A fatigue estimation and measurement method and system for tool

Abstract

The present invention relates to a method and system for estimating and measuring fatigue of tools. The method comprises: step S1: collecting data information of tools during the production process; step S2: cleaning the data information of the tools and extracting test information and usage information of the tools from the cleaned data information; step S3: constructing a state matrix of the tools based on the test information and usage information of the tools, and simultaneously constructing the usage frequency of the tools; step S4: calculating the fatigue index weight of the tools based on the state matrix of the tools and the usage frequency of the tools; and step S5: measuring the fatigue degree of the tools based on the fatigue index weight of the tools. The present invention can effectively evaluate the fatigue degree of tools.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial data analysis, and in particular to a method and system for estimating and measuring fatigue of a tool. Background Art

[0002] A single province's power grid may hold over 700,000 pieces of safety tools, encompassing a wide variety and large quantity. A survey revealed that issues persist in inventory management and record maintenance, as well as in-depth data analysis within the lifecycle management module. The safety tool lifecycle management module remains limited to basic functions like data storage and display, lacking in-depth analysis of massive usage data. Lean management and control efforts need improvement, and the data analysis foundation for differentiated configurations and categorized testing strategies for grassroots units remains inadequate. The same situation also applies to other industrial products.

[0003] To sum up, the core problem is the lack of a universal evaluation method for industrial products. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problem that there is no universal evaluation method for fatigue of industrial products (ie tools) in the prior art.

[0005] To solve the above technical problems, the present invention provides a tool fatigue estimation and measurement method, comprising:

[0006] Step S1: Collecting data information of tools during the production process;

[0007] Step S2: cleaning the data information of the tool, and extracting the test information and usage information of the tool from the cleaned data information;

[0008] Step S3: constructing a tool status matrix based on the tool test information and usage information, and constructing the tool usage frequency;

[0009] Step S4: Calculating the fatigue index weight of the tool according to the tool's state matrix and the tool's usage frequency;

[0010] Step S5: measuring the fatigue degree of the tool according to the fatigue index weight of the tool.

[0011] In one embodiment of the present invention, the data information of the tool is cleaned in step S2, and the method includes: setting a threshold value through a preset correlation coefficient, and cleaning the data information of the tool based on the threshold value, wherein the preset correlation coefficient is a Spearman correlation coefficient.

[0012] In one embodiment of the present invention, the formula for the Spearman correlation coefficient is: Among them, d i It represents the level difference between two data, and n represents the number of samples.

[0013] In one embodiment of the present invention, in step S3, a tool state matrix is constructed based on the test information and usage information of the tool, and the formula is:

[0014]

[0015]

[0016] in, represents the outer product, use_nscrap means use and not scrapped, nouse_nscrap means no use and no scrapping, try_nscrap means test and not scrapped, notry_nscrap means no test and no scrapping, use_nscrap means use and scrap, try_nscrap means test and scrap, U i and T i are the usage vector and the test vector respectively, u represents the proportion of the number of tools that are used and not scrapped to the total number, It indicates the proportion of unused and unscrapped tools to the total number, s u It represents the proportion of scrapped tools to the total number, t is the proportion of tools that were tested but not scrapped to the total number, It indicates the proportion of tools that have not been tested and scrapped to the total number, s t It represents the ratio of the number of tools scrapped in the test to the total number, and j represents the jth tool.

[0017] In one embodiment of the present invention, the frequency of use of the tool is constructed in step S3 using the formula:

[0018]

[0019] Among them, total represents the number of all tool types, tool i Indicates the number of tools of type i used.

[0020] In one embodiment of the present invention, in step S4, the fatigue index weight of the tool is calculated based on the state matrix of the tool and the usage frequency of the tool, and the formula is:

[0021] weight=ω1ω2NF(j)

[0022] Where N is the frequency of use of the tool, F(j) is the state matrix of the j-th tool, and ω1ω2 is the product of the proportion of the test behavior and the use behavior in the total data volume.

[0023] In one embodiment of the present invention, the step S5 of measuring the fatigue of the tool according to the fatigue index weight of the tool includes:

[0024] If the fatigue index weight of the tool is greater than a preset weight threshold, the tool is in a fatigue state;

[0025] If the fatigue index weight of the tool is less than a preset weight threshold, the tool is in a non-fatigue state.

[0026] To solve the above technical problems, the present invention provides a tool fatigue estimation and measurement system, comprising:

[0027] Statistics module: used to collect statistics on tools and equipment during the production process;

[0028] Cleaning and extraction module: used for cleaning the data information of the tool and extracting the test information and usage information of the tool from the cleaned data information;

[0029] A construction module is used to construct a state matrix of the tool according to the test information and usage information of the tool, and also to construct the usage frequency of the tool;

[0030] Calculation module: used for calculating the fatigue index weight of the tool according to the state matrix of the tool and the use frequency of the tool;

[0031] Measuring module: used to measure the fatigue degree of the tool according to the fatigue index weight of the tool.

[0032] To solve the above technical problems, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned tool fatigue estimation and measurement method are implemented.

[0033] To solve the above technical problems, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned tool fatigue estimation and measurement method are implemented.

[0034] The above technical solution of the present invention has the following advantages over the prior art:

[0035] The present invention first performs feature screening on the data and creatively constructs the state matrix and usage frequency of industrial products (i.e., tools) based on the screened features. Then, a fatigue index weight is constructed based on the state matrix and usage frequency, and the fatigue degree of the tool is measured by the fatigue index weight.

[0036] Experiments have shown that the fatigue index weight constructed by the present invention can effectively evaluate the fatigue degree of industrial products (i.e. tools);

[0037] The present invention constructs a universal fatigue evaluation method, which is applicable to multiple industries and fields and can be easily promoted on a large scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0039] Figure 1 is a flow chart of the method of the present invention;

[0040] Figure 2 Schematic diagram of fatigue weight calculation results of various tools in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0042] Example 1

[0043] Reference Figure 1 As shown, the present invention relates to a method for estimating and measuring fatigue of a tool, comprising:

[0044] Step S1: Collecting data information of tools during the production process;

[0045] Step S2: cleaning the data information of the tool, and extracting the test information and usage information of the tool from the cleaned data information;

[0046] Step S3: constructing a tool status matrix based on the tool test information and usage information, and constructing the tool usage frequency;

[0047] Step S4: Calculating the fatigue index weight of the tool according to the tool's state matrix and the tool's usage frequency;

[0048] Step S5: measuring the fatigue degree of the tool according to the fatigue index weight of the tool.

[0049] The present invention first performs feature screening on the data, and creatively constructs the state matrix and usage frequency of industrial products (i.e., tools) based on the screened features, and then constructs a fatigue index weight based on the state matrix and usage frequency. The fatigue index weight can effectively measure the fatigue of the tool.

[0050] The following is a detailed introduction to this embodiment:

[0051] Step 1: Collect data information of industrial products (i.e. tools) during the production process.

[0052] Step 2: Generally speaking, the features of industrial products can affect each other, and the impact of different features varies. Retaining data features with low correlation will increase the amount of model training, while retaining data features with high correlation will lead to too close connections between features, resulting in redundancy of data information and weakening the generalization ability of the model. In this process, the Spearman coefficient can well complete the setting of thresholds.

[0053] Correlation coefficients are often used to measure the correlation of discrete information. Common correlation coefficients include the Pearson correlation coefficient and the Spearman correlation coefficient. Compared to the two, the Spearman correlation coefficient has the advantages of being unaffected by the data dimension and insensitive to abnormally large numbers.

[0054] The Spearman correlation coefficient, also known as the Spearman rank correlation coefficient, is used to measure the correlation between two variables.

[0055]

[0056] In practical applications, variables do not need to be continuous, so the calculation of p is simplified to the rank difference of the two variables being calculated:

[0057]

[0058] Among them, d i It represents the level difference between two data, and n represents the number of samples.

[0059] Step 3: Extract product test and usage information from the feature-reduced data using the decision tree-RFE method. This embodiment combines correlation coefficient filtering with the decision tree-RFE method to screen features, significantly improving prediction accuracy and reducing computational space. Define the test information as the test vector T and the usage information as the usage vector U. For an industrial product, there will only be one test state and one usage state. Therefore, we can define the tool state matrix F, which is:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] in, Indicates the outer product, use_nscrap means used and not scrapped, nouse_nscrap means not used and not scrapped, try_nscrap means tested and not scrapped, notry_nscrap means no test and no scrapping, use_nscrap means used and scrapped, try_nscrap means tested and scrapped, U i and T i are the usage vector and the test vector respectively, u is the proportion of the number of tools that are used and not scrapped to the total number, It indicates the proportion of unused and unscrapped tools to the total number of tools, s u It represents the proportion of scrapped tools to the total number, t is the proportion of tools that were tested but not scrapped to the total number, It indicates the proportion of tools that have not been tested and scrapped to the total number, s t It represents the ratio of the number of tools scrapped in the test to the total number, and j represents the jth tool.

[0069] Step 4: Since different industrial tools have different uses and are used at different frequencies, we can define N as the frequency of use of the tool: the frequency of the type of tool, not the frequency of a single tool.

[0070]

[0071] Among them, total represents the number of all types of tools (such as helmet + grounding wire + gloves + ...), tool i Indicates the number of tools of type i used.

[0072] It should be noted that j represents the number of a certain type of tool, and i represents the type of tool.

[0073] Step 5: From the moment a tool leaves the factory, it can be assumed that it has begun to wear out, and the tool's product fatigue index begins to increase. Frequent use and irregular batch testing are the main reasons for increasing the tool's product fatigue index, and the endurance of different tools varies. Here, the product fatigue index weight of a single tool within a certain tool can be defined as:

[0074] weight=ω1ω2NF(j)

[0075] Where N is the frequency of use of the tool, F(j) is the state matrix of the j-th tool, and ω1ω2 is the product of the proportion of the test behavior and the use behavior in the total data volume.

[0076] This embodiment can measure the fatigue of the tool according to the fatigue index weight of the tool, including: if the fatigue index weight of the tool is greater than the preset weight threshold, the tool is in a fatigue state; if the fatigue index weight of the tool is less than the preset weight threshold, the tool is in a non-fatigue state.

[0077] Experimental analysis

[0078] To verify the validity of the fatigue index weight, this example calculates the fatigue index weights of the electroscope, red curtain, and insulating ladder. For specific calculation results, see Figure 2 Since there is no universal method to evaluate the fatigue of tools in the prior art, the fatigue of the electroscope, red curtain, and insulating ladder are evaluated one by one through professional knowledge in the industry. The evaluation results are basically consistent with the fatigue index weight calculated in this embodiment, indicating that the method of the present invention is effective and universal.

[0079] Example 2

[0080] This embodiment provides a tool fatigue estimation and measurement system, including:

[0081] Statistics module: used to collect statistics on tools and equipment during the production process;

[0082] Cleaning and extraction module: used for cleaning the data information of the tool and extracting the test information and usage information of the tool from the cleaned data information;

[0083] A construction module is used to construct a state matrix of the tool according to the test information and usage information of the tool, and also to construct the usage frequency of the tool;

[0084] Calculation module: used for calculating the fatigue index weight of the tool according to the state matrix of the tool and the use frequency of the tool;

[0085] Measuring module: used to measure the fatigue degree of the tool according to the fatigue index weight of the tool.

[0086] Example 3

[0087] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the tool fatigue estimation and measurement method described in the first embodiment are implemented.

[0088] Example 4

[0089] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the tool fatigue estimation and measurement method described in the first embodiment are implemented.

[0090] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0094] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0095] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for estimating and measuring fatigue of a tool, characterized by: include: Step S1: Collecting data information of tools during the production process; Step S2: cleaning the data information of the tool, and extracting the test information and usage information of the tool from the cleaned data information; Step S3: constructing a tool status matrix based on the tool test information and usage information, and constructing the tool usage frequency; In step S3, a tool state matrix is constructed based on the test information and usage information of the tool, and the formula is: in, represents the outer product, use_nscrap means used and not scrapped, nouse_nscrap means not used and not scrapped, try_nscrap means tested and not scrapped, notry_nscrap means not tested and not scrapped, use_scrap means used and scrapped, try_scrap means tested and scrapped, U i and T i are the usage vector and the test vector respectively, u represents the proportion of the number of tools that are used and not scrapped to the total number, It indicates the proportion of unused and unscrapped tools to the total number, s u It represents the proportion of scrapped tools to the total number, t is the proportion of tools that were tested but not scrapped to the total number, It indicates the proportion of tools that have not been tested and scrapped to the total number, s t It represents the proportion of tools scrapped during the test to the total number of tools, i represents the i-th type of tool, and j represents the j-th tool under the i-th type of tool; In step S3, the frequency of use of the tool is constructed using the formula: Among them, total represents the sum of the corresponding quantities of each tool type, tool i represents the number of tools of type i used; Step S4: Calculate the fatigue index weight of the tool according to the tool's state matrix and the tool's usage frequency. The formula is: weight=ω1ω2NF i Where N is the frequency of use of the tool, F i is the state matrix of the i-th tool, ω1ω2 is the product of the proportion of the test behavior and the use behavior in the total data volume; Step S5: measuring the fatigue degree of the tool according to the fatigue index weight of the tool.

2. The tool fatigue estimation and measurement method according to claim 1, characterized in that: In step S2, the data information of the tool is cleaned. The method includes: setting a threshold value by a preset correlation coefficient, and cleaning the data information of the tool based on the threshold value, wherein the preset correlation coefficient is a Spearman correlation coefficient.

3. The tool fatigue estimation and measurement method according to claim 2, characterized in that: The formula for the Spearman correlation coefficient is: Among them, d i It represents the rank difference between two data, and n represents the number of samples.

4. The tool fatigue estimation and measurement method according to claim 1, characterized in that: The step S5 of measuring the fatigue of the tool according to the fatigue index weight of the tool includes: If the fatigue index weight of the tool is greater than a preset weight threshold, the tool is in a fatigue state; If the fatigue index weight of the tool is less than a preset weight threshold, the tool is in a non-fatigue state.

5. A tool fatigue estimation and measurement system, used to implement the tool fatigue estimation and measurement method according to any one of claims 1 to 4, characterized in that: include: Statistics module: used to collect statistics on tools and equipment during the production process; Cleaning and extraction module: used for cleaning the data information of the tool and extracting the test information and usage information of the tool from the cleaned data information; A construction module is used to construct a state matrix of the tool according to the test information and usage information of the tool, and also to construct the usage frequency of the tool; Calculation module: used for calculating the fatigue index weight of the tool according to the state matrix of the tool and the use frequency of the tool; Measuring module: used to measure the fatigue degree of the tool according to the fatigue index weight of the tool.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the tool fatigue estimation and measurement method according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the tool fatigue estimation and measurement method according to any one of claims 1 to 4 are implemented.

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