FMECA analysis method and system based on relative importance of data

Abstract

The application provides an FMECA analysis method and system based on data relative importance, which comprises the following steps: decomposing a target object to be analyzed to generate a plurality of failure units; collecting historical failure data for each failure unit, and generating quantized values of a severity index, an occurrence index and a detection index of a failure cause in the historical failure data; calculating a relative weight of each index according to the quantized values of the severity index, the occurrence index and the detection index, and calculating a risk priority number (RPN) value of the failure cause according to the relative weight of each index; generating an RPN value of each failure unit according to the RPN value of the failure cause corresponding to each failure unit, and analyzing a key failure unit according to the RPN values of all the failure units. The method is based on a subjective data relative importance algorithm to perform FMECA analysis, and the reliability and accuracy of the FMECA analysis are improved.

Description

Technical Field

[0001] This application relates to the field of reliability analysis technology, and in particular to an FMECA analysis method based on the relative importance of data. Background Technology

[0002] Currently, in important engineering projects, product reliability analysis is often conducted to analyze potential failures and improve product safety. Among these methods, Failure Mode, Effects and Criticality Analysis (FMECA) is an effective reliability analysis technique. FMECA analyzes all possible product failures, identifies the impact of each failure mode on product operation, pinpoints single points of failure, and determines their severity.

[0003] In related technologies, the FMECA method relies solely on the experience of domain experts to quantify reliability analysis indicators. However, when expert experience data is insufficient, this approach leads to inaccurate subjective data, increasing the uncertainty of quantitative indicators. Consequently, reliability analysis results become difficult to quantify and risk level to determine, rendering the reliability analysis results meaningless. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose an FMECA analysis method based on the relative importance of data. This method performs FMECA analysis based on a subjective data relative importance algorithm, which improves the reliability and accuracy of FMECA analysis, reduces the uncertainty of reliability analysis results, and helps ensure the normal and safe operation of products.

[0006] The second objective of this application is to propose an FMECA analysis system based on the relative importance of data;

[0007] The third objective of this application is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of this application provides an FMECA analysis method based on the relative importance of data, the method comprising the following steps:

[0009] The target object to be analyzed is decomposed to generate multiple fault units;

[0010] For each of the faulty units, historical fault data is collected, and quantitative values ​​of the severity index, occurrence index, and detectability index of the fault cause in the historical fault data are generated.

[0011] The relative weight of each indicator is calculated based on the quantified values ​​of the severity indicator, the occurrence indicator, and the detectability indicator, and the risk priority number (RPN) value of the cause of failure is calculated based on the relative weight of each indicator.

[0012] Based on the Risk Priority Number (RPN) value corresponding to the fault cause for each fault unit, an RPN value for each fault unit is generated, and the key fault units are analyzed based on the RPN values ​​of all fault units.

[0013] Optionally, in one embodiment of this application, before generating the quantified values ​​of the severity index, occurrence index, and detectability index of the fault cause in the historical fault data, the method further includes: determining whether the number of collected historical fault data is greater than or equal to a preset numerical threshold; if the number of historical fault data is less than the numerical threshold, collecting historical fault data of objects similar to the target object.

[0014] Optionally, in one embodiment of this application, generating quantitative values ​​of the severity index, occurrence index, and detectability index of the fault causes in the historical fault data includes: determining all fault causes corresponding to each fault mode of the fault unit; collecting quantitative values ​​of the severity index, occurrence index, and detectability index given by a preset number of experts for each fault cause; and constructing a quantitative matrix of fault cause risk indicators based on the quantitative values ​​of the indicators corresponding to all fault causes.

[0015] Optionally, in one embodiment of this application, calculating the relative weight of each indicator based on the quantified values ​​of the severity indicator, the occurrence indicator, and the detectability indicator includes: comparing each risk assessment indicator of each fault cause with the risk assessment indicators corresponding to other fault causes in the quantified matrix of fault cause risk indicators to obtain a comparison value of the risk assessment indicator of each fault cause; constructing a comparison matrix of fault cause risk assessment indicators based on the comparison values ​​of the risk assessment indicators of all fault causes; and normalizing each indicator in the comparison matrix to obtain the relative weight of each indicator.

[0016] Optionally, in one embodiment of this application, when the fault unit is at the system level, generating the RPN value for each fault unit includes: for each fault unit, summing the RPN values ​​of all fault causes corresponding to each fault mode to obtain the RPN value of each fault mode; summing the RPN values ​​of all fault modes corresponding to each component to obtain the RPN value of each component; and summing the RPN values ​​of all components corresponding to each system to obtain the RPN value of each system.

[0017] Optionally, in one embodiment of this application, after the critical fault unit is analyzed, the method further includes: formulating an operation and maintenance plan and preventive measures based on the fault cause and fault mode of the critical fault unit, and implementing the operation and maintenance plan and preventive measures on the critical fault unit.

[0018] To achieve the above objectives, a second aspect of this application also proposes an FMECA analysis system based on the relative importance of data, comprising the following modules:

[0019] The decomposition module is used to decompose the target object to be analyzed, generating multiple fault units.

[0020] The generation module is used to collect historical fault data for each fault unit and generate quantitative values ​​of the severity index, occurrence index and detectability index of the fault cause in the historical fault data.

[0021] The calculation module is used to calculate the relative weight of each indicator based on the quantified values ​​of the severity indicator, the occurrence indicator, and the detectability indicator, and to calculate the risk priority number (RPN) value of the cause of failure based on the relative weight of each indicator.

[0022] The analysis module is used to generate the RPN value of each fault unit based on the Risk Priority Number (RPN) value of the fault cause corresponding to each fault unit, and to analyze the key fault units based on the RPN values ​​of all fault units.

[0023] Optionally, in one embodiment of this application, the system further includes: a judgment module, configured to judge whether the number of collected historical fault data is greater than or equal to a preset numerical threshold; and a collection module, configured to collect historical fault data of objects similar to the target object when the number of historical fault data is less than the numerical threshold.

[0024] Optionally, in one embodiment of this application, the generation module is specifically used for: determining all fault causes corresponding to each fault mode of the fault unit; collecting a preset number of quantitative values ​​of the severity index, the occurrence index, and the detectability index given by experts for each fault cause; and constructing a quantitative matrix of fault cause risk indicators based on the quantitative values ​​of the indicators corresponding to all fault causes.

[0025] The technical solution provided by the embodiments of this application brings at least the following beneficial effects: This application constructs a FMECA method for reliability analysis based on a data relative importance algorithm. It quantifies the three failure risk assessment indicators—severity, occurrence, and detectability—given by domain experts based on experience, calculates the relative weight of each indicator, and establishes a Risk Priority Number (RPN) based on the relative weights of each indicator to assess the risk level of the failure unit. This application further quantifies the reliability analysis indicators, i.e., the three failure risk assessment indicators, by collecting more historical data and calculating the relative importance of the introduced subjective data, reducing the uncertainty of the reliability analysis results. The RPN value is generated through the calculated relative weights, facilitating the determination of the risk level of each unit. Therefore, this application improves the reliability and accuracy of FMECA analysis, reduces the uncertainty of reliability analysis results, and helps ensure the normal and safe operation of the product.

[0026] To implement the above embodiments, a third aspect of this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the FMECA analysis method based on the relative importance of data in the above embodiments.

[0027] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0029] Figure 1 A flowchart illustrating an FMECA analysis method based on the relative importance of data proposed in this application.

[0030] Figure 2 A flowchart illustrating a method for calculating the quantified value of an evaluation index as proposed in an embodiment of this application;

[0031] Figure 3 A flowchart illustrating a method for calculating the relative weights of an evaluation index as proposed in an embodiment of this application;

[0032] Figure 4 A flowchart illustrating a specific FMECA analysis method based on the relative importance of data, as proposed in this application embodiment.

[0033] Figure 5 This is a schematic diagram of the structure of an FMECA analysis system based on the relative importance of data proposed in an embodiment of this application. Detailed Implementation

[0034] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0035] The following describes in detail, with reference to the accompanying drawings, a method and system for FMECA analysis based on the relative importance of data proposed in this application.

[0036] Figure 1 A flowchart illustrating an FMECA analysis method based on the relative importance of data proposed in this application is shown below. Figure 1 As shown, the method includes the following steps:

[0037] Step S101: Decompose the target object to be analyzed to generate multiple fault units.

[0038] It should be noted that, in this application, product reliability analysis refers to a systematic process of collecting, characterizing, and organizing reliability information, and obtaining reliability analysis results based on scientific and reasonable modeling methods. This application uses the FMECA method for reliability analysis. FMECA includes Failure Mode and Effects Analysis (FMEA) and Criticality Analysis (CA). FMEA assigns failure risk assessment indicators to the basic failure units of the analyzed product or system, including subjective indicators such as severity (S), occurrence (O), and detectability (D), and objective indicators such as failure probability and failure cost, thereby establishing a Risk Priority Number (RPN) to comprehensively evaluate the risk level of the basic failure units. Therefore, this application first decomposes the target object to be analyzed, generating multiple failure units.

[0039] The target object to be analyzed can be a product or system in an actual engineering project. For example, the target object can be a product such as a wind turbine or a photovoltaic power station, or it can be a system within the product, such as the wind energy receiving system of a wind turbine.

[0040] In one embodiment of this application, the target object can be decomposed into multiple fault units based on the different functions or specific equipment types of each unit within the target object. As an example, when the target object is a product, the systems within that product that implement different functions are separated, thereby decomposing the product into multiple systems based on the functions implemented by each system. For instance, a wind turbine can be decomposed into multiple systems such as a wind energy receiving system, a power generation system, and a power conversion system. In this example, the fault units are each of these systems.

[0041] As another example, when the target object is a system, the system can be decomposed into multiple devices according to the individual components that make up the system. For example, the aforementioned power generation system can be decomposed into multiple components such as the main shaft, main bearing, gearbox, and generator. In this example, the fault units are the individual devices.

[0042] It should be noted that, in order to facilitate the subsequent calculation of the RPN value of each fault unit, after the product is decomposed into multiple fault units in this embodiment, it can be further decomposed to the lowest granularity, that is, the system can be decomposed into individual devices. For example, for a certain product, the product can be decomposed into multiple systems, and each system can be decomposed into several components, and each component may have several failure modes.

[0043] Therefore, this application can take the actual object of analysis and research as the target object, decompose it into fault units of different granularity levels, which facilitates the subsequent determination of the risk level of each fault unit and improves the applicability of the analysis method in practical applications.

[0044] Step S102: For each faulty unit, collect historical fault data and generate quantitative values ​​for the severity index, occurrence index, and detectability index of the fault cause in the historical fault data.

[0045] The historical fault data includes fault mode data that the fault unit has historically generated and fault cause data that led to each fault mode.

[0046] Specifically, historical fault data is first collected for each faulty unit using different methods. For example, during actual product operation, the product's operating status and parameters are monitored. When a faulty unit fails, the resulting fault mode is recorded in real time. After the fault cause is manually detected by relevant personnel or automatically diagnosed by the fault diagnosis system, the fault mode and its corresponding fault cause are stored in a fault database. When reliability analysis is required, the relevant historical fault data for that faulty unit is retrieved from the database. Alternatively, all possible fault causes corresponding to a given fault mode can be retrieved from a pre-set fault diagnosis database.

[0047] Furthermore, based on the collected historical fault data, fault risk assessment indicators are assigned to each fault cause, namely, severity (S), occurrence (O), and detectability (D) are evaluated based on historical data, and the quantitative values ​​of severity, occurrence, and detectability indicators for each fault cause in the historical fault data are calculated.

[0048] To more clearly illustrate the specific implementation of the quantification methods for the severity, occurrence, and detectability indices in this application, the following example illustrates a quantification method proposed in an embodiment of this application. Figure 2 This is a flowchart illustrating a method for calculating the quantified value of an evaluation index according to an embodiment of this application, as shown below. Figure 2 As shown, the method includes the following steps:

[0049] Step S201: Determine all fault causes corresponding to each fault mode of the faulty unit.

[0050] Specifically, a failure mode may be caused by several failure causes. By collecting the failure causes corresponding to each failure mode when collecting historical failure data, all failure causes corresponding to each failure mode can be determined, so as to facilitate subsequent analysis of each possible failure cause.

[0051] Step S202: Collect quantitative values ​​of severity, occurrence, and detectability indices given by a preset number of experts for each cause of failure.

[0052] Specifically, experts from multiple related fields provide quantitative values ​​for the severity, occurrence, and detectability of each fault cause based on their experience and expertise.

[0053] For example, based on the accuracy of historical evaluations by various experts, a predetermined number of n experts are selected from the field of the target object for quantitative evaluation. The severity (S), occurrence (O), and detectability (D) values ​​given by expert k (k is any one of the n experts) for the i-th failure cause (i is any one of all failure causes corresponding to the current failure mode) are recorded. The risk index for the i-th cause of failure is represented by the following formula:

[0054]

[0055] The meanings of the parameters in the formula are as described above and will not be repeated here.

[0056] Step S203: Construct a quantitative matrix of fault cause risk indicators based on the quantitative values ​​of all the indicators corresponding to the fault causes.

[0057] Specifically, after calculating the quantitative value of each fault cause risk indicator according to the formula in the previous step, the quantitative values ​​of all fault cause risk indicators are summarized to generate the following quantitative matrix of fault cause risk indicators:

[0058]

[0059] In this quantization matrix, each element represents the quantified value of the severity (S), occurrence (O), or detectability (D) of a certain fault cause.

[0060] As can be seen from the above embodiments, the experience data provided by experts is generated for each fault cause under each fault mode in historical fault data. Therefore, when historical fault data is insufficient, it will lead to insufficient experience data and thus inaccurate subjective data. To improve the accuracy of the FMECA analysis method of this application, in one embodiment of this application, after collecting historical fault data and before quantifying the indicators, the following steps are included: determining whether the number of collected historical fault data is greater than or equal to a preset numerical threshold; if the number of historical fault data is less than the numerical threshold, collecting historical fault data of objects similar to the target object.

[0061] Specifically, the preset numerical threshold is determined in advance through extensive experimental verification and other methods. It represents the minimum amount of historical fault data required to meet the needs of reliability analysis. If the amount of historical fault data is less than this threshold, it indicates that the historical data is insufficient, and the subsequent evaluation and calculation results of various indicators may be inaccurate. Furthermore, historical fault data of objects similar to the target object are collected. These similar objects can be those that are similar to the target object in terms of functionality, structure, or other aspects.

[0062] Step S103: Calculate the relative weight of each indicator based on the quantified values ​​of the severity, occurrence, and detectability indicators, and calculate the risk priority number (RPN) of the cause of failure based on the relative weight of each indicator.

[0063] Specifically, relative weights represent the relative importance of each indicator data point. Relative weights can be calculated by dividing the quantified value of the fault cause risk indicator by the quantified value of other risk indicators.

[0064] To more clearly illustrate the specific implementation of calculating the relative weight of each indicator in this application, the following example illustrates a method for calculating relative weights proposed in an embodiment of this application. Figure 3 This is a flowchart illustrating a method for calculating the relative weights of evaluation indicators according to an embodiment of this application, as shown below. Figure 3 As shown, the method includes the following steps:

[0065] Step S301: Compare each risk assessment index of each fault cause with the risk assessment indexes corresponding to other fault causes in the quantification matrix of fault cause risk indicators to obtain the comparison value of the risk assessment index of each fault cause.

[0066] Specifically, the risk assessment indicators are compared using the following formula:

[0067]

[0068] Where R can represent severity (S), occurrence (O), and detectability (D), respectively; that is, the quantified value of any severity, occurrence, or detectability index can be substituted into the above formula for calculation. j can represent other failure causes besides the current failure cause in the quantified matrix of failure cause risk indicators under the current failure mode. Then α Rj It can represent the quantitative value of risk assessment indicators corresponding to other causes of failure.

[0069] In one embodiment of this application, during the comparison, any other fault cause can be arbitrarily selected for comparison, or the average value of the quantified values ​​of the risk assessment indicators of all fault causes in the quantification matrix can be calculated; no limitation is imposed here. Therefore, each risk assessment indicator in each fault cause is sequentially substituted into the above formula for calculation to obtain the comparison value of the risk assessment indicator for each fault cause, i.e., the quantified value of the indicator after comparison.

[0070] Step S302: Based on the comparison values ​​of all the risk assessment indicators for the causes of failure, construct a comparison matrix of the risk assessment indicators for the causes of failure.

[0071] Specifically, the comparison values ​​of various risk indicators corresponding to each cause of failure are summarized to generate the following comparison matrix of risk assessment indicators for failure causes:

[0072]

[0073] In this quantification matrix, each element represents a quantified value of the relative importance of a certain fault cause, based on a comparison of its severity (S), occurrence (O), or detectability (D).

[0074] Step S303: Normalize each indicator in the comparison matrix to obtain the relative weight of each indicator.

[0075] Specifically, each element in the comparison matrix above is normalized using the following formula:

[0076]

[0077] The meanings of the parameters in the formula are the same as those in the above embodiments, and will not be repeated here. Substitute each element of the comparison matrix into the above formula to calculate the relative weights of the severity, occurrence, and detectability evaluation indicators.

[0078] Furthermore, by summing the relative weights of each evaluation indicator, a relative weight matrix is ​​generated as shown below:

[0079]

[0080] Furthermore, after calculating the relative weight of the risk assessment index for each fault cause using the calculation method described in the above embodiments, the risk priority number (RPN) for that fault cause is calculated based on the relative weight of all indicators corresponding to any fault cause.

[0081] As an example, the RPN (RPN) for the i-th cause of failure can be calculated using the following formula. reason_i ):

[0082]

[0083] Therefore, the RPN value of each fault cause under the current fault mode j can be calculated sequentially.

[0084] Step S104: Generate the RPN value for each fault unit based on the Risk Priority Number (RPN) value of the fault cause corresponding to each fault unit, and analyze the key fault units based on the RPN values ​​of all fault units.

[0085] Specifically, based on the product or system decomposition method in step S101, the RPN values ​​are calculated sequentially from bottom to top according to the calculated RPN value of the lowest level of failure cause, until the RPN value of each failure unit is calculated.

[0086] As an example, when the target object is a product, after calculating the RPN value for each failure cause, the RPN value for the j-th failure mode is first calculated using the following formula:

[0087] RPN mode_j =∑RPN reason_i

[0088] Where j represents any of the all failure modes. That is, the RPN value of the j-th failure mode (RPN). mode_j It is equal to the sum of the RPN values ​​of all the corresponding fault causes.

[0089] Then, the RPN value of the p-th part is calculated using the following formula:

[0090] RPN part_p =∑RPNmode_j

[0091] Where p represents any one of the decomposed parts. That is, the RPN value of the p-th part (RPN). part_p It is equal to the sum of the RPN values ​​of all its corresponding failure modes.

[0092] Finally, the RPN value of the s-th system is calculated using the following formula:

[0093] RPN system_s =∑RPN part_p

[0094] Where s represents any one of the decomposed systems. That is, the RPN value of the s-th system (RPN). system_s It is equal to the sum of the RPN values ​​of all its corresponding parts.

[0095] Furthermore, based on the RPN values ​​of all faulty units, critical faulty units with high risk levels are identified. One possible approach is to sort all faulty units by their risk priority RPN values ​​from highest to lowest. Based on this sorting, critical faulty units with risk levels above a preset risk level threshold are identified, and corresponding fault avoidance measures are then implemented for these critical faulty units. The risk level threshold can be a pre-determined RPN value used to distinguish between high and low risk levels; when the calculated RPN value of a faulty unit is greater than this threshold, it indicates a higher risk level for that faulty unit. After sorting the RPN values ​​of all faulty units, faulty units with RPN values ​​greater than the risk level threshold are identified as critical faulty units.

[0096] Furthermore, for the selected critical fault units, corresponding fault avoidance measures are taken to prevent the occurrence of faults. In one embodiment of this application, an operation and maintenance plan and preventive measures are formulated based on the fault causes and fault modes of the critical fault units, and the operation and maintenance plan and the preventive measures are executed on the critical fault units.

[0097] In one embodiment of this application, since the Reliability Probability Notice (RPN) values ​​for each failure mode and cause under the critical failure unit have been calculated, failure modes and causes with higher RPN values ​​are selected. For these failure modes and causes, targeted operation and maintenance plans and preventative measures are formulated by referring to effective maintenance measures recorded in the historical failure maintenance knowledge base. Therefore, this embodiment can select critical failure units with high risk levels based on the quantitative analysis results of reliability analysis, and take measures to avoid failures with a higher probability of occurrence and a greater impact on the product, so as to eliminate the cause of failure in a timely manner, avoid failures, and help ensure the normal and safe operation of the product.

[0098] In summary, the FMECA analysis method based on relative data importance in this application constructs a reliability analysis FMECA method based on the relative data importance algorithm. It quantifies the three failure risk assessment indicators—severity, occurrence, and detectability—given by domain experts based on experience, calculates the relative weights of each indicator, and establishes a Risk Priority Number (RPN) based on these relative weights to assess the risk level of the failure unit. This method collects more historical data and calculates the relative importance of the introduced subjective data, further quantifying the reliability analysis indicators, i.e., the three failure risk assessment indicators, reducing the uncertainty of the reliability analysis results. The RPN value generated by the calculated relative weights facilitates the determination of the risk level of each unit. Therefore, this method improves the reliability and accuracy of FMECA analysis, reduces the uncertainty of reliability analysis results, and helps ensure the normal and safe operation of the product.

[0099] To more clearly illustrate the implementation process of the FMECA analysis method based on the relative importance of data in this application, a specific embodiment of the FMECA analysis method based on the relative importance of data will be described in detail below. Figure 4 A flowchart illustrating a specific FMECA analysis method based on the relative importance of data proposed in this application is shown below. Figure 4 As shown, the analysis method of this embodiment includes the following steps:

[0100] Step S401: Product and system identification.

[0101] Step S402: Decompose the product or system.

[0102] Step S403: Collect historical fault data of the decomposition unit.

[0103] Step S404: Determine whether the historical fault data is sufficient. If so, collect the expert's experience in judging the fault. If not, collect data on similar products.

[0104] Step S405: Evaluate the severity, occurrence, and detectability based on historical fault data.

[0105] Step S406: Calculate the relative weights of the severity, occurrence, and detectability evaluation metrics.

[0106] Step S407: Determine the RPN value for the cause and mode of the fault.

[0107] Step S408: Determine the RPN value of the decomposition unit.

[0108] Step S409: Assessment of uncertainty in the analysis results.

[0109] Step S410: Propose operation and maintenance suggestions and preventive measures.

[0110] It should be noted that the specific implementation of each step in this embodiment can be referred to the relevant description in the above embodiments, and will not be repeated here.

[0111] To implement the above embodiments, this application also proposes an FMECA analysis system based on the relative importance of data. Figure 5 This is a schematic diagram of the structure of an FMECA analysis system based on the relative importance of data proposed in an embodiment of this application, as shown below. Figure 5 As shown, the device includes a decomposition module 100, a generation module 200, a calculation module 300, and an analysis module 400.

[0112] The decomposition module 100 is used to decompose the target object to be analyzed and generate multiple fault units.

[0113] The generation module 200 is used to collect historical fault data for each fault unit and generate quantitative values ​​of the severity index, occurrence index and detectability index of the fault cause in the historical fault data.

[0114] The calculation module 300 is used to calculate the relative weight of each indicator based on the quantified values ​​of the severity indicator, occurrence indicator, and detectability indicator, and to calculate the risk priority number (RPN) value of the cause of failure based on the relative weight of each indicator.

[0115] The analysis module 400 is used to generate the RPN value of each fault unit based on the risk priority number (RPN) value of the fault cause corresponding to each fault unit, and to analyze the key fault units based on the RPN values ​​of all fault units.

[0116] Optionally, in one embodiment of this application, the system further includes: a judgment module, used to judge whether the number of collected historical fault data is greater than or equal to a preset numerical threshold; and a collection module, used to collect historical fault data of objects similar to the target object when the number of historical fault data is less than the numerical threshold.

[0117] Optionally, in one embodiment of this application, the generation module 200 is specifically used to: determine all failure causes corresponding to each failure mode of the fault unit; collect the quantified values ​​of severity, occurrence, and detectability indicators given by a preset number of experts for each failure cause; and construct a quantified matrix of failure cause risk indicators based on the quantified values ​​of the indicators corresponding to all failure causes.

[0118] Optionally, in one embodiment of this application, the calculation module 300 is specifically used to: compare each risk assessment index of each fault cause with the risk assessment index corresponding to other fault causes in the quantification matrix of fault cause risk indicators in turn to obtain the comparison value of the risk assessment index of each fault cause; construct a comparison matrix of fault cause risk assessment indicators based on the comparison values ​​of all fault cause risk assessment indicators; and normalize each indicator in the comparison matrix to obtain the relative weight of each indicator.

[0119] Optionally, in one embodiment of this application, when the fault unit is at the system level, the analysis module 400 is further configured to: for each fault unit, add up the RPN values ​​of all fault causes corresponding to each fault mode to obtain the RPN value of each fault mode; add up the RPN values ​​of all fault modes corresponding to each component to obtain the RPN value of each component; add up the RPN values ​​of all components corresponding to each system to obtain the RPN value of each system.

[0120] Optionally, in one embodiment of this application, the analysis module 400 is further configured to: formulate operation and maintenance plans and preventive measures based on the failure causes and failure modes of the critical failure units, and execute the operation and maintenance plans and preventive measures on the critical failure units.

[0121] It should be noted that the foregoing explanation of the embodiment of the FMECA analysis method based on the relative importance of data also applies to the system of this embodiment, and will not be repeated here.

[0122] In summary, the FMECA analysis system based on relative data importance in this application constructs a reliability analysis FMECA method based on the relative data importance algorithm. It quantifies the three failure risk assessment indicators—severity, occurrence, and detectability—given by domain experts based on experience, calculates the relative weights of each indicator, and establishes a Risk Priority Number (RPN) based on these relative weights to assess the risk level of the failure unit. By collecting more historical data and calculating the relative importance of the introduced subjective data, the system further quantifies the reliability analysis indicators, i.e., the three failure risk assessment indicators, reducing the uncertainty of the reliability analysis results. The RPN value generated by the calculated relative weights facilitates the determination of the risk level of each unit. Therefore, this system improves the reliability and accuracy of FMECA analysis, reduces the uncertainty of reliability analysis results, and helps ensure the normal and safe operation of the product.

[0123] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the FMECA analysis method based on the relative importance of data as described in any of the above embodiments.

[0124] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0125] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0126] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0128] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0129] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0130] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0131] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A FMECA analysis method based on the relative importance of data, characterized in that, Includes the following steps: The target object to be analyzed is decomposed into multiple fault units. The target object includes wind turbines or photovoltaic power stations. Based on the different functions or equipment types of each unit in the target object, the target object is decomposed into multiple fault units. The fault units include various systems and various equipment. For each faulty unit, historical fault data is collected, and quantitative values ​​of the severity, occurrence, and detectability indices of the fault causes in the historical fault data are generated. The historical fault data includes fault mode data that the faulty unit has historically generated and fault cause data that led to each fault mode, including: Determine all fault causes corresponding to each fault mode of the fault unit; Collect quantitative values ​​of the severity index, occurrence index, and detectability index given by a preset number of experts for each of the fault causes; Based on the quantitative values ​​of the indicators corresponding to all failure causes, a quantitative matrix of failure cause risk indicators is constructed. The relative weight of each indicator is calculated based on the quantified values ​​of the severity indicator, the occurrence indicator, and the detectability indicator, and the risk priority number (RPN) value of the cause of failure is calculated based on the relative weight of each indicator. This includes: comparing each risk assessment indicator of each cause of failure with the risk assessment indicators corresponding to other causes of failure in the quantification matrix of the risk indicators of the cause of failure, and obtaining the comparison value of the risk assessment indicator of each cause of failure. Based on the comparison values ​​of the risk assessment indicators for all causes of failure, a comparison matrix of risk assessment indicators for causes of failure is constructed. Normalize each indicator in the comparison matrix to obtain the relative weight of each indicator; Based on the Risk Priority Number (RPN) value corresponding to the fault cause for each fault unit, an RPN value for each fault unit is generated, and the key fault units are analyzed based on the RPN values ​​of all fault units.

2. The analytical method according to claim 1, characterized in that, Before generating the quantified values ​​of the severity index, occurrence index, and detectability index of the fault cause in the historical fault data, the method further includes: Determine whether the amount of collected historical fault data is greater than or equal to a preset numerical threshold; If the number of historical fault data is less than the numerical threshold, historical fault data of objects similar to the target object are collected.

3. The analytical method according to claim 1, characterized in that, When the faulty unit is at the system level, generating the RPN value for each faulty unit includes: For each fault unit, the RPN values ​​of all fault causes corresponding to each fault mode are summed to obtain the RPN value of each fault mode; The RPN values ​​for all failure modes corresponding to each part are summed to obtain the RPN value for each part. The RPN values ​​of all parts corresponding to each system are added together to obtain the RPN value of each system.

4. The analytical method according to claim 1, characterized in that, After the critical fault units are identified through analysis, the following is also included: Based on the cause and mode of failure of the critical failure unit, formulate an operation and maintenance plan and preventive measures, and implement the operation and maintenance plan and preventive measures on the critical failure unit.

5. An FMECA analysis system based on the relative importance of data, characterized in that, Includes the following modules: The decomposition module is used to decompose the target object to be analyzed and generate multiple fault units. The target object includes wind turbines or photovoltaic power stations. According to the different functions or equipment types of each unit in the target object, the target object is decomposed into multiple fault units. The fault units include various systems and various equipment. The generation module is used to collect historical fault data for each fault unit and generate quantitative values ​​of the severity index, occurrence index and detectability index of the fault cause in the historical fault data. The historical fault data includes fault mode data that the fault unit has generated in the past and fault cause data that caused each fault mode. The calculation module is used to calculate the relative weight of each indicator based on the quantified values ​​of the severity indicator, the occurrence indicator, and the detectability indicator, and to calculate the risk priority number (RPN) value of the cause of failure based on the relative weight of each indicator. The analysis module is used to generate the RPN value of each fault unit based on the Risk Priority Number (RPN) value of the fault cause corresponding to each fault unit, and to analyze the key fault units based on the RPN values ​​of all fault units. The generation module is specifically used for: Determine all fault causes corresponding to each fault mode of the fault unit; Collect quantitative values ​​of the severity index, occurrence index, and detectability index given by a preset number of experts for each of the fault causes; Based on the quantitative values ​​of the indicators corresponding to all failure causes, a quantitative matrix of failure cause risk indicators is constructed. The calculation module is used to compare each risk assessment index of each fault cause with the risk assessment index corresponding to other fault causes in the quantification matrix of the fault cause risk index in turn, and obtain the comparison value of the risk assessment index of each fault cause. Based on the comparison values ​​of the risk assessment indicators for all causes of failure, a comparison matrix of risk assessment indicators for causes of failure is constructed. Each indicator in the comparison matrix is ​​normalized to obtain the relative weight of each indicator.

6. The analysis system according to claim 5, characterized in that, Also includes: The judgment module is used to determine whether the number of collected historical fault data is greater than or equal to a preset numerical threshold. The collection module is used to collect historical fault data of objects similar to the target object when the number of historical fault data is less than the numerical threshold.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the FMECA analysis method based on the relative importance of data as described in any one of claims 1-4.

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