CN114116430A - System reliability analysis method, device and equipment - Google Patents
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- G06F11/30—Monitoring
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Abstract
The application provides a system reliability analysis method, a device and equipment, firstly, the fault mode and the influence analysis are carried out on the fault of the target system, each fault mode of each component in the target system is obtained, then N groups of severity assessment values, likelihood assessment values and detectability assessment values corresponding to the fault modes are obtained, the weighted average value of the severity assessment value, the likelihood assessment value and the detectability assessment value corresponding to each fault mode is calculated, and finally the weighted average value of the severity assessment value, the likelihood assessment value and the detectability assessment value is used as input data, and carrying out hazard analysis on each fault mode to obtain a hazard predicted value of each fault mode, and using the hazard predicted value as a reliability index of the target system to realize reliability prediction of the target system.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method, a device and equipment for analyzing system reliability.
Background
Before the equipment is subjected to formal work or mass production, in order to ensure that the equipment can reliably work in a long time, the reliability of the equipment needs to be analyzed. How to analyze the reliability of the operation state of the equipment to predict whether the equipment can reliably work for a long time is one of the technical problems to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, and a device for analyzing system reliability, so as to implement reliability prediction of a device.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
a system reliability analysis method is used for carrying out fault analysis on a target system, and comprises the following steps:
analyzing the fault mode and the influence of the fault of the target system to obtain each fault mode of each component in the target system;
acquiring N groups of severity evaluation values, possibility evaluation values and detectability evaluation values corresponding to the fault modes, wherein N is not less than 2, and each fault mode corresponds to N groups of severity evaluation values, possibility evaluation values and detectability index evaluation values;
calculating a weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value corresponding to each fault mode;
taking the weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value as input data, and carrying out hazard analysis on each fault mode to obtain a hazard predicted value of each fault mode;
outputting a hazard prediction value for each of the failure modes.
Optionally, in the system reliability analysis method, the target system is a floating wind turbine generator system on the sea.
Optionally, in the system reliability analysis method, performing a hazard analysis on each of the failure modes by using a weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value as input data, includes:
and inputting the weighted average of the severity assessment value, the possibility assessment value and the detectability assessment value by adopting a neural network algorithm, and carrying out CA analysis by a sigmod activation function algorithm.
Optionally, in the method for analyzing system reliability, the outputting a predicted value of the hazard of each fault mode includes:
judging whether the hazard predicted value of each fault mode is smaller than a preset value;
when the hazard predicted value of each fault mode is smaller than a preset value, outputting prompt information for representing the reliable operation of a target system;
when a fault mode with the hazard predicted value larger than the preset value exists, prompt information for representing unreliable operation of the target system is output, and equipment components, fault influences and fault reasons corresponding to the fault mode with the hazard predicted value larger than the preset value are output.
Optionally, in the system reliability analysis method, the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value are severity evaluation values, likelihood evaluation values, and detectability evaluation values obtained by evaluating, by different evaluation subjects, each component based on configuration parameters of each component in the target system.
A system reliability analysis device for performing fault analysis on a target system comprises:
the FMEA analysis unit is used for analyzing the fault modes and influences of the faults of the target system to obtain the fault modes of all components in the target system;
an evaluation value acquisition unit for acquiring N sets of severity evaluation values, likelihood evaluation values, and detectability evaluation values corresponding to the failure modes, N being not less than 2, each failure mode corresponding to the N sets of severity evaluation values, likelihood evaluation values, and detectability index evaluation values;
an evaluation value processing unit for calculating a weighted average of the severity evaluation value, the likelihood evaluation value and the detectability evaluation value corresponding to each of the failure modes;
the CA analysis unit is used for performing hazard analysis on each fault mode by taking the weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value as input data to obtain a hazard prediction value of each fault mode; outputting a hazard prediction value for each of the failure modes.
Optionally, in the system reliability analysis apparatus, when performing hazard analysis on each of the failure modes by using a weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value as input data, the CA analysis unit is specifically configured to:
and inputting the weighted average of the severity assessment value, the possibility assessment value and the detectability assessment value by adopting a neural network algorithm, and carrying out CA analysis by a sigmod activation function algorithm.
Optionally, in the system reliability analysis device, when the CA analysis unit outputs the predicted hazard value of each fault mode, the CA analysis unit is specifically configured to:
judging whether the hazard predicted value of each fault mode is smaller than a preset value;
when the hazard predicted value of each fault mode is smaller than a preset value, outputting prompt information for representing the reliable operation of a target system;
when a fault mode with the hazard predicted value larger than the preset value exists, prompt information for representing unreliable operation of the target system is output, and equipment components, fault influences and fault reasons corresponding to the fault mode with the hazard predicted value larger than the preset value are output.
A system reliability analysis device for performing fault analysis on a target system, comprising:
a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the system reliability analysis method according to any one of the above embodiments.
Optionally, the analysis device is a PC.
Based on the above technical solution, in the above solution provided by the embodiment of the present invention, first, a failure mode and an influence analysis are performed on a failure of the target system to obtain each failure mode of each component in the target system, then N sets of severity assessment values, likelihood assessment values and detectability assessment values corresponding to the failure modes are obtained, a weighted average of the severity assessment values, the likelihood assessment values and the detectability assessment values corresponding to each failure mode is calculated, and finally the weighted average of the severity assessment values, the likelihood assessment values and the detectability assessment values is used as input data, and carrying out hazard analysis on each fault mode to obtain a hazard predicted value of each fault mode, and using the hazard predicted value as a reliability index of the target system to realize reliability prediction of the target system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart of a system reliability analysis method disclosed in an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a floating wind generating set on the sea;
FIG. 3 is a schematic flow chart illustrating a system reliability analysis method according to another embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a system reliability analysis apparatus disclosed in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system reliability analysis device disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Failure Mode, influence and Criticality Analysis (hereinafter, referred to as FMECA) is to determine the influence of each Failure Mode on the work of a product according to the Analysis of the Failure modes, find out a single-point Failure, and determine the Criticality of the Failure modes according to the severity and occurrence probability of the Failure modes. By single point failure is meant a partial failure that causes a product failure and has no redundant or replacement working program as a remedy. FMECA includes failure mode and impact analysis (FMEA) and hazard analysis (CA).
The applicant finds that the traditional FMECA quantitative method mainly comprises a hazard matrix method and a Risk Priority Number (RPN) method, and the basic idea is to comprehensively consider the influence of the fault possibility and the severity degree, sort the fault modes and find out more important fault modes. However, the reliability of the output result in this manner is low.
In the technical scheme disclosed by the embodiment of the application, the system is divided according to system levels; classifying and collecting all possible faults of the target system according to the system and the components, and performing FMEA (failure mode analysis) on all possible faults, wherein the faults include components, main failure modes, results and causes; asking related domain experts to evaluate the failure modes of the components in three dimensions according to respective domains, wherein the three dimensions comprise severity, possibility and detectability; assigning the evaluation values of different experts to different weight coefficients, and calculating a severity evaluation value, a possibility evaluation value and a weighted average score of the detectability evaluation value corresponding to each fault mode of the single component; and carrying out hazard analysis on the severity evaluation value, the possibility evaluation value and the detectability evaluation value to obtain a hazard prediction value of each fault mode, taking the hazard prediction value as a hazard value of the fault mode, and judging whether related components of the system can work reliably for a long time based on the hazard value so as to judge whether equipment can work reliably for a long time.
Fig. 1 is a schematic flowchart of a system reliability analysis method disclosed in an embodiment of the present application, where the method is used for performing fault analysis on a target system, and referring to fig. 1, the method may include: steps S101-S105.
Step S101: and analyzing the fault mode and the influence of the fault of the target system to obtain each fault mode of each component in the target system.
In this step, the target system is classified into system and component levels by itself according to the structure of the target system, wherein the independent device component is a minimum functional unit.
In the technical solutions disclosed in the above embodiments of the present application, the target system is any system that needs to be subjected to reliability evaluation, for example, in the present solution, the target system may be a floating wind turbine generator system, and the floating wind turbine generator system is divided according to the form shown in fig. 2.
And classifying and collecting all possible faults of the target system according to the system and the components, and performing FMEA (failure mode analysis) on all the possible faults to obtain each fault mode, fault influence, fault reason and the like of each component in the target system.
Step S102: n sets of severity evaluation values, likelihood evaluation values, and detectability evaluation values corresponding to the failure modes are acquired.
In this step, the configuration parameters, the operating environment, the operating parameters, and the failure modes, the failure influences, and the failure causes of the components are sent to N assessment experts, and after each assessment expert analyzes the failure modes, the severity assessment value, the possibility assessment value, and the detectability index assessment value corresponding to the failure modes of the components are given.
And N is not less than 2, and after N evaluation experts evaluate, each fault mode corresponds to N groups of severity evaluation values, possibility evaluation values and detectability index evaluation values.
For example, taking a floating wind turbine generator system on the sea as an example, the N sets of severity evaluation values, likelihood evaluation values, and detectability evaluation values corresponding to the failure modes can be seen from table 1.
Step S103: and calculating a weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value corresponding to each fault mode.
In this step, the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value are severity evaluation values, likelihood evaluation values, and detectability evaluation values obtained by different evaluation subjects evaluating the respective components based on the configuration parameters of the respective components in the target system. The different evaluation subjects may refer to different experts, different weights are given to the evaluation values given by the experts according to authority degrees of the evaluation values of the different experts, and after the severity evaluation value, the possibility evaluation value and the detectability evaluation value corresponding to the fault mode are obtained through calculation, a weighted average value of the severity evaluation value, the possibility evaluation value and the detectability evaluation value corresponding to each fault mode is obtained through calculation according to the weight values corresponding to the experts.
For example, the evaluated value of the a1 failure mode of expert A at the a component is M1, the evaluated value of the a2 failure mode of A at the a component is M2, the evaluated value of the a1 failure mode of expert B at the a component is M3, the evaluated value of the a2 failure mode of B at the a component is M4, for example, the evaluated value of the B1 failure mode of expert A at the B component is M5, the evaluated value of the B2 failure mode of A at the B component is M6, the evaluated value of the B1 failure mode of expert B at the B component is M7, and the evaluated value of the B2 failure mode of B at the B component is M8.
In calculating the weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value, a formula may be used, taking the weighted average of the severity evaluation values as an example
Is obtained by calculation in the formula
As a weighted average of severity estimates, x1，x2，...，xkEvaluation value f for a fault pattern of a component by an expert1，f2，...，fkAre weight values given to evaluation values given by different experts, and n is the number of experts. Similarly, a weighted average of the likelihood estimates may be calculated using the above formula
Weighted average of detectability assessment value
Step S104: and performing hazard analysis on each fault mode by using the weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value as input data to obtain a hazard predicted value of each fault mode.
In this step, after the weighted average of the cool degree evaluation value, the likelihood evaluation value, and the detectability evaluation value is calculated, CA analysis (hazard analysis) is performed using the weighted average of the cool degree evaluation value, the likelihood evaluation value, and the detectability evaluation value as an input layer, and a hazard prediction value of each of the failure modes is obtained. In the scheme, the range of the hazard prediction value is 0 to 1, the closer the hazard prediction value is to 0, the lower the reliability of the corresponding component is, and the closer the hazard prediction value is to 1, the higher the reliability of the corresponding component is.
Step S105: outputting a hazard prediction value for each of the failure modes.
In this step, after the hazard predicted value of each failure mode is determined, the hazard predicted values of each failure mode corresponding to all the components in the target system may be output in a form of a list, so that a user may determine the reliability of each component in the target system, and further determine the reliability of the target system.
In the technical solution disclosed in the embodiment of the present application, when performing a hazard analysis on each failure mode by using the weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value as input data, a neural network model may be used to process the weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value, and then perform the hazard analysis, specifically, the step may include:
and inputting the weighted average of the severity assessment value, the possibility assessment value and the detectability assessment value by adopting a neural network algorithm, and carrying out CA analysis by a sigmod activation function algorithm.
In a technical solution disclosed in another embodiment of the present application, in order to facilitate a user to know an analysis result of reliability analysis in time, referring to fig. 3, in the technical solution, the outputting a predicted hazard value of each fault mode includes: step S301 to step S303.
Step S301: and judging whether the hazard predicted value of each fault mode is smaller than a preset value.
In the scheme, when the fact that the hazard predicted values of all the fault modes of all the components are smaller than the preset value is detected, the target system can work safely and reliably, otherwise, the fact that related components of the target system are difficult to work safely and reliably is indicated. The preset value can be set according to actual requirements.
Step S302: and when the hazard predicted value of each fault mode is smaller than a preset value, outputting prompt information for representing the reliable operation of the target system.
Step S303: when a fault mode with the hazard predicted value larger than the preset value exists, prompt information for representing unreliable operation of the target system is output, and equipment components, fault influences and fault reasons corresponding to the fault mode with the hazard predicted value larger than the preset value are output.
In this step, when a fault mode exists in which the hazard prediction value is greater than the preset value, the equipment component, the fault influence and the fault reason corresponding to the fault mode in which the hazard prediction value is greater than the preset value may be output, and after the designer acquires the equipment component, the fault influence and the fault reason corresponding to the fault mode in which the hazard prediction value is greater than the preset value, the designer may redesign the corresponding component of the target system based on these contents, thereby improving the reliability of the target system.
The present embodiment discloses a system reliability analysis device, which is used for performing fault analysis on a target system, and please refer to the content of the above method embodiment for the specific working content of each unit in the device.
The system reliability analysis device provided by the embodiment of the invention is described below, and the system reliability analysis device described below and the system reliability analysis method described above can be referred to correspondingly.
Referring to fig. 4, a system reliability analysis apparatus disclosed in an embodiment of the present application may include: an FMEA analyzing unit a, an evaluation value acquiring unit B, an evaluation value processing unit C, and a CA analyzing unit D.
The FMEA analysis unit A corresponds to the method and is used for analyzing the fault mode and the influence of the fault of the target system to obtain each fault mode of each component in the target system;
an evaluation value acquisition unit B corresponding to the above method for acquiring N sets of severity evaluation values, likelihood evaluation values, and detectability evaluation values corresponding to the failure modes, N being not less than 2, each failure mode corresponding to the N sets of severity evaluation values, likelihood evaluation values, and detectability index evaluation values;
an evaluation value processing unit C, corresponding to the above method, for calculating a weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value corresponding to each of the failure modes;
a CA analysis unit D corresponding to the method, which is used for taking the weighted average value of the severity evaluation value, the possibility evaluation value and the detectability evaluation value as input data, and carrying out hazard analysis on each fault mode to obtain a hazard predicted value of each fault mode; outputting a hazard prediction value for each of the failure modes.
Corresponding to the above method, in the above solution, the CA analysis unit is specifically configured to, when performing hazard analysis on each of the failure modes by using a weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value as input data:
and inputting the weighted average of the severity assessment value, the possibility assessment value and the detectability assessment value by adopting a neural network algorithm, and carrying out CA analysis by a sigmod activation function algorithm.
Corresponding to the above method, when the CA analysis unit outputs the predicted hazard value of each failure mode, the CA analysis unit is specifically configured to:
judging whether the hazard predicted value of each fault mode is smaller than a preset value;
when the hazard predicted value of each fault mode is smaller than a preset value, outputting prompt information for representing the reliable operation of a target system;
when a fault mode with the hazard predicted value larger than the preset value exists, prompt information for representing unreliable operation of the target system is output, and equipment components, fault influences and fault reasons corresponding to the fault mode with the hazard predicted value larger than the preset value are output.
Fig. 5 is a hardware structure diagram of a system reliability analysis device according to an embodiment of the present invention, which is shown in fig. 5 and may include: at least one processor 100, at least one communication interface 200, at least one memory 300, and at least one communication bus 400;
in the embodiment of the present invention, the number of the processor 100, the communication interface 200, the memory 300, and the communication bus 400 is at least one, and the processor 100, the communication interface 200, and the memory 300 complete the communication with each other through the communication bus 400; it is clear that the communication connections shown by the processor 100, the communication interface 200, the memory 300 and the communication bus 400 shown in fig. 5 are merely optional;
optionally, the communication interface 200 may be an interface of a communication module, such as an interface of a GSM module;
the processor 100 may be a central processing unit CPU or an application Specific Integrated circuit asic or one or more Integrated circuits configured to implement embodiments of the present invention.
Wherein, the processor 100 is specifically configured to:
analyzing the fault mode and the influence of the fault of the target system to obtain each fault mode of each component in the target system;
acquiring N groups of severity evaluation values, possibility evaluation values and detectability evaluation values corresponding to the fault modes, wherein N is not less than 2, and each fault mode corresponds to N groups of severity evaluation values, possibility evaluation values and detectability index evaluation values;
calculating a weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value corresponding to each fault mode;
taking the weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value as input data, and carrying out hazard analysis on each fault mode to obtain a hazard predicted value of each fault mode;
outputting a hazard prediction value for each of the failure modes.
The processor 100 is further configured to perform other steps of the reliability analysis method disclosed in the above embodiments of the present application.
The analysis device can be an intelligent terminal with a data processing function, such as a PC (personal computer) or a computer.
For convenience of description, the above system is described with the functions divided into various modules, which are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations as the present application.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A system reliability analysis method is used for carrying out fault analysis on a target system, and is characterized by comprising the following steps:
analyzing the fault mode and the influence of the fault of the target system to obtain each fault mode of each component in the target system;
acquiring N groups of severity evaluation values, possibility evaluation values and detectability evaluation values corresponding to the fault modes, wherein N is not less than 2, and each fault mode corresponds to N groups of severity evaluation values, possibility evaluation values and detectability index evaluation values;
calculating a weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value corresponding to each fault mode;
taking the weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value as input data, and carrying out hazard analysis on each fault mode to obtain a hazard predicted value of each fault mode;
outputting a hazard prediction value for each of the failure modes.
2. The system reliability analysis method according to claim 1, wherein the target system is a floating offshore wind turbine.
3. The system reliability analysis method according to claim 1, wherein performing a hazard analysis for each of the failure modes using a weighted average of the severity assessment value, the likelihood assessment value, and the detectability assessment value as input data includes:
and inputting the weighted average of the severity assessment value, the possibility assessment value and the detectability assessment value by adopting a neural network algorithm, and carrying out CA analysis by a sigmod activation function algorithm.
4. The system reliability analysis method according to claim 1, wherein the outputting the predicted value of the hazard for each of the failure modes comprises:
judging whether the hazard predicted value of each fault mode is smaller than a preset value;
when the hazard predicted value of each fault mode is smaller than a preset value, outputting prompt information for representing the reliable operation of a target system;
when a fault mode with the hazard predicted value larger than the preset value exists, prompt information for representing unreliable operation of the target system is output, and equipment components, fault influences and fault reasons corresponding to the fault mode with the hazard predicted value larger than the preset value are output.
5. The system reliability analysis method according to claim 1, wherein the severity evaluation value, the likelihood evaluation value and the detectability evaluation value are a severity evaluation value, a likelihood evaluation value and a detectability evaluation value obtained by evaluating each component by different evaluation subjects based on configuration parameters of each component in the target system.
6. A system reliability analysis apparatus for performing fault analysis on a target system, comprising:
the FMEA analysis unit is used for analyzing the fault modes and influences of the faults of the target system to obtain the fault modes of all components in the target system;
an evaluation value acquisition unit for acquiring N sets of severity evaluation values, likelihood evaluation values, and detectability evaluation values corresponding to the failure modes, N being not less than 2, each failure mode corresponding to the N sets of severity evaluation values, likelihood evaluation values, and detectability index evaluation values;
an evaluation value processing unit for calculating a weighted average of the severity evaluation value, the likelihood evaluation value and the detectability evaluation value corresponding to each of the failure modes;
the CA analysis unit is used for performing hazard analysis on each fault mode by taking the weighted average of the severity evaluation value, the possibility evaluation value and the detectability evaluation value as input data to obtain a hazard prediction value of each fault mode; outputting a hazard prediction value for each of the failure modes.
7. The system reliability analysis device according to claim 6, wherein the CA analysis unit, when performing the hazard analysis for each of the failure modes using the weighted average of the severity evaluation value, the likelihood evaluation value, and the detectability evaluation value as input data, is specifically configured to:
and inputting the weighted average of the severity assessment value, the possibility assessment value and the detectability assessment value by adopting a neural network algorithm, and carrying out CA analysis by a sigmod activation function algorithm.
8. The system reliability analysis device according to claim 6, wherein the CA analysis unit, when outputting the predicted hazard value for each of the failure modes, is specifically configured to:
judging whether the hazard predicted value of each fault mode is smaller than a preset value;
when the hazard predicted value of each fault mode is smaller than a preset value, outputting prompt information for representing the reliable operation of a target system;
when a fault mode with the hazard predicted value larger than the preset value exists, prompt information for representing unreliable operation of the target system is output, and equipment components, fault influences and fault reasons corresponding to the fault mode with the hazard predicted value larger than the preset value are output.
9. A system reliability analysis apparatus for performing fault analysis on a target system, comprising:
a memory and a processor;
the memory is used for storing programs;
the processor, configured to execute the program, implementing the steps of the system reliability analysis method according to any one of claims 1 to 6.
10. The system reliability analysis device according to claim 9, wherein the analysis device is a PC.
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| CN115169038A (en) * | 2022-07-06 | 2022-10-11 | 中国华能集团清洁能源技术研究院有限公司 | FMECA-based offshore floating type fan reliability analysis method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115169038A (en) * | 2022-07-06 | 2022-10-11 | 中国华能集团清洁能源技术研究院有限公司 | FMECA-based offshore floating type fan reliability analysis method and device |
| CN115169038B (en) * | 2022-07-06 | 2024-02-09 | 中国华能集团清洁能源技术研究院有限公司 | FMECA-based reliability analysis method and device for offshore floating fan |
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