Industrial robot manufacturing process potential failure mode risk analysis method

Abstract

The invention relates to the field of quality and reliability engineering, and discloses an industrial robot manufacturing process potential failure mode risk analysis method, which comprises the following steps: S1, describing the risk of a potential failure mode in an industrial robot manufacturing process by using a graduatory fuzzy language; s2, solving evaluation personnel and influence factor weights based on a best worst method and a water injection theory; s3, performing compromise calculation on risk values of the failure modes by adopting a multi-criterion iterative decision-making method, and determining a risk sequence of the failure modes; considering that the structure of the industrial robot is complex and the evaluation personnel have a blind area in understanding the failure mode, the hesitant psychology of the evaluation personnel is captured by adopting the graduatory fuzzy language with higher degree of freedom, and meanwhile, the weight of the evaluation personnel is calculated to quantify the ability of the evaluation personnel, so that the evaluation result is more objective; according to the method, a multi-criterion iterative decision method is adopted by considering that the consequence is serious when the industrial robot fails, and the weight of an influence factor must be considered in failure mode risk calculation.

Description

Technical Field

[0001] This invention relates to the field of quality and reliability engineering, and specifically to a method for risk analysis of potential failure modes in the manufacturing process of industrial robots. Background Technology

[0002] In modern production operations, industrial robots possess advantages such as high precision, high stability, and continuous operation, enabling them to replace human labor in some high-frequency and dangerous tasks. Currently, industrial robots have become the core force in the intelligent manufacturing framework, and their quality directly determines the efficiency and quality of enterprise production. Once an industrial robot fails, it can cause huge economic losses and even endanger the lives of operators. Therefore, potential failure mode risk analysis in the industrial robot manufacturing process is very important for improving its quality. Failure Mode and Effects Analysis (FMEA) is a method for identifying potential failure modes during the product design and manufacturing stages, with the aim of preventing and avoiding their occurrence. FMEA first appeared in the 1960s and, due to its ease of operation and wide applicability, has been widely used in manufacturing, transportation, and supply chain industries. However, with societal development, traditional FMEA has revealed some problems in its application: First: Using integers as the evaluation language is impractical; in complex work environments, evaluators may hesitate, making it unrealistic to give precise evaluations of failure modes. Second: The weight of the evaluators was not calculated; in actual production, evaluators in different positions have different understandings of failure modes, so the qualifications of the evaluators must be quantified. Third: The weights of influencing factors are evenly distributed; usually, people pay more attention to the severity S of the failure mode because it is directly related to the safety of people and property. Fourth: Risk values ​​cannot prevent failure modes from occurring; FMEA determines risk values ​​through Risk Priority Numbers (RPNs), based on... However, it does not take into account weighting information and the calculation results may be duplicated, which cannot guide enterprises to develop preventive measures. Summary of the Invention

[0003] The purpose of this invention is to solve the above problems by designing a method for risk analysis of potential failure modes in the manufacturing process of industrial robots.

[0004] This invention provides a method for potential failure mode risk analysis in the manufacturing process of industrial robots, the method comprising the following steps: Step S1: Use Pythagorean fuzzy language to describe the risks of potential failure modes in the industrial robot manufacturing process; Step S2: Solve for the weights of evaluators and influencing factors based on the best-worst method and water injection theory; Step S3: Use the multi-criteria iterative decision-making method to calculate the risk value of failure modes and determine the risk ranking of failure modes.

[0005] Optionally, in the first implementation of the present invention, the calculation step of the evaluator weights in step S2 includes: Step S211: Collect evaluator qualification information; Step S212: Select the most qualified and least qualified evaluators; Step S213: Compare the remaining evaluators with the best and worst evaluators respectively, and obtain the corresponding judgment vectors; Step S214: Based on the judgment vector, construct a nonlinear programming model for calculating the weights of evaluators, and solve for the weights of evaluators.

[0006] Optionally, in the second implementation of the present invention, in step S214, a nonlinear programming model for calculating the weights of evaluators is constructed based on the judgment vector as follows: ; in, To determine the optimal evaluation personnel weights, Weighting for the worst-performing personnel. Assign weights to the remaining evaluators. and To determine the vector, This serves as a consistency indicator.

[0007] Optionally, in a third implementation of the present invention, the calculation step of the influencing factor weights in step S2 includes: Step S221: Aggregate the evaluation matrix based on the evaluator weights; Step S222: Calculate the mean and standard deviation of the aggregated evaluation matrix; Step S223: Calculate the weights of influencing factors by measuring the horizontal plane.

[0008] Optionally, in the fourth implementation of the present invention, in step S221, the weight of the evaluator is determined. An intuitive fuzzy weighted geometric operator is used to evaluate the matrix. For aggregation, the intuitionistic fuzzy weighted geometric operator expression is as follows:

[0009] In the formula, Indicates the number of evaluators. Indicates the number of failure modes. This indicates the number of influencing factors.

[0010] Optionally, in a fifth implementation of the present invention, in step S223, the weights are calculated based on the mean and standard deviation of the evaluation matrix, as shown in the following expression: ; ; In the formula, It refers to the horizontal plane. It is the standard deviation. It is the mean.

[0011] Optionally, in the sixth implementation of the present invention, step S3 includes the following steps: Step S311: Calculate the relative dominance of the failure mode based on the weights of the influencing factors; Step S312: Calculate the global dominance of the failure modes by aggregating relative dominance. Step S313: Determine the risk value and ranking of failure modes based on global dominance.

[0012] Optionally, in the seventh implementation of the present invention, in step S311, Failure mode Influencing factors The following evaluation information 、 and The weights for severity, frequency, and detectability of the influencing factors are respectively, and the failure modes are... and The formula for calculating the relative dominance among the influencing factors is as follows: ; In the formula, The loss attenuation coefficient is... Represents the fuzzy distance of the evaluation information. A scoring function representing evaluation information.

[0013] Optionally, in the eighth implementation of the present invention, in step S312, the failure mode and The relative advantage degree is 、 and The formula for calculating global dominance is as follows: .

[0014] Optionally, in the ninth implementation of the present invention, in step S313, the failure mode The global dominance is calculated using the following formula across all failure modes: .

[0015] The beneficial effects of this invention are: 1. The industrial robot manufacturing process potential failure mode risk analysis method provided by the present invention takes into account the complex structure of industrial robots and the blind spots in the evaluation personnel's understanding of failure modes. The present invention uses the Pythagorean fuzzy language with higher degree of freedom to capture the evaluation personnel's hesitation psychology, and calculates the evaluation personnel's weight to quantify their ability, so as to make the evaluation results more objective. 2. The industrial robot manufacturing process potential failure mode risk analysis method provided by the present invention takes into account that the robot needs to perform some dangerous and high-frequency tasks. The present invention calculates the weight of the factors affecting the failure mode of the robot. Moreover, the present invention combines the TODIM method and the weight of the influencing factors to calculate the risk value and ranking of potential failure modes, which not only conforms to the actual application scenario, but also reduces the probability of the same evaluation result. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0017] Figure 1 A flowchart illustrating a potential failure mode risk analysis method for industrial robot manufacturing processes provided by this invention; Figure 2 A schematic diagram of the degrees of freedom of Pythagorean fuzzy language in a potential failure mode risk analysis method for industrial robot manufacturing process provided by the present invention. Figure 3 This is a schematic diagram of the best-worst method in the potential failure mode risk analysis method for industrial robot manufacturing process provided by the present invention; Figure 4 This is a schematic diagram of the water injection theory in a potential failure mode risk analysis method for industrial robot manufacturing process provided by the present invention; Figure 5 This is a schematic diagram of the industrial robot structure provided by the present invention; Figure 6 This is a schematic diagram of the structure tree of the industrial robot control system provided by the present invention; Figure 7 This is a schematic diagram of the functional tree of the industrial robot control system provided by the present invention; Figure 8 This is a schematic diagram illustrating the results of the LINGO software used in this invention to calculate the weights of evaluators. Figure 9A schematic diagram of experimental data for ranking failure modes under different loss attenuation coefficients provided by this invention; Figure 10 The results of a comparative analysis of the present invention with other risk analysis methods are shown in the first group. Figure 11 The results of a second group of comparative analyses provided by this invention with other risk analysis methods are shown in the figure. Figure 12 This diagram illustrates the calculation results of the central solution for risk ranking obtained by different risk analysis methods provided by this invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] To address the shortcomings of FMEA in areas such as risk assessment language, weight information confirmation, and Risk Priority Number (RPN) calculation, this invention provides a method for potential failure mode risk analysis in industrial robot manufacturing processes, referring to... Figure 1 This includes the following steps: Step S1: Use Pythagorean Fuzzy Language (PFN) to describe the risks of potential failure modes in the industrial robot manufacturing process; Step S2: Solve for the weights of evaluators and influencing factors based on the Best-Worst Method (BWM) and Water-Filling Theory (WFT); Step S3: The multi-criteria iterative decision method (TODIM) is used to calculate the risk value of failure modes and determine the risk ranking of failure modes.

[0020] In this embodiment, in step S1, It is a non-empty set. and From to the interval The mapping of the Pythagorean fuzzy language set The expression is as follows: ; in, and Representing elements respectively Belongs to set The membership degree and non-membership degree of the two elements must satisfy the following conditions: ; Compared to general intuitive and vague language ,conform to Constraints, such as Figure 2 As shown, Pythagorean fuzzy language has greater freedom and therefore can express richer semantics.

[0021] In step S1, Pythagorean fuzzy language The exact function expression is shown below: ; The scoring function expression is as follows: ; in, , .

[0022] In step S1, Pythagorean fuzzy language and The formula for calculating the fuzzy distance between the two is as follows: ; In step S1, the Pythagorean fuzzy language, in practical applications, needs to follow computational rules. , It is a set of two Pythagorean fuzzy languages. It is a constant. The complement of a set is represented by the following expression: ; ; ; ; ; In this embodiment, the calculation of the evaluator's weight in step S2 includes: Step S211: Collect the qualification information of the evaluators; Step S212: Select the evaluators with the best and worst qualifications; Step S213: Conduct mutual evaluation based on qualifications; Step S214: Construct a nonlinear model to solve for the weights of the evaluators.

[0023] In this embodiment, in steps S212 and S213, the set of evaluator qualification information is as follows: The best qualified evaluator is The worst qualified evaluator is The remaining evaluators are compared with the best evaluator to obtain the judgment vector. The remaining evaluators are compared with the worst evaluator to obtain the judgment vector. Compare the diagrams, such as Figure 3 As shown.

[0024] In step S214, based on the judgment vector, a nonlinear programming model for calculating the weights of evaluators is constructed as follows: ; in, To determine the optimal evaluation personnel weights, Weighting for the worst-performing personnel. Assign weights to the remaining evaluators. and To determine the vector, This serves as a consistency indicator.

[0025] Consistency Indicators This is to perform a consistency check on the results obtained using the Best-Worst Method (BWM). Consistency checks can promptly identify and correct inconsistencies or errors in the data, ensuring the accuracy and effectiveness of subsequent analyses. Without consistency checks, contradictory situations may arise where evaluation information A is greater than B, B is greater than C, but A is less than C. The consistency ratio (CR) is the standard for consistency checks, and its calculation formula is shown below: ; in, The values ​​of are shown in Table 1. If the consistency check is passed, the decision vector is considered valid; otherwise, the decision vector needs to be reconstructed.

[0026] Table 1 Value table ; In this embodiment, the calculation steps for the weights of influencing factors in step S2 include: Step S221: Aggregate the evaluation matrix based on the evaluator weights; Step S222: Calculate the mean and standard deviation of the aggregated evaluation matrix; Step S223: Calculate the weights of influencing factors by measuring the horizontal plane.

[0027] In step S221, based on the evaluator weights The Intuitive Fuzzy Weighted Geometric Operator (SIFWG) is used to evaluate the matrix. Aggregation is performed, and its expression is shown below: ; In the formula, Indicates the number of evaluators. Indicates the number of failure modes. This indicates the number of influencing factors.

[0028] In step S222, based on the aggregated evaluation matrix Calculate their mean, as shown in the following expression: ; In the formula, Representing the evaluation matrix The exact function.

[0029] The standard deviation expression is as follows: ; In step S223, based on the mean and standard deviation of the evaluation matrix, the weights of influencing factors are calculated using the Waterfill Theory (WFT). It is necessary to first determine the amount of information contained in the risk assessment. Its expression is as follows: ; in, Indicates the weight of the influencing factors. Representative influencing factors, The greater the amount of information included in a failure mode risk assessment, the more comprehensive the risk analysis results. To maximize the amount of information... Maximize it by constructing the Lagrange function as follows: ; Calculate the partial derivative and assign a value. The expression for calculating the weights of influencing factors is obtained as follows: ; make To indicate the horizontal plane, the expression for calculating the weights of influencing factors is as follows: ; because And according to the water injection theory (WFT) diagram, such as Figure 4 As shown, The expression is as follows: ; In this embodiment, the execution of step S3 includes the following steps: Step S311: Calculate the relative dominance of the failure mode based on the weights of the influencing factors; Step S312: Calculate the global dominance of the failure modes by aggregating relative dominance. Step S313: Determine the risk value and ranking of failure modes based on global dominance. In step S311, Failure mode Influencing factors The following evaluation information , and The weights for the severity (S), frequency (O), and detectability (D) of the influencing factors are respectively used to determine the failure modes. and The formula for calculating the relative dominance of influencing factor S is as follows: ; in, The loss attenuation coefficient is... Represents the fuzzy distance of the evaluation information. A scoring function representing evaluation information.

[0030] In step S312, failure mode and The relative advantage degree is , and The global dominance calculation formula is as follows: ; In step S313, failure mode The global dominance is calculated using the following formula across all failure modes: ; Here, the present invention provides an example, as shown in the structural diagram of an industrial robot product. Figure 5 As shown, the method proposed in this application is used to analyze potential failure modes in the industrial robot manufacturing process.

[0031] Step S1: Use Pythagorean Fuzzy Language (PFN) to describe the risks of potential failure modes in the industrial robot manufacturing process; The company selected four evaluators from different positions, designated E1, E2, E3, and E4. In FMEA, the potential failure modes of the industrial robot are determined by analyzing the product's structure tree and function tree. This application uses a robot control system as an example, whose structure tree and function tree are shown below. Figure 6 and Figure 7 As shown in Table 2, the structure tree analysis decomposes the product into smaller, more manageable components to better understand its structural characteristics. Building upon the structure tree, the functional tree analysis adds the functions of these components and their interconnections. Finally, by assuming functional failures in each component, the causes and consequences of these failures are collected, as detailed in Table 2. The evaluators used Pythagorean fuzzy language to evaluate the failure modes, and the evaluation results are shown in Tables 3 to 6.

[0032] Table 2 Summary of Potential Failure Modes in Industrial Robot Manufacturing Process

[0033] Table 3 E1 Evaluation Information Table

[0034] Table 4 E2 Evaluation Information Table

[0035] Table 5 E3 Evaluation Information Table

[0036] Table 6 E4 Evaluation Information Table

[0037] Step S2: Solve for the weights of evaluators and influencing factors based on the Best-Worst Method (BWM) and Waterfill Theory (WFT). The company quantified the qualifications of evaluators based on six dimensions: years of service (WY), education level (EB), professional title, number of times participating in FMEA (PT), number of training sessions (TT), and position. The quantification criteria are shown in Table 7. Based on the quantification criteria in Table 7, the qualifications of four evaluators were quantified, and the results are shown in Table 8.

[0038] Table 7 Quantitative Scoring Table for Evaluators' Qualifications

[0039] Table 8 Quantitative Score Sheet for Evaluators

[0040] As shown in Table 8, E1 had the highest quantitative score, while E4 had the lowest. Therefore, E1 was determined to be the evaluator with the best qualifications. E4 represents the lowest level of qualification. The judgment vector is... and A nonlinear solution model for the weights of evaluators is constructed as follows: ; The nonlinear programming model was solved using LINGO software, and the solution results are as follows: Figure 8 As shown. By Figure 8 It can be seen that the weight of the evaluator is... , , , Consistency indicators According to Table 1, the consistency ratio can be obtained. Therefore, the weight calculation results of the evaluators passed the consistency test.

[0041] The SIFWG operator is used to aggregate the evaluation information. Taking the aggregation of evaluation information of failure mode FM1 under influencing factor S as an example, the aggregation results are shown below: ; Similarly, the aggregated results for other failure modes are calculated, as shown in Table 9, and then the precise values ​​of the evaluation information are calculated. Taking the precise value calculation of failure mode FM1 under influencing factor S as an example, the precise value is... Similarly, the precise values ​​for other failure modes were calculated, and the results are shown in Table 10.

[0042] Table 9 Summary of SIFWG Aggregation Results

[0043] Table 10 Summary of Failure Mode Precision Value Calculations

[0044] Calculate the mean of the influencing factors to obtain , , Next, calculate the standard deviation of the influencing factors of the failure mode. Taking the standard deviation calculation of failure mode FM1 under influencing factor S as an example, the standard deviation is: Similarly, the standard deviations of other failure modes were calculated, and the results are shown in Table 11.

[0045] Calculate the horizontal plane according to Table 11. Therefore, the weights of the influencing factors are as follows: , , .

[0046] Step S3: The multi-criteria iterative decision method (TODIM) is used to calculate the risk value of failure modes and determine the risk ranking of failure modes. The scores for the evaluation information are calculated based on Table 9. Taking the score calculation of failure mode FM1 under influencing factor S as an example, the score is: Similarly, the scores for other failure modes were calculated, and the results are shown in Table 12.

[0047] Based on the comparison of failure mode scores in Table 12, combined with... The relative dominance and global dominance of the failure modes were calculated, and the results are shown in Table 13.

[0048] Based on the global dominance in Table 13, the risk ranking of failure modes is: FM9>FM7>FM4>FM6>FM2>FM8>FM5>FM3>FM1. Preventive measures can be taken for potential failure modes in the industrial robot manufacturing process based on this risk ranking.

[0049] Table 11 Summary of Standard Deviations of Failure Modes

[0050] Table 12 Summary Table of Failure Mode and Effects Evaluation Information Scores

[0051] Table 13 Summary of Failure Mode Relative Dominance and Global Dominance Calculations

[0052] A loss attenuation coefficient was introduced when calculating the relative advantage. For observation The impact on the final result is expressed in steps of 0.1. The values ​​are set to 0.1, 0.2, ..., 1.5. When At that time, the order of failure modes FM1 and FM3 changes, specifically as follows: Figure 9 As shown. When At that time, by Figure 9 It can be seen that the failure mode order has not changed; even if Only the ranking of low-risk failure modes changes, while the ranking of high-risk failure modes remains unchanged.

[0053] To verify the validity of the calculation results of this invention, the results of this invention are compared with the results of other methods.

[0054] Comparison Method 1: Best-Worst Method (BWM) + Traditional FMEA; First, using the evaluation data of this application, the evaluator weights are calculated using the best-worst method (BWM). Then, the precise values ​​of the evaluation information are calculated and aggregated using the SIFWG operator. Finally, the RPN values ​​are calculated and sorted for the precise values ​​of the aggregated evaluation information.

[0055] Comparison Method 2: Best-Worst Method (BWM) + Multi-Criterion Iterative Decision Making (TODIM) First, the weights of the influencing factors are evenly distributed, and the best-worst method (BWM) is used to calculate the weights of the evaluators. Then, the exact values ​​are calculated based on the evaluation data of this application and aggregated using the SIFWG operator. Finally, the global dominance of the aggregated evaluation information is calculated and ranked.

[0056] Comparison Method 3: Best-Worst Method (BWM) + Approximation of Ideal Solution Sorting Method (TOPSIS) First, the Best-Worst Method (BWM) is used to calculate the evaluator weights. Then, the evaluation information is aggregated by combining the evaluator weights and the SIFWG operator to calculate the precise values ​​of the evaluation data. Second, the evaluation data of influencing factors are standardized (where influencing factors S and O are cost indicators, and influencing factor D is a benefit indicator). Finally, the Approximation to Ideal Solution Ranking Method (TOPSIS) is used to calculate and rank the standardized evaluation data.

[0057] Comparison Method 4: Water-filling Theory (WFT) + Multi-criteria Compromise Solution Ranking Method (VIKOR) The Multi-Criterion Iterative Decision Making (TODIM) method in this application is replaced by the Multi-Criterion Optimization Compromise Solution Ranking (VIKOR) method. The evaluators' weights are evenly distributed, and the Waterfill Theory (WFT) is used to calculate the weights of influencing factors. In the VIKOR calculation, the parameters... .

[0058] This application presents two sets of comparative analyses: Comparative Analysis 1: The results of this invention are compared with those of BWM + traditional FMEA and BWM + TODIM, such as... Figure 10 The diagram illustrates the impact of influencing factor weights and evaluation language on risk ranking.

[0059] Comparative Analysis 2: The results of this invention are compared with those of BWM + TOPSIS and WFT + VIKOR, such as... Figure 11 The figure is shown to illustrate the impact of the evaluator weights and risk value calculation methods on the ranking.

[0060] To explain the differences in failure mode risk ranking calculations among different methods, this application introduces the optimal decision aggregation technique. The optimal decision aggregation technique is a commonly used method for judging the superiority of evaluation results. It constructs a nonlinear programming model based on the ranking of the evaluation objects in different methods, obtains the central solution of the evaluation objects by solving the model, and finally calculates the degree of difference between the ranking results of different methods and the central solution, thereby measuring the superiority of the methods. The formula for calculating the degree of difference is as follows: ; in, Representing the evaluation objects under different methods The sorting, Indicates the object of evaluation The central solution.

[0061] Assume the failure mode sorting matrix is The square array, in which Indicates failure mode Assigned to the The number of times each risk ranking is performed; let... Failure mode Assigned to the The cumulative number of times each risk ranking is performed, then This step helps to achieve centralized allocation of failure modes.

[0062] Because high-risk failure modes are crucial for FMEA, therefore... Indicates the first The importance of risk ranking When failure mode Assigned to the When ranking risks, ,otherwise The nonlinear programming model expression for the optimal decision aggregation technique is shown below: ; Figure 10 and Figure 11 The frequency of occurrence of each risk level is shown in Table 14. The cumulative occurrence count of failure mode risk ranking is obtained, as shown in Table 15.

[0063] Table 14 Different Risk Analysis Methods Summary table of calculation results

[0064] Table 15 Different Risk Analysis Methods Summary table of calculation results

[0065] Therefore, the nonlinear programming model for this application case is as follows:

[0066] The nonlinear programming model was calculated using LINGO software, and the results are as follows: Figure 12 As shown.

[0067] Depend on Figure 12 It can be seen that the central solution of the failure modes is: FM9>FM7>FM4>FM6>FM2>FM3>FM5>FM8>FM1.

[0068] Through the formula for calculating the degree of difference The degree of difference between different risk analysis methods can be obtained. , , , , . Finally, the difference calculation results show that the potential failure mode risk analysis method for industrial robot manufacturing process proposed in this application can make the failure mode risk ranking closer to the central solution compared with similar risk analysis methods.

[0069] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for potential failure mode risk analysis in the manufacturing process of industrial robots, characterized in that, The method includes the following steps: Step S1: Use Pythagorean fuzzy language to describe the risks of potential failure modes in the industrial robot manufacturing process; Step S2: Solve for the weights of evaluators and influencing factors based on the best-worst method and water injection theory; Step S3: Use the multi-criteria iterative decision-making method to calculate the risk value of failure modes and determine the risk ranking of failure modes.

2. The method for potential failure mode risk analysis in the manufacturing process of industrial robots as described in claim 1, characterized in that, In step S2, the calculation steps for the evaluator weights include: Step S211: Collect evaluator qualification information; Step S212: Select the most qualified and least qualified evaluators; Step S213: Compare the remaining evaluators with the best and worst evaluators respectively, and obtain the corresponding judgment vectors; Step S214: Based on the judgment vector, construct a nonlinear programming model for calculating the weights of evaluators, and solve for the weights of evaluators.

3. The method for potential failure mode risk analysis in the manufacturing process of industrial robots as described in claim 2, characterized in that, In step S214, based on the judgment vector, a nonlinear programming model for calculating the weights of evaluators is constructed as follows: ； in, To determine the optimal evaluation personnel weights, Weighting for the worst-performing personnel. Assign weights to the remaining evaluators. and To determine the vector, This serves as a consistency indicator.

4. The method for potential failure mode risk analysis in the manufacturing process of industrial robots as described in claim 1, characterized in that, In step S2, the calculation steps for the weights of influencing factors include: Step S221: Aggregate the evaluation matrix based on the evaluator weights; Step S222: Calculate the mean and standard deviation of the aggregated evaluation matrix; Step S223: Calculate the weights of influencing factors by measuring the horizontal plane.

5. The method for potential failure mode risk analysis in the manufacturing process of industrial robots as described in claim 4, characterized in that, In step S221, based on the evaluator weights An intuitive fuzzy weighted geometric operator is used to evaluate the matrix. For aggregation, the intuitionistic fuzzy weighted geometric operator expression is as follows: ； In the formula, Indicates the number of evaluators. Indicates the number of failure modes. This indicates the number of influencing factors.

6. The method for potential failure mode risk analysis in the manufacturing process of industrial robots as described in claim 4, characterized in that, In step S223, the weights are calculated based on the mean and standard deviation of the evaluation matrix, as shown in the following expression: ； ； In the formula, It refers to the horizontal plane. It is the standard deviation. It is the mean.

7. The method for potential failure mode risk analysis in the manufacturing process of an industrial robot as described in claim 1, characterized in that, Step S3 includes the following steps: Step S311: Calculate the relative dominance of the failure mode based on the weights of the influencing factors; Step S312: Calculate the global dominance of the failure modes by aggregating relative dominance. Step S313: Determine the risk value and ranking of failure modes based on global dominance.

8. The method for potential failure mode risk analysis in the manufacturing process of an industrial robot as described in claim 7, characterized in that, In step S311, Failure mode Influencing factors The following evaluation information 、 and The weights for severity, frequency, and detectability of the influencing factors are respectively, and the failure modes are... and The formula for calculating the relative dominance among the influencing factors is as follows: ； In the formula, The loss attenuation coefficient is... Represents the fuzzy distance of the evaluation information. A scoring function representing evaluation information.

9. The method for potential failure mode risk analysis in the manufacturing process of an industrial robot as described in claim 8, characterized in that, In step S312, failure mode and The relative advantage degree is 、 and The formula for calculating global dominance is as follows: 。 10. The method for potential failure mode risk analysis in the manufacturing process of an industrial robot as described in claim 1, characterized in that, In step S313, failure mode The global dominance is calculated using the following formula across all failure modes: 。

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