Improved FMEA analysis method and device for oil and gas pipeline welding robot

Abstract

The embodiment of the invention provides an improved FMEA analysis method and device for an oil and gas pipeline welding robot, and is applied to the technical field of product fault analysis. A qualitative concept is converted into a quantitative value through the cloud model, uncertainty in information is reserved, a language term set and the cloud model are combined, the requirement of expert evaluation can be met, information loss can be reduced, and authenticity and integrity of evaluation information are reserved. The DEMATEL method considers the correlation between risk factors to solve the decision problem in a complex environment. Accurate sorting and classification of fault risks are achieved through a three-way decision cloud model method, and the problem that in the fault risk analysis and evaluation process of an existing oil and gas pipeline welding robot, analysis and evaluation are inaccurate and unreasonable is solved.

Description

Technical Field

[0001] This disclosure relates to the field of product failure analysis technology, and in particular to an improved FMEA analysis method and apparatus for oil and gas pipeline welding robots. Background Technology

[0002] Against the backdrop of my country's extensive oil and gas pipeline construction, oil and gas pipeline welding robots are widely used in engineering construction. As complex equipment, oil and gas pipeline welding robots are highly technology-intensive, highly integrated, and have complex manufacturing processes. They are prone to various failures during operation, necessitating failure risk analysis and assessment to improve their safe and reliable service capabilities. Failure Mode and Effect Analysis (FMEA) is widely used in failure risk analysis and assessment. In its analysis, it primarily uses real numbers from 1 to 10 to calculate risk priority numbers (RPNs) and then ranks the identified failure risks. However, when using traditional FMEA to analyze and assess the failure risks of oil and gas pipeline welding robots, some problems still exist: First, experts generally evaluate failure risks using qualitative language, and it is difficult to accurately judge these linguistic variables using only quantitative real-valued information from 1 to 10; Second, the correlation between risk factors is not considered when calculating the risk factor weights, but rather the risk factors are assumed to be of equal importance; Third, after calculating the risk priority number, traditional FMEA only ranks the risks without classifying them, but in the development or use of oil and gas pipeline welding robots, time and cost constraints make it difficult to address all risks, and it is necessary to prioritize the resolution of higher-risk failures through risk classification.

[0003] Therefore, in the process of analyzing and assessing the failure risks of existing oil and gas pipeline welding robots, there are problems of inaccurate and unreasonable analysis and assessment. Summary of the Invention

[0004] This disclosure provides an improved FMEA analysis method, apparatus, equipment, and storage medium for welding robots used in oil and gas pipelines.

[0005] According to a first aspect of this disclosure, an improved FMEA analysis method for oil and gas pipeline welding robots is provided, the method comprising: Establish an analysis group consisting of n experts. Failure mode and effect analysis was conducted on a welding robot for oil and gas pipelines, identifying m failure modes. and s risk factors ; Based on the opinions of various experts, the failure modes under each risk factor were evaluated using linguistic variables, resulting in an evaluation matrix. And use preset cloud model language variables to evaluate the matrix Transformed into a cloud information matrix ; Based on three indicators—professional title, work experience, and field relevance—the analytic hierarchy process (AHP) and scoring method were used to evaluate each expert. The weights are calculated to obtain the expert weight vector. ; Decision-making experiments and evaluation laboratories were used to assess risk factors. The weights are calculated to obtain the risk factor weights. ; Fault modes are ranked based on cloud distance, and fault risks are classified and handled based on the three-way decision principle.

[0006] According to a second aspect of this disclosure, an improved FMEA analysis apparatus for oil and gas pipeline welding robots is provided, the apparatus comprising: The building block is used to create an analysis group consisting of n experts. Failure mode and effect analysis was conducted on a welding robot for oil and gas pipelines, identifying m failure modes. and s risk factors ; The computational analysis module is used to evaluate the failure modes under each risk factor based on the opinions of various experts and using linguistic variables, thereby obtaining an evaluation matrix. And use preset cloud model language variables to evaluate the matrix Transformed into a cloud information matrix ; The calculation and analysis module is also used to evaluate each expert based on three indicators: professional title, work experience, and field matching degree, using methods such as the analytic hierarchy process and scoring methods. The weights are calculated to obtain the expert weight vector. ; The computational analysis module is also used to analyze risk factors using decision experiment and evaluation laboratory methods. The weights are calculated to obtain the risk factor weights. ; The computational analysis module is also used to rank fault modes by risk based on cloud distance and to classify and handle fault risks based on the three-way decision principle.

[0007] According to a third aspect of this disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the program to implement the method as described in the first aspect of this disclosure.

[0008] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method of the first aspect.

[0009] This disclosure transforms qualitative concepts into quantitative values ​​using a cloud model while preserving the uncertainty in the information. By combining a linguistic terminology set with the cloud model, it can meet the needs of expert evaluation while reducing information loss and preserving the authenticity and integrity of the evaluation information. The DEMATEL method considers the correlation between risk factors to solve decision-making problems in complex environments. The three-branch decision cloud model method achieves accurate ranking and classification of failure risks, solving the problems of inaccurate and unreasonable analysis and evaluation in the existing failure risk analysis and assessment of oil and gas pipeline welding robots.

[0010] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A flowchart illustrating an improved FMEA analysis method for an oil and gas pipeline welding robot according to an embodiment of the present disclosure is shown. Figure 2 A schematic diagram of an improved FMEA analysis apparatus for an oil and gas pipeline welding robot according to an embodiment of the present disclosure is shown. Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0013] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0014] In this disclosure, to address the aforementioned technical problems, a cloud model is used to transform qualitative concepts into quantitative values ​​while preserving the uncertainty in the information. By combining a language terminology set with the cloud model, the needs of expert evaluation are met while reducing information loss and preserving the authenticity and integrity of the evaluation information. The DEMATEL method considers the correlations between risk factors to solve decision-making problems in complex environments. A three-branch decision cloud model method is used to accurately rank and classify failure risks, resolving the problems of inaccurate and unreasonable analysis and evaluation in existing failure risk analysis and assessment processes for oil and gas pipeline welding robots.

[0015] Figure 1 The diagram illustrates a flowchart of an improved FMEA analysis method for oil and gas pipeline welding robots according to an embodiment of this disclosure. Figure 1 As shown, the improved FMEA analysis method 100 for oil and gas pipeline welding robots may include: S101, Establish an analysis group consisting of n experts. Failure mode and effect analysis was conducted on a welding robot for oil and gas pipelines, identifying m failure modes. and s risk factors ; S102, based on the opinions of various experts, evaluate the failure modes under each risk factor using linguistic variables to obtain the evaluation matrix. And use preset cloud model language variables to evaluate the matrix Transformed into a cloud information matrix The evaluation matrix (i.e., Equation 1). Specifically, the above-mentioned evaluation matrix uses preset cloud model language variables. Transformed into a cloud information matrix The process can be described in Table 1, using cloud model linguistic variables to evaluate the matrix. Transformed into a cloud information matrix ,in, E x This represents the expectation of the cloud model. E n Represents the entropy of the cloud model. H e X represents the hyperentropy of the cloud model. maxX represents the maximum value of the evaluation universe. min This represents the minimum value of the evaluation domain.

[0016] Table 1. Transformation of Language Terminology Sets into Cloud Models S103, based on three indicators—professional title, work experience, and field relevance—the analytic hierarchy process and scoring method were used to evaluate each expert. The weights are calculated to obtain the expert weight vector. ; S104, employing the Decision Making Trial and Evaluation Laboratory (DEMATEL) approach to assess risk factors. The weights are calculated to obtain the risk factor weights. ; In some embodiments, decision-making experiments and evaluation laboratory methods are used to assess risk factors. The weights are calculated to obtain the risk factor weights. ,include: Constructing the Direct Influence Matrix ,in, Indicates risk factors With risk factors The results of pairwise comparisons; According to the formula That is, Equation 2) on the direct influence matrix Standardization yields the matrix ; According to the formula Equation 3) calculates the comprehensive influence matrix, where I is the identity matrix; Calculate the impact And the degree of influence Among them, influence It sums the values ​​of each row in the comprehensive influence matrix to determine the degree of influence. This involves summing each column of the comprehensive influence matrix. Calculate centrality and causal degree ; Based on centrality Calculate the weights of risk factors Satisfying the formula That is, formula 4).

[0017] In some embodiments, if This indicates that the risk factor is a causal factor that actively influences other risk factors; if If so, it means that the risk factor is an outcome-type factor that is influenced by other risk factors.

[0018] S105 ranks fault modes by risk based on cloud distance and classifies and handles fault risks based on the three-way decision principle.

[0019] In some embodiments, S105 includes ranking the failure modes by risk based on cloud distance, including: The evaluations of the same risk factor by multiple experts are combined into a comprehensive cloud model, and the weighted calculation formula satisfies... That is, equation 5), where, Indicates expert weights; The evaluation results of multiple risk factors for the same failure mode are combined into a comprehensive evaluation cloud, and the comprehensive cloud for each indicator is... Perform weighted synthesis to satisfy the formula That is, equation 6), where, Indicates the risk factor weights; Calculate the distance between the integrated cloud and the optimal and worst clouds for each failure mode, and sort the integrated cloud models by comparing the distance results for different failure modes.

[0020] In some embodiments, the above calculation of the distance between the integrated cloud of each failure mode and the optimal and worst clouds, and the ranking by comparing the integrated cloud model distance results for different failure modes, includes: Determine the optimal cloud and the worst cloud Satisfying the formula That is, Equation 7), where the positive and negative ideal solutions of the benefit-type indicators are respectively and The positive and negative ideal solutions for cost-type indicators are respectively and ; Calculate the distance between the failure mode and the best and worst clouds under each risk factor. The distance calculation formula is as follows: That is, formula 8). Calculate the relative proximity of each failure mode. Satisfying the formula That is, equation 9). Each failure mode is ranked according to its relative similarity. The values ​​are sorted, with larger values ​​indicating lower failure risk and smaller values ​​indicating higher failure risk.

[0021] In some embodiments, the above-mentioned classification and handling of fault risks based on the three-way decision-making principle includes: The decision threshold is calculated based on a comprehensive loss function constructed using cloud distance, satisfying the following conditions. That is, formula 10). That is, Equation 11), where, For risk aversion coefficient, ; Based on decision threshold and Establish decision-making rules to classify and handle fault risks.

[0022] In some embodiments, the above is based on a decision threshold. and Develop decision-making rules to classify and handle fault risks, including: when At that time, the risk was low, and the fault handling was temporarily postponed; when At this time, the risk is considered medium. If time and cost permit, fault handling measures should be taken to eliminate or reduce the risk of failure. when When the risk is high, priority should be given to taking fault handling measures to eliminate or reduce the fault risk.

[0023] In one specific embodiment, the improved FMEA analysis method for oil and gas pipeline welding robots described above may include: (1) Conduct failure mode and impact analysis Oil and gas pipeline external welding robots are key equipment in oil and gas pipeline construction. Their main function is to complete filler and capping welds on the outside of the pipeline to ultimately form a complete welded structure. Utilizing these robots can improve welding efficiency and reduce construction costs while ensuring welding quality. However, if an oil and gas pipeline external welding robot malfunctions, it will interrupt welding operations. If not repaired promptly, subsequent processes cannot be carried out, leading to delays in pipeline construction and increased project time costs. Furthermore, malfunctions may cause unstable welding parameters and deviations from the welding trajectory, resulting in welding defects (such as porosity, slag inclusions, and incomplete penetration), affecting the quality of the pipeline weld and requiring rework, increasing costs and creating safety hazards. Therefore, it is necessary to conduct FMEA analysis on oil and gas pipeline external welding robots to identify and classify key risks in order to take more targeted measures.

[0024] Please have three experts form the FMEA analysis team. FMEA analysis was conducted on the external welding robot for oil and gas pipelines, identifying seven failure modes. As shown in Table 2, severity S, frequency of occurrence O, and detectability D are used as risk factors, i.e. The corresponding linguistic variable terminology is shown in Table 3.

[0025] Table 2 Failure Modes, Impacts, and Causes of External Welding Robots for Oil and Gas Pipelines Table 3. Variable Table of Language Terminology for Risk Factors (2) Transform the expert evaluation results into cloud model language variables. Each expert evaluated the failure mode under each risk factor using linguistic variables, resulting in an evaluation matrix. The evaluation results are shown in Table 4.

[0026] Table 4 Risk Factor Evaluation Table Based on the conversion model in Table 1, this example gives He0=0.02, Xmax=10, and Xmin=0, and will evaluate the matrix... Transformed into a cloud information matrix That is, the evaluation results in Table 4 are transformed, and the transformation results are... The results are shown in Table 5.

[0027] Table 5 Risk Factor Cloud Information Evaluation Table (3) Calculate expert weights Based on three indicators—professional title, work experience, and field relevance—this example uses a scoring method to calculate the weights of the three experts. The scoring table is shown in Table 6, yielding the expert weight vector. .

[0028] Table 6 Expert Weighting Scoring Table (4) Calculate the risk factor weights using the DEMATEL method. Based on the aforementioned steps, the DEMATEL method was used to analyze the risk factors. The weights are calculated as follows: First, the degree of direct influence between risk factors is determined through expert assessment, and a direct influence matrix is ​​constructed. , (12) Secondly, the direct influence matrix is ​​analyzed using equation (2). Standardization is performed to obtain the standardization matrix. , (13) Then, the comprehensive influence matrix is ​​calculated using equation (3). , (14) Finally, calculate the centrality. The risk factor weights are calculated using equation (4). .

[0029] (5) Ranking the risk of failure modes based on cloud distance 1) The expert single-factor evaluation was synthesized according to formula (5), and the results are shown in Table 7.

[0030] Table 7. Results of Single-Factor Synthesis of Failure Modes 2) Combine multiple risk factors into a comprehensive evaluation cloud according to formula (6), and the results are shown in Table 8.

[0031] Table 8 Comprehensive Evaluation of Fault Modes 3) Cloud ranking based on comprehensive evaluation. The distance between the comprehensive cloud and the best and worst clouds for each failure mode is calculated. The cloud models of different failure modes are ranked by comparing the distance results. The calculation results are shown in Table 9.

[0032] Table 9 Comprehensive Evaluation of Fault Modes (6) Calculate the decision threshold using equations (10) and (11). and This example Based on decision rules, decisions are made regarding the failure risks caused by each failure mode, as shown in Table 10.

[0033] Table 10 Fault Risk Classification Decision Results The improved FMEA analysis method for oil and gas pipeline welding robots disclosed in this publication, compared with existing technologies, firstly, transforms qualitative concepts into quantitative values ​​through a cloud model while retaining the uncertainty in the information. Combining a linguistic terminology set with the cloud model satisfies the needs of expert evaluation while reducing information loss, preserving the authenticity and completeness of the evaluation information. The DEMATEL method considers the correlation between risk factors, improving the rationality of risk factor weight determination and significantly enhancing the objectivity and accuracy of fault risk quantification analysis. Secondly, the three-branch decision cloud model method enables the ranking and classification of fault risks, allowing R&D or maintenance teams to accurately identify high-risk fault modes requiring priority handling, rationally allocate resources under time and cost constraints, and significantly improve the pertinence and economic efficiency of risk management. This solves the problems of inaccurate and unreasonable analysis and evaluation in existing oil and gas pipeline welding robot fault risk analysis and assessment processes.

[0034] The above is an introduction to the method embodiments. The following describes the present disclosure further through device embodiments.

[0035] Figure 2 A schematic diagram of an improved FMEA analysis apparatus for an oil and gas pipeline welding robot according to an embodiment of the present disclosure is shown.

[0036] like Figure 2 As shown, the improved FMEA analysis device 200 for oil and gas pipeline welding robots may include: Module 201 is used to establish an analysis group consisting of n experts. Failure mode and effect analysis was conducted on a welding robot for oil and gas pipelines, identifying m failure modes. and s risk factors ; The calculation and analysis module 202 is used to evaluate the failure modes under each risk factor based on the opinions of various experts and using linguistic variables, thereby obtaining an evaluation matrix. And use preset cloud model language variables to evaluate the matrix Transformed into a cloud information matrix ; The calculation and analysis module 202 is also used to analyze each expert's performance based on three indicators: professional title, work experience, and field matching degree, using methods such as the analytic hierarchy process and scoring. The weights are calculated to obtain the expert weight vector. ; The computational analysis module 202 is also used to analyze risk factors using decision experiment and evaluation laboratory methods. The weights are calculated to obtain the risk factor weights. ; The calculation and analysis module 202 is also used to rank the failure modes based on cloud distance and classify and handle failure risks based on the three-way decision principle.

[0037] In some embodiments, the above-described methods of decision-making experiments and evaluation laboratories are used to assess risk factors. The weights are calculated to obtain the risk factor weights. ,include: Constructing the Direct Influence Matrix ,in, Indicates risk factors With risk factors The results of pairwise comparisons; According to the formula Direct Influence Matrix Standardization yields the matrix ; According to the formula Calculate the comprehensive impact matrix, where I is the identity matrix; Calculate the impact And the degree of influence Among them, influence It sums the values ​​of each row in the comprehensive influence matrix to determine the degree of influence. This involves summing each column of the comprehensive influence matrix. Calculate centrality and causal degree ; Based on centrality Calculate the weights of risk factors Satisfying the formula .

[0038] In some embodiments, if This indicates that the risk factor is a causal factor that actively influences other risk factors; if If so, it means that the risk factor is an outcome-type factor that is influenced by other risk factors.

[0039] In some embodiments, the above-mentioned risk ranking of failure modes based on cloud distance includes: The evaluations of the same risk factor by multiple experts are combined into a comprehensive cloud model, and the weighted calculation formula satisfies... ,in, Indicates expert weights; The evaluation results of multiple risk factors for the same failure mode are combined into a comprehensive evaluation cloud, and the comprehensive cloud for each indicator is... Perform weighted synthesis to satisfy the formula ,in, Indicates the risk factor weights; Calculate the distance between the integrated cloud and the optimal and worst clouds for each failure mode, and sort the integrated cloud models by comparing the distance results for different failure modes.

[0040] In some embodiments, the above calculation of the distance between the integrated cloud of each failure mode and the optimal and worst clouds, and the ranking by comparing the integrated cloud model distance results for different failure modes, includes: Determine the optimal cloud and the worst cloud Satisfying the formula Among them, the positive and negative ideal solutions of the benefit-type indicators are respectively and The positive and negative ideal solutions for cost-type indicators are respectively and ; Calculate the distance between the failure mode and the best and worst clouds under each risk factor. The distance calculation formula is as follows: ; Calculate the relative proximity of each failure mode. Satisfying the formula ; Each failure mode is ranked according to its relative similarity. The values ​​are sorted, with larger values ​​indicating lower failure risk and smaller values ​​indicating higher failure risk.

[0041] In some embodiments, the above-mentioned classification and handling of fault risks based on the three-way decision-making principle includes: The decision threshold is calculated based on a comprehensive loss function constructed using cloud distance, satisfying the following conditions. , , in, For risk aversion coefficient, ; Based on decision threshold and Establish decision-making rules to classify and handle fault risks.

[0042] In some embodiments, the above is based on a decision threshold. and Develop decision-making rules to classify and handle fault risks, including: when At that time, the risk was low, and the fault handling was temporarily postponed; when At this time, the risk is considered medium. If time and cost permit, fault handling measures should be taken to eliminate or reduce the risk of failure. when When the risk is high, priority should be given to taking fault handling measures to eliminate or reduce the fault risk.

[0043] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0044] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0045] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0046] Figure 3A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Electronic device 300 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0047] Electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 302 or a computer program loaded from storage unit 308 into random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0048] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0049] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed.

[0050] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0051] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0052] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0053] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0054] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0055] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0056] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0057] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An improved FMEA analysis method for welding robots in oil and gas pipelines, characterized in that, The method includes: Establish an analysis group consisting of n experts. Failure mode and effect analysis was conducted on a welding robot for oil and gas pipelines, identifying m failure modes. and s risk factors ; Based on the opinions of various experts, the failure modes under each risk factor were evaluated using linguistic variables, resulting in an evaluation matrix. And use preset cloud model language variables to evaluate the matrix Transformed into a cloud information matrix ; Based on three indicators—professional title, work experience, and field relevance—the analytic hierarchy process (AHP) and scoring method were used to evaluate each expert. The weights are calculated to obtain the expert weight vector. ; Decision-making experiments and evaluation laboratories were used to assess risk factors. The weights are calculated to obtain the risk factor weights. ; Fault modes are ranked based on cloud distance, and fault risks are classified and handled based on the three-way decision principle.

2. The method according to claim 1, characterized in that, The method of using decision experimentation and evaluation laboratory to assess risk factors. The weights are calculated to obtain the risk factor weights. ,include: Constructing the Direct Influence Matrix ,in, Indicates risk factors With risk factors The results of pairwise comparisons; According to the formula Direct Influence Matrix Standardization yields the matrix ; According to the formula Calculate the comprehensive impact matrix, where I is the identity matrix; Calculate the impact And the degree of influence Among them, influence It sums the values ​​of each row in the comprehensive influence matrix to determine the degree of influence. This involves summing each column of the comprehensive influence matrix. Calculate centrality and causal degree ; Based on centrality Calculate the weights of risk factors Satisfying the formula .

3. The method according to claim 2, characterized in that, The method further includes: like This indicates that the risk factor is a causal factor that actively influences other risk factors; if If so, it means that the risk factor is an outcome-type factor that is influenced by other risk factors.

4. The method according to claim 2, characterized in that, The risk ranking of failure modes based on cloud distance includes: The evaluations of the same risk factor by multiple experts are combined into a comprehensive cloud model, and the weighted calculation formula satisfies... ,in, E x This represents the expectation of the cloud model. E n Represents the entropy of the cloud model. H e Represents the hyperentropy of the cloud model. Indicates expert weights; The evaluation results of multiple risk factors for the same failure mode are combined into a comprehensive evaluation cloud, and the comprehensive cloud for each indicator is... Perform weighted synthesis to satisfy the formula ,in, Indicates the risk factor weights; Calculate the distance between the integrated cloud and the optimal and worst clouds for each failure mode, and sort the integrated cloud models by comparing the distance results for different failure modes.

5. The method according to claim 4, characterized in that, The calculation of the distance between the integrated cloud for each failure mode and the optimal and worst clouds, and the ranking of the integrated cloud model distance results for different failure modes, includes: Determine the optimal cloud and the worst cloud Satisfying the formula Among them, the positive and negative ideal solutions of the benefit-type indicators are respectively and The positive and negative ideal solutions for cost-type indicators are respectively and ; Calculate the distance between the failure mode and the best and worst clouds under each risk factor. The distance calculation formula is as follows: ; Calculate the relative proximity of each failure mode. Satisfying the formula ; Each failure mode is ranked according to its relative similarity. The values ​​are sorted, with larger values ​​indicating lower failure risk and smaller values ​​indicating higher failure risk.

6. The method according to claim 5, characterized in that, The classification and handling of fault risks based on the three-way decision-making principle includes: The decision threshold is calculated based on a comprehensive loss function constructed using cloud distance, satisfying the following conditions. , ,in, For risk aversion coefficient, ; Based on decision threshold and Establish decision-making rules to classify and handle fault risks.

7. The method according to claim 6, characterized in that, The decision threshold and Develop decision-making rules to classify and handle fault risks, including: when At that time, the risk was low, and the fault handling was temporarily postponed; when At this time, the risk is considered medium. If time and cost permit, fault handling measures should be taken to eliminate or reduce the risk of failure. when When the risk is high, priority should be given to taking fault handling measures to eliminate or reduce the fault risk.

8. An improved FMEA analysis device for oil and gas pipeline welding robots, characterized in that, The device includes: The building block is used to create an analysis group consisting of n experts. Failure mode and effect analysis was conducted on a welding robot for oil and gas pipelines, identifying m failure modes. and s risk factors ; The computational analysis module is used to evaluate the failure modes under each risk factor based on the opinions of various experts and using linguistic variables, thereby obtaining an evaluation matrix. And use preset cloud model language variables to evaluate the matrix Transformed into a cloud information matrix ; The calculation and analysis module is also used to evaluate each expert based on three indicators: professional title, work experience, and field matching degree, using methods such as the analytic hierarchy process and scoring methods. The weights are calculated to obtain the expert weight vector. ; The computational analysis module is also used to analyze risk factors using decision experiment and evaluation laboratory methods. The weights are calculated to obtain the risk factor weights. ; The computational analysis module is also used to rank fault modes by risk based on cloud distance and to classify and handle fault risks based on the three-way decision principle.

9. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory is characterized in that it stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the method according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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