented monitoring data; Step 5: Calculate the difference between the maximum stress and the minimum stress of the segmented monitoring data, assuming that the fatigue limit of the material is S f The threshold value of stress amplitude Sa is 5% of S f , that is, Sa_threshold=0.05*S f ; Step 6: Determine whether the stress monitoring data of this section needs to be stored / remotely transmitted. The specific determination method is: Definition Criteria 1: （1）； Definition Criterion 2: Sa < Sa_threshold(2); in, is the maximum stress value, is the minimum stress value; If criterion 1 is met, the stress monitoring data in this section does not need to be stored / transmitted remotely; if >Sa_threshold, if criterion 1 is not met, the cycle counting method is used to calculate the stress amplitude Sa. If criterion 2 is met, the stress monitoring data of this section does not need to be stored / remotely transmitted; If Sa>Sa_threshold and criterion 2 is not met, the stress monitoring data of this section and the calculated Sa are stored / remotely transmitted and subsequently used for calculation and analysis of structural fatigue damage.

2. The crane stress monitoring data processing method according to claim 1 is characterized in that: In step 2, the calculation formula for removing stress anomalies from monitoring data is: （3）； Among them, x i is the stress monitoring data of a certain period of time, i=1, 2,…, n, For x i The mean of for x i The standard deviation of .

3. The crane stress monitoring data processing method according to claim 1 is characterized in that: In step 3, the extraction and separation of the trend items of the monitoring data adopts the least square method or the Savitzky-Golay smoothing filter method, which specifically includes the following steps: Assume that the stress monitoring data is x(n), n=1, 2, ..., N. Since the sampling data is at equal time intervals, a polynomial function is assumed: （4）； To determine The undetermined coefficient (j=0,1,…,m), so that the function The sum of squares of the error with discrete data x(n) is minimized, that is: （5）； The conditions for E to have an extreme value are: （6）； Take E pairs in turn Find the partial derivative and get m+1 linear equations: （7）； Solve equation (7) to obtain m+1 unknown coefficients (j=0,1,…,m); The formula for eliminating the linear trend term is: （8）； when When is a quadratic / higher order curve trend item.

4. The crane stress monitoring data processing method according to claim 1 is characterized in that: In step 5, the calculation steps of the stress amplitude Sa are as follows: (1) Eliminate data points that are neither peaks nor valleys, and convert the time history records into peak-valley value sequences; (2) For the peak-to-valley value sequence, the two-parameter rainflow counting method is used to count the cycles and extract and count the amplitude and mean.

5. A crane stress monitoring data processing system, used to implement the crane stress monitoring data processing method according to any one of claims 1 to 4, characterized in that: include: Stress monitoring data acquisition module, used to obtain crane stress monitoring data in real time; An outlier removal module is used to remove outliers from stress monitoring data; A trend item extraction and separation module is used to extract and separate trend items from the monitoring data after removing abnormal values; A data segmentation module, used to segment the monitoring data after trend item extraction and separation; The stress amplitude calculation and judgment module is used to calculate the stress amplitude of each segment of data and judge whether the segment of data is valid data and whether it needs to be stored or remotely transmitted according to preset criteria.

6. An electronic device, characterized in that: It comprises a processor and a memory; the memory stores computer instructions, and the processor is used to run the computer instructions stored in the memory to execute the steps of the crane stress monitoring data processing method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute the steps of the crane stress monitoring data processing method according to any one of claims 1 to 4.

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