RAM (Random Access Memory) performance evaluation method and device of rail transit signal product and medium

Abstract

The invention discloses an RAM performance evaluation method and device for a rail transit signal product and a medium, and the method comprises the steps: carrying out the reliability prediction of the rail transit signal product according to a reliability analysis result, and obtaining the reliability prediction information; analyzing and processing the fault information of the rail transit signal product, and outputting field operation data of the rail transit signal product; performing compatibility test on the reliability prediction information and the field operation data by using the Bayesian theorem, and fusing the reliability prediction information and the field operation data during compatibility to obtain fused posterior distribution; and converting posterior information corresponding to the fused posterior distribution into a failure rate input reliability model, and outputting an RAM performance evaluation result. RAM performance data in the full life cycle stage of the rail transit signal product is fully mined, and field operation data in the operation stage and reliability prediction information in the design and development stage are fused, so that the RAM performance of the rail transit signal product is rapidly and accurately evaluated.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device and medium for evaluating the RAM performance of a rail transit signal product. Background Art

[0002] For high-safety and high-reliability rail transit signal systems, during the warranty period and operation and maintenance phase of the project, it is necessary to continuously monitor and evaluate the reliability, availability, and maintainability (RAM) performance of the rail transit signal systems to verify the compliance of RAM indicators.

[0003] The existing technical solution is to establish a Failure Report Analysis and Corrective Action System (FRACAS) for the rail transit signal system. FRACAS is used to collect on-site operation data during the operation and maintenance phase, record effective operation time, faults or defects, and classify faults or defects according to their severity and criticality.

[0004] However, existing technical solutions all rely on on-site operation data during the operation and maintenance phase to conduct real RAM performance evaluation. The on-site data during the operation and maintenance phase alone cannot respond to the RAM indicator verification requirements during the warranty period (usually one to two years), and it is difficult to prove that the rail transit signal products in the rail transit signal system can be safely and reliably operated throughout their life cycle. In other words, existing technology cannot quickly and accurately evaluate the RAM performance of rail transit signal products. Summary of the invention

[0005] The present application provides a method, device and medium for evaluating the RAM performance of a rail transit signal product, which can quickly and accurately evaluate the RAM performance of a rail transit signal product in a rail transit signal system.

[0006] In a first aspect, the present application provides a method for evaluating the RAM performance of a rail transit signal product, the method comprising: Obtain rail transit signal products in rail transit signal systems; Perform reliability analysis on the rail transit signal product through failure mode and effect analysis and criticality analysis FMECA to obtain reliability analysis results; the reliability analysis results represent the reliability level of the rail transit signal product; Performing reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information; the reliability prediction information represents the probability of failure of the rail transit signal product within a unit time; Analyze and process the fault information of the rail transit signal product, and output the on-site operation data of the rail transit signal product; Using Bayesian theorem to perform compatibility test on prior information and a posteriori information to obtain a compatibility test result; the prior information includes the reliability prediction information, and the a posteriori information includes the field operation data; When the compatibility test result indicates that the prior information and the posterior information are compatible, the prior information and the posterior information are fused using the Bayesian theorem to obtain a fused posterior distribution; Converting the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product; The failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product, and the reliability, availability and maintainability RAM performance evaluation results of the rail transit signal product are output.

[0007] Optionally, performing reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information includes: Obtaining component information of components in the rail transit signal product; Select a component failure rate analysis method corresponding to the rail transit signal product; Calling the component failure rate analysis method, performing component failure rate analysis on the components in the rail transit signal product according to the reliability analysis result and the component information, and obtaining the failure rate of the components in the rail transit signal product; Obtaining an estimated failure rate of the rail transit signal product according to the failure rate of components in the rail transit signal product; The reliability prediction information including the predicted failure rate of the rail transit signal product is obtained.

[0008] Optionally, the component failure rate analysis method includes a component counting method or a component stress analysis method.

[0009] Optionally, performing reliability analysis on the rail transit signal product through FMECA to obtain reliability analysis results includes: Performing component failure mode analysis on components in the rail transit signal product through the FMECA to obtain failure modes of components in the rail transit signal product; Determining the severity level corresponding to the failure mode based on the degree of influence of the failure mode of the components in the rail transit signal product on the rail transit signal system; According to the severity level corresponding to the failure mode, a hazard analysis is performed on the components in the rail transit signal product to obtain a hazard analysis result; The reliability analysis results including failure modes of components in the rail transit signal product and the criticality analysis results are obtained.

[0010] Optionally, the analyzing and processing the fault information of the rail transit signal product and outputting the on-site operation data of the rail transit signal product includes: Collecting the fault information of the rail transit signal product; The fault information is analyzed and processed by using a fault reporting, analysis and corrective action system FRACAS, and the field operation data of the rail transit signal product is output; wherein the FRACAS predefines the fault type and fault frequency statistical principle that affect RAM performance.

[0011] Optionally, the field operation data includes field operation time and the number of associated faults, and the compatibility test of the prior information and the posterior information using the Bayesian theorem to obtain the compatibility test result includes: Constructing a prior distribution based on the prior information; According to the on-site operation time and the number of associated faults, the confidence interval of the Bayesian interval estimation is constructed using the Bayesian theorem; the confidence interval is the confidence interval of the prior distribution; Performing a compatibility check on the prior information and the a posteriori information according to a preset checking method; If the prior information is within the confidence interval, obtaining the compatibility test result indicates that the prior information and the posterior information are compatible; If the prior information is not within the confidence interval, obtaining the compatibility test result indicates that the prior information and the a posteriori information are incompatible.

[0012] Optionally, the preset test method includes a non-parametric test method and a parametric test method, the non-parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are unknown, and the parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are known.

[0013] Optionally, the fault type includes associated faults and unassociated faults, wherein the associated faults represent faults directly related to the rail transit signal product itself or the use process, and the unassociated faults represent faults not directly related to the structure, design, manufacturing or use process of the rail transit signal product itself.

[0014] Optionally, the method further comprises: When the compatibility check result indicates that the a priori information and the a posteriori information are incompatible, the step of returning to perform reliability prediction of the rail transit signal product according to the reliability analysis result is performed.

[0015] In a second aspect, the present application further provides a RAM performance evaluation device for a rail transit signal product, the device comprising: An acquisition unit, used for acquiring rail transit signal products in a rail transit signal system; A reliability analysis unit, used for performing reliability analysis on the rail transit signal product through FMECA to obtain a reliability analysis result; the reliability analysis result represents the reliability level of the rail transit signal product; A reliability prediction unit, used to perform reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information; the reliability prediction information represents the probability of failure of the rail transit signal product within a unit time; A field operation data acquisition unit, used for analyzing and processing the fault information of the rail transit signal product, and outputting the field operation data of the rail transit signal product; A compatibility checking unit, used for performing a compatibility check on the prior information and the a posteriori information using the Bayesian theorem to obtain a compatibility check result; the prior information includes the reliability prediction information, and the a posteriori information includes the field operation data; A fusion unit, used to fuse the prior information and the posterior information using the Bayesian theorem to obtain a fused posterior distribution when the compatibility test result represents compatibility; A conversion unit, used to convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product; The output unit is used to input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product and output the RAM performance evaluation result of the rail transit signal product.

[0016] Optionally, the reliability prediction unit is specifically used to: Obtaining component information of components in the rail transit signal product; Select a component failure rate analysis method corresponding to the rail transit signal product; Calling the component failure rate analysis method, performing component failure rate analysis on the components in the rail transit signal product according to the reliability analysis result and the component information, and obtaining the failure rate of the components in the rail transit signal product; Obtaining an estimated failure rate of the rail transit signal product according to the failure rate of components in the rail transit signal product; The reliability prediction information including the predicted failure rate of the rail transit signal product is obtained.

[0017] Optionally, the component failure rate analysis method includes a component counting method or a component stress analysis method.

[0018] Optionally, the reliability analysis unit is specifically used to: Performing component failure mode analysis on the components in the rail transit signal product through FMECA to obtain the failure mode of the components in the rail transit signal product; Determining the severity level corresponding to the failure mode based on the degree of influence of the failure mode of the components in the rail transit signal product on the rail transit signal system; According to the severity level corresponding to the failure mode, a hazard analysis is performed on the components in the rail transit signal product to obtain a hazard analysis result; The reliability analysis results including failure modes of components in the rail transit signal product and the criticality analysis results are obtained.

[0019] Optionally, the on-site operation data acquisition unit is specifically used to: Collecting the fault information of the rail transit signal product; The fault information is analyzed and processed by using FRACAS to output the field operation data of the rail transit signal product; wherein the FRACAS predefines the fault type and fault frequency statistical principle that affect RAM performance.

[0020] Optionally, the field operation data includes field operation time and the number of associated faults, and the compatibility verification unit is specifically used to: Constructing a prior distribution based on the prior information; According to the on-site operation time and the number of associated faults, the confidence interval of the Bayesian interval estimation is constructed using the Bayesian theorem; the confidence interval is the confidence interval of the prior distribution; Performing a compatibility check on the prior information and the a posteriori information according to a preset checking method; If the prior information is within the confidence interval, obtaining a compatibility test result indicates that the prior information and the posterior information are compatible; If the prior information is not within the confidence interval, obtaining the compatibility test result indicates that the prior information and the a posteriori information are incompatible.

[0021] Optionally, the preset test method includes a non-parametric test method and a parametric test method, the non-parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are unknown, and the parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are known.

[0022] Optionally, the fault type includes associated faults and unassociated faults, wherein the associated faults represent faults directly related to the rail transit signal product itself or the use process, and the unassociated faults represent faults not directly related to the structure, design, manufacturing or use process of the rail transit signal product itself.

[0023] Optionally, the compatibility verification unit is further used to: When the compatibility check result indicates that the a priori information and the a posteriori information are incompatible, the process returns to execute reliability prediction of the rail transit signal product according to the reliability analysis result.

[0024] In a third aspect, the present application further provides an electronic device, the electronic device comprising a processor and a memory: The memory is used to store computer programs; The processor is used to execute the method provided in the first aspect according to the computer program.

[0025] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method provided in the first aspect above.

[0026] It can be seen that this application has the following beneficial effects: The present application provides a method, device and medium for evaluating the RAM performance of a rail transit signal product. In the method, a rail transit signal product in a rail transit signal system is obtained; reliability analysis of the rail transit signal product is performed through FMECA to obtain reliability analysis results; the reliability analysis results represent the reliability level of the rail transit signal product; reliability prediction of the rail transit signal product is performed according to the reliability analysis results to obtain reliability prediction information; the reliability prediction information represents the probability of failure of the rail transit signal product within a unit time; the failure information of the rail transit signal product is analyzed and processed, and the on-site operation data of the rail transit signal product is output; the compatibility test of prior information and posterior information is performed using Bayesian theorem to obtain a compatibility test result; the prior information includes reliability prediction information, and the posterior information includes on-site operation data; when the compatibility test result represents that the prior information and the posterior information are compatible, the prior information and the posterior information are fused using Bayesian theorem to obtain a fused posterior distribution; the posterior information corresponding to the fused posterior distribution is converted to obtain the failure rate of the rail transit signal product; the failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product, and the RAM performance evaluation result of the rail transit signal product is output.

[0027] Thus, compared with the prior art, the embodiments of the present application fully exploit RAM performance data during the entire life cycle of rail transit signal products. On the one hand, reliability analysis of rail transit signal products is performed through FMECA to obtain reliability analysis results, and reliability prediction information is obtained for rail transit signal products based on the reliability analysis results. On the other hand, fault information of rail transit signal products is analyzed and processed, and on-site operation data of rail transit signal products is output. In addition, a data fusion verification method is provided for the mined RAM performance data. When the reliability prediction information is compatible with the on-site operation data, the on-site operation data of the operation stage and the reliability prediction information of the design and development stage are integrated based on the Bayesian theorem to realize the RAM performance evaluation of rail transit signal products, effectively solve the actual engineering problems caused by insufficient on-site operation data in the early stage of operation and maintenance, and achieve the purpose of quickly and accurately verifying the RAM performance of rail transit signal products. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0029] Figure 1 A flow chart of a RAM performance evaluation method for a rail transit signal product provided in an embodiment of the present application; Figure 2 A schematic diagram of obtaining a RAM performance evaluation result based on a reliable model provided in an embodiment of the present application; Figure 3 A flow chart of an embodiment of a method for evaluating RAM performance of a rail transit signal product provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a RAM performance evaluation device 1000 for a rail transit signal product provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of an electronic device 1100 provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The multiple involved in the embodiments of the present application means greater than or equal to two. It should be noted that in the description of the embodiments of the present application, the words "first", "second", etc. are only used for the purpose of distinguishing the description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying an order.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and specific implementation methods. It is understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. In addition, it should be noted that, for ease of description, only the parts related to the present application are shown in the accompanying drawings, not all structures.

[0032] The existing technical solution is to establish a Failure Report Analysis and Corrective Action System (FRACAS) for the rail transit signal system. FRACAS is used to collect on-site operation data during the operation and maintenance phase, record effective operation time, faults or defects, and classify faults or defects according to their severity and criticality.

[0033] However, existing technical solutions all rely on on-site operation data during the operation and maintenance phase to conduct real RAM performance evaluation. The on-site data during the operation and maintenance phase alone cannot respond to the RAM indicator verification requirements during the warranty period (usually one to two years), and it is difficult to prove that the rail transit signal system can be operated safely and reliably throughout its life cycle. In other words, existing technology cannot accurately evaluate the RAM performance of the rail transit signal system.

[0034] In the specific implementation process of the existing technology, the effective operating time and the number of failures are substituted into the reliability model, such as the Reliability Block Diagrams (RBD) model, and the RAM index is output to evaluate the RAM performance of the rail transit signal system.

[0035] The following explanation uses the reliability model as the RBD model. The entire evaluation process is based on the exponential distribution statistical analysis calculation process commonly used for electronic products. The calculation process is usually carried out in combination with the RBD model, and its essence is still based on the exponential distribution statistical analysis method commonly used for electronic products.

[0036] The exponential distribution reliability evaluation method is shown in the following formula (1): Formula (1) The point estimate of failure rate is calculated according to the following formula (2), the upper limit of failure rate is calculated according to the following formula (3), the point estimate of reliability is calculated according to the following formula (4), and the lower limit of reliability is calculated according to the following formula (5); Formula (2) Formula (3) Formula (4) Formula (5) In the above formula (1) to formula (5): R represents reliability; λ represents failure rate; t represents task time; represents the point estimate of failure rate; T represents the cumulative effective running time; r represents the number of failures. The cumulative number of failures is used to calculate the basic failure rate, that is, the failure number r uses the value of the cumulative number of failures; represents the upper limit of failure rate; express The quantile of the distribution can be obtained by looking up the statistical distribution value table x2 distribution (GB / T 4086.2); represents the reliability point estimate; Represents the lower limit of reliability.

[0037] In the absence of failure, the reliability point estimate is calculated according to the following formula (6), and the reliability lower limit is calculated according to the following formula (7): Formula (6) Formula (7) The existing technical solution completely relies on the on-site data in the operation and maintenance phase to carry out the real performance evaluation of RAM. The applicant has found that the reasons for the above problems in the existing technical solution are mainly the following two points: ① The operation time in the early operation and maintenance stage is short; In the early operation and maintenance stage of rail transit signal systems (taking subway project signal systems as an example), the effective operation time that can be counted is relatively short due to insufficient equipment quantity and short cumulative working time. Once a failure occurs, the product performance cannot be truly reflected if the RAM feature quantity has insufficient effective operation time.

[0038] ② The operation data of different engineering projects cannot be trusted; The RAM verification input data, evaluation process and conclusions during the operation and maintenance phase of the subway project signal system must be reviewed by the owner and the project evaluation agency. Therefore, the input data for verification can only use the data of this project to ensure that the data source is controlled and the data is credible. Even if other projects use the same or similar products, the on-site operation data cannot be mutually trusted.

[0039] Due to the above two reasons, it is impossible to respond to the RAM indicator verification requirements during the warranty period (usually one to two years) based solely on on-site data from the operation and maintenance phase, and it is difficult to prove that the rail transit signal system can be operated safely and reliably throughout its entire life cycle.

[0040] Based on this, the embodiment of the present application provides a RAM performance evaluation method, device and medium for a rail transit signal product. In the method, a rail transit signal product in a rail transit signal system is obtained; a reliability analysis is performed on the rail transit signal product through FMECA to obtain a reliability analysis result; the reliability analysis result represents the reliability level of the rail transit signal product; the reliability of the rail transit signal product is predicted according to the reliability analysis result to obtain reliability prediction information; the reliability prediction information represents the probability of failure of the rail transit signal product within a unit time; the fault information of the rail transit signal product is analyzed and processed, and the on-site operation data of the rail transit signal product is output; the compatibility test of the prior information and the posterior information is performed using the Bayesian theorem to obtain a compatibility test result; the prior information includes the reliability prediction information, and the posterior information includes the on-site operation data; when the compatibility test result represents that the prior information and the posterior information are compatible, the prior information and the posterior information are fused using the Bayesian theorem to obtain a fused posterior distribution; the posterior information corresponding to the fused posterior distribution is converted to obtain the failure rate of the rail transit signal product; the failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product, and the RAM performance evaluation result of the rail transit signal product is output.

[0041] Thus, compared with the prior art, the embodiments of the present application fully exploit RAM performance data during the entire life cycle of rail transit signal products. On the one hand, reliability analysis of rail transit signal products is performed through FMECA to obtain reliability analysis results, and reliability prediction information is obtained for rail transit signal products based on the reliability analysis results. On the other hand, fault information of rail transit signal products is analyzed and processed, and on-site operation data of rail transit signal products is output. In addition, a data fusion verification method is provided for the mined RAM performance data. When the reliability prediction information is compatible with the on-site operation data, the on-site operation data of the operation stage and the reliability prediction information of the design and development stage are integrated based on the Bayesian theorem, effectively solving the actual engineering problems caused by insufficient on-site operation data in the early stage of operation and maintenance, and achieving the purpose of quickly and accurately verifying the RAM performance of rail transit signal products, so as to realize the RAM performance evaluation of rail transit signal products.

[0042] To facilitate understanding of the specific implementation of the RAM performance evaluation method for rail transit signal products provided in the embodiment of the present application, it will be described below with reference to the accompanying drawings.

[0043] It should be noted that the subject implementing the RAM performance evaluation method of the rail transit signal product may be the RAM performance evaluation device of the rail transit signal product provided in the embodiment of the present application, and the RAM performance evaluation device of the rail transit signal product may be carried in an electronic device or a functional module of an electronic device. The electronic device of the embodiment of the present application may be any device capable of implementing the RAM performance evaluation method of the rail transit signal product in the embodiment of the present application, for example, an Internet of Things (IoT) device or a server.

[0044] See also Figure 1 , Figure 1 A flow chart of a method for evaluating the RAM performance of a rail transit signal product provided in an embodiment of the present application. The method can be applied to a RAM performance evaluation device for a rail transit signal product. The RAM performance evaluation device for a rail transit signal product can be as follows: Figure 4 The RAM performance evaluation device 1000 of the rail transit signal product shown in FIG. Figure 5 Functional modules in the electronic device 1100 are shown.

[0045] like Figure 1 As shown, the method may include, for example: S1: Obtain rail transit signal products in the rail transit signal system.

[0046] In the embodiment of the present application, the rail transit signal system may be, for example, a rail transit subway project signal system, and the rail transit signal product may be, for example, a rail transit subway signal product.

[0047] S2: Conduct reliability analysis on rail transit signal products through FMECA and obtain reliability analysis results.

[0048] It should be noted that the reliability analysis results represent the reliability level of rail transit signal products.

[0049] Among them, Failure Mode, Effects and Criticality Analysis (FMECA) is an analysis method used for reliability design, mainly used to identify and evaluate potential failure modes in products or systems and their impact on the system, so as to take corresponding measures to improve the reliability and safety of the system. FMECA analyzes each possible failure mode, determines its impact on product operation, finds single point failures, and determines its criticality based on the severity of the failure mode and its probability of occurrence.

[0050] S3: Carry out reliability prediction of rail transit signal products based on reliability analysis results to obtain reliability prediction information.

[0051] It should be noted that the reliability prediction information represents the probability of failure of rail transit signal products in a unit time, and the reliability prediction information is obtained during the design and development stage of the rail transit signal product's full life cycle. In the specific implementation process, the reliability prediction information can be the expected failure rate of the rail transit signal product.

[0052] S4: Analyze and process the fault information of rail transit signal products, and output the on-site operation data of rail transit signal products.

[0053] It should be noted that the on-site operation data is obtained during the operation stage of the entire life cycle of the rail transit signal product. The on-site operation data may include on-site operation time and the number of associated faults. The on-site operation time may indicate the time that the rail transit product has been running on-site.

[0054] It should be noted that, in the embodiment of the present application, S3 and S4 can be executed successively or simultaneously, that is, S3 can be executed first and then S4, or S4 can be executed first and then S3, or S3 and S4 can be executed simultaneously.

[0055] S5: Use Bayesian theorem to perform consistency test on prior information and posterior information to obtain consistency test results.

[0056] It should be noted that the a priori information includes reliability prediction information, and the a posteriori information includes field operation data, that is, the a posteriori information includes field operation time and the number of associated failures.

[0057] It should be noted that the premise of applying prior information is that the prior information should be able to reflect the statistical characteristics of the reliability parameters. Therefore, while strengthening the collection and organization of prior information, it is also necessary to ensure the credibility of the prior information.

[0058] The credibility of the prior information is compared with the on-site information. The credibility of the prior information in the form of reliability prediction information is usually guaranteed by checking the compatibility of the prior information and the on-site information.

[0059] S6: When the compatibility test result indicates that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the prior information and the posterior information to obtain the fused posterior distribution.

[0060] It should be noted that when the compatibility test results indicate that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the field operation data and the reliability prediction information to obtain the fused posterior distribution.

[0061] In a possible implementation, when the compatibility check result indicates that the a priori information and the a posteriori information are incompatible, the process returns to perform reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information, i.e., returns to execute step S3.

[0062] S7: Convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product.

[0063] It should be noted that because the failure rate is mainly used as the input data of each unit of the reliability model, the posterior information corresponding to the fused posterior distribution is converted into the failure rate.

[0064] S8: Input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product, and output the RAM performance evaluation result of the rail transit signal product.

[0065] It should be noted that the reliability model has been constructed during the design and development stage and has passed the product safety certification and can be used directly. The failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product to output the RAM performance evaluation results of the rail transit signal product.

[0066] In this way, the embodiments of the present application make full use of the RAM information of various information sources in the entire life cycle of rail transit signal products to perform compatibility checks and correlation fusion to obtain more complete and accurate input data on the RAM performance of rail transit signal products, thereby achieving the purpose of improving the accuracy and effectiveness of RAM verification in the operation and maintenance stage, and realizing rapid and accurate evaluation of the RAM performance of rail transit signal products in the rail transit signal system.

[0067] Embodiment 1 Based on the above embodiments, the embodiment of the present application specifically describes the specific execution process of performing reliability analysis on rail transit signal products through FMECA and obtaining reliability analysis results.

[0068] S1: Obtain rail transit signal products in the rail transit signal system.

[0069] S2: Conduct reliability analysis on rail transit signal products through FMECA and obtain reliability analysis results.

[0070] In the specific implementation process, FMECA can include Failure Modes and Effects Analysis (FMEA) and Criticality Analysis (CA). FMEA is used to analyze each potential failure mode in the product and the possible impact on the product, and classify each potential failure mode according to its severity. CA is used to classify each potential failure mode according to its severity and the comprehensive impact caused by the probability of its occurrence.

[0071] It should be noted that the embodiments of the present application may be to perform reliability analysis on circuits, boards, and components in rail transit signal products to ultimately obtain reliability analysis results of rail transit signal products, wherein the circuit includes boards, and the boards include components.

[0072] In a possible implementation, S2 provided in the embodiment of the present application may include: S21, analyze the failure modes of components in rail transit signal products through FMECA to obtain the failure modes of components in rail transit signal products.

[0073] Among them, component failure mode analysis refers to the process of segmenting the failure phenomena and failure causes that may occur during the use of components. Through the component failure mode, the potential failure mode of the component can be identified.

[0074] S22: Based on the impact of the failure mode of components in rail transit signal products on the rail transit signal system, determine the severity level corresponding to the failure mode.

[0075] Among them, the severity level is determined according to the ultimate impact of the failure mode on the system or equipment. The severity level reflects the severity of the consequences of failure and is usually divided into multiple levels, such as catastrophic failure, fatal failure, serious failure and minor failure. Determining the severity level helps to assess the degree of harm of the failure mode to the system or equipment.

[0076] S23, based on the severity level corresponding to the failure mode, conduct a hazard analysis on the components in the rail transit signal products to obtain the hazard analysis results.

[0077] S24, obtaining reliability analysis results including failure modes and criticality analysis results of components in rail transit signal products.

[0078] The following describes the embodiments S21 to S24 of the present application in conjunction with the specific implementation process.

[0079] 1) The specific implementation process of component failure mode analysis may include: The analysis is carried out based on the failure modes of components defined in the Handbook of Reliability Prediction of Electronic Equipment (such as GJB / Z 299C). At the same time, the analysis compliance of the failure modes defined in the industry standards and specifications for rail transit signal products (such as Appendix C of EN50129-2018 standard) must also be considered to reasonably determine the failure modes of components.

[0080] Among them, the Electronic Equipment Reliability Prediction Handbook provides a complete set of reliability prediction procedures to help designers evaluate the reliability of electronic equipment during the design and development phase. This standard provides methods for reliability prediction of systems, subsystems and equipment, including analysis of failure modes, failure rates and failure impacts. In the specific implementation process, the standard can be built into the software and the component failure mode can be selected.

[0081] Among them, the rail transit signal product industry standard specification is an important industry specification, which is mainly aimed at the safety of railway signal systems. This standard is an important reference for the rail transit industry to design, develop, verify and maintain systems, and aims to ensure the reliability and safety of rail transit communication and signal systems in operation.

[0082] It should be noted that the analysis convention for failure modes of components in rail transit signal products can be as follows: ① The resistance tolerance of resistors is generally 5%, and the capacitance tolerance of capacitors is generally 10%. Changes within the tolerance range specified in the component specification are not considered to be faults; ② If the resistance value changes within 10%, it will be analyzed as a resistance increase or decrease fault; if the resistance value changes more than 10%, it will be analyzed as a short circuit or open circuit fault; ③ If the capacitance value changes within 20%, it is analyzed as a capacitance increase or decrease fault; if the capacitance value changes more than 20%, it is analyzed as a short circuit or open circuit fault.

[0083] 2) Determination of severity level The severity level is determined according to the final impact of the failure mode on the rail transit signal system. Except for the availability of the rail transit signal system, safety-related functions are uniformly defaulted to a major failure in the system.

[0084] Severity level determination refers to the severity of the impact of the failure mode on the product, which is usually divided into four categories: I, II, III, and IV. Among them, I represents catastrophic failures, which will cause death or system damage; II represents fatal failures, which will cause serious injuries to personnel, serious damage to equipment or systems, and thus cause mission failure; III represents serious failures, which will cause minor injuries to personnel, minor damage to equipment and systems, resulting in delayed execution of the mission, or mission degradation, or system failure; IV represents minor failures, which are not serious enough to cause injuries to personnel, damage to equipment or systems, but require unplanned maintenance or repair.

[0085] The severity level is very important in the product design and development process, and is mainly used to evaluate the impact of potential failure modes. By analyzing the failure modes of different severity levels, measures can be taken in advance to make improvements and avoid adverse effects in the product. The core of FMEA is pre-emptive action rather than post-remedy. Introducing FMEA at different stages of the product development cycle (such as planning and determination, design and development, process design, small batch production, and mass production) is essential for producing high-quality products, recording and continuously optimizing processes.

[0086] S3: Carry out reliability prediction of rail transit signal products based on reliability analysis results to obtain reliability prediction information.

[0087] It should be noted that the reliability prediction information represents the probability of failure of rail transit signal products per unit time.

[0088] S4: Analyze and process the fault information of rail transit signal products, and output the on-site operation data of rail transit signal products.

[0089] It should be noted that the on-site operation data is obtained during the operation stage of the entire life cycle of the rail transit signal product. The on-site operation data may include on-site operation time and the number of associated faults. The on-site operation time may indicate the time that the rail transit product has been running on-site.

[0090] It should be noted that, in the embodiment of the present application, S3 and S4 can be executed successively or simultaneously, that is, S3 can be executed first and then S4, or S4 can be executed first and then S3, or S3 and S4 can be executed simultaneously.

[0091] S5: Use Bayesian theorem to perform consistency test on prior information and posterior information to obtain consistency test results.

[0092] It should be noted that the a priori information includes reliability prediction information, and the a posteriori information includes field operation data, that is, the a posteriori information includes field operation time and the number of associated failures.

[0093] S6: When the compatibility test result indicates that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the prior information and the posterior information to obtain the fused posterior distribution.

[0094] It should be noted that when the compatibility test results indicate that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the field operation data and the reliability prediction information to obtain the fused posterior distribution.

[0095] In the specific implementation process, the smallest unit of field operation data recording in the embodiments of the present application is generally a board card, and the target objects of the above reliability prediction, prior information, field operation data, a posteriori information and other execution processes are all based on the board card as the smallest unit. The compatibility test in the above embodiments is also based on the board card as the object, and is tested one by one.

[0096] S7: Convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product.

[0097] It should be noted that because the failure rate is mainly used as the input data of each unit of the reliability model, the posterior information corresponding to the fused posterior distribution is converted into the failure rate.

[0098] S8: Input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product, and output the RAM performance evaluation result of the rail transit signal product.

[0099] It should be noted that the reliability model has been constructed during the design and development stage and has passed the product safety certification and can be used directly. The failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product to output the RAM performance evaluation results of the rail transit signal product.

[0100] In the embodiment of the present application, a reliability model of rail transit signal products is constructed to describe the logical relationship of reliability between the various levels of rail transit signal products; the prior information of the expected reliability information in the design and development stage is combined with the a posteriori information of the field operation data in the operation stage to verify and fuse; the a posteriori information corresponding to the fused a posteriori distribution is substituted into the reliability model to evaluate the actual RAM performance level of the rail transit signal product. The embodiment of the present application takes into account the characteristics of rail transit signal products and provides a new method for RAM verification in the operation and maintenance stage, which can greatly improve the effectiveness and accuracy of verification and quickly and accurately verify the RAM performance of rail transit signal products.

[0101] Embodiment 2 In the embodiments of the present application, a specific execution process of performing reliability prediction on rail transit signal products according to reliability analysis results and obtaining reliability prediction information is specifically described.

[0102] S1: Obtain rail transit signal products in the rail transit signal system.

[0103] S2: Conduct reliability analysis on rail transit signal products through FMECA and obtain reliability analysis results.

[0104] It should be noted that the embodiments of the present application may be to perform reliability analysis on circuits, boards, and components in rail transit signal products to ultimately obtain reliability analysis results of rail transit signal products, wherein the circuit includes boards, and the boards include components.

[0105] S3: Carry out reliability prediction of rail transit signal products based on reliability analysis results to obtain reliability prediction information.

[0106] It should be noted that the reliability prediction information represents the probability of failure of rail transit signal products per unit time.

[0107] In the embodiment of the present application, during the hardware design stage of the rail transit signal product, reliability prediction is carried out by combining the existing rail transit signal product information, component quantity, and component stress.

[0108] In a possible implementation, S3 provided in the embodiment of the present application may include: S31: Obtain component information of components in rail transit signal products.

[0109] S32: Select the component failure rate analysis method corresponding to the rail transit signal product.

[0110] It should be noted that the component failure analysis method may include a component counting method or a component stress analysis method.

[0111] S33: calling a component failure rate analysis method, performing component failure rate analysis on components in the rail transit signal product according to the reliability analysis results and component information, and obtaining the failure rate of components in the rail transit signal product.

[0112] S34: Obtain an estimated failure rate of the rail transit signal product based on the failure rate of components in the rail transit signal product.

[0113] S35: Obtain reliability prediction information including predicted failure rate of rail transit signal products.

[0114] In the specific implementation process, we first need to select the component failure rate analysis method corresponding to the rail transit signal product. The Electronic Equipment Reliability Prediction Handbook (such as MIL-HDBK-217) provides two electronic equipment reliability prediction methods: component counting method and component stress analysis method. The above reliability prediction methods are all based on the basic failure rate modeling, and then use the estimation factor correction.

[0115] It should be noted that the component counting method requires very little information, usually only the type and quantity of components, the quality grade of components and the working environment. This method is suitable for reliability prediction in the preliminary design stage of electronic products. At this time, the type and quantity of components have been roughly determined, but the specific working stress and working environment have not yet been clarified.

[0116] It should be noted that stress analysis is used to estimate the unit failure rate in the detailed design stage of electronic products. When estimating the failure rate of components in a unit (board), the basic failure rate is corrected by factors such as the quality grade of the components, stress level, and environmental conditions.

[0117] That is, if the rail transit signal system is in the early stage of design, the component counting method is selected as the component failure rate analysis method to calculate the failure rate; if the rail transit signal system is in the late stage of design, the component stress analysis method is selected as the component failure rate analysis method.

[0118] ① Component counting method The component counting method simply calculates the failure rate of the components in the device. The reliability model assumed by the component counting method is that the failure rate of the device is the sum of the failure rates of various groups of components. It can also be regarded as the reliability model of the device is obtained by connecting the component reliability models in series.

[0119] The equipment failure rate can be expressed by the following formula (8): Formula (8) In the above formula (8), It represents the total failure rate of the equipment; represents the failure rate of the ith component.

[0120] Among them, the failure rate of the i-th component can be expressed as the basic failure rate Indicates that, through coefficients (such as quality coefficient ) to correct it. Therefore, the reliability of the equipment can be calculated by the following formula (9): Formula (9) In general, if the i-th component has If the conditions are the same, then the above formula (9) can be expressed by formula (10): Formula (10) in, represents the usage of the i-th component; n represents the number of different general component types.

[0121] In the embodiments of the present application, the device may be represented as a rail transit signal product, and the component may be represented as a component in the rail transit signal product.

[0122] Regarding the above basic failure rate , you can refer to the general failure rate of existing general electronic components If the components are imported, you can refer to the Electronic Equipment Reliability Prediction Handbook MIL-STD-217F. If the components are domestic, you can refer to the Electronic Equipment Reliability Prediction Handbook GJB / Z 299C.

[0123] In the specific implementation process, the working environment is first classified. For example, the environment with good ground fixation is coded as G. B , the general ground fixed environment code is G F1 …. And the general operating environment temperature of each component is specified, for example, G B At 35°C, G F1 Components have different component quality grades, such as A, A1, A2, A3, A4, B, B 1、 B2, C, C1, C2. Different component quality grades correspond to different quality coefficients For example, the composite resistor quality grade is A2 , the quality grade of the composite resistor is B1 …. Thus, according to the number of n common components in a device, the number of the i-th component , find out the failure rate of the corresponding environment code , find out the corresponding quality coefficient , the reliability prediction information of the equipment can be estimated through the component counting method.

[0124] In order to understand the specific implementation process of obtaining the estimated reliability information based on the component counting method, the following is an example of a power module. Environment, the power module uses domestic electronic components, the component name, quality grade, quantity N and quality coefficient of each domestic electronic component Please refer to the following Table 1: Table 1

[0125] In the above Table 1, the power module includes components: sealed CMOS digital circuit (10 gates), power transformer, semiconductor regulator, metal film resistor, aluminum electrolytic capacitor, mica capacitor, circular connector type I, no wrap soldering iron solder joint and double-sided printed board, 40 metal holes; the failure rate corresponding to each component is obtained , the failure rate of each component in the power module is superimposed, and finally the failure rate of the power module is obtained as shown in the following formula (11): Formula (11) That is, in the embodiment of the present application, S34 may include: S3411, obtaining the working environment of the rail transit signal product and the types, number, quality level and basic failure rate of components in the rail transit signal product. S3412, calculating and obtaining the expected failure rate of the rail transit signal product based on the working environment, types, number, quality level and basic failure rate of components.

[0126] ②Component stress analysis method Component stress analysis is used to estimate the similarity calculation of the failure rate of a single component. It should be noted that component stress analysis is applicable to the later stages of circuit design and actual hardware design of a system or device, when the system or device has a component list and component stress analysis has been performed.

[0127] The component stress analysis method expression is shown in the following formula (12): Formula (12) in, Indicates the failure rate of the component; Indicates the basic failure rate of the component; represents the quality coefficient; It indicates the environmental stress coefficient. It should be noted that the environmental coefficient is different from the environmental stress coefficient, and different environments have different coefficients.

[0128] In order to understand the specific implementation process of obtaining the estimated reliability information according to the component stress analysis method, the following is an explanation with a specific example. For example, given a component with a basic failure rate of 0.0323, a quality factor of 0.97, and an environmental stress factor of 0.93.

[0129] According to the above formula (12), the failure rate of the component is calculated. The calculation process and the calculation result of the failure rate of the component are shown in formula (13) and formula (14) respectively: Formula (13) Formula (14) It should be noted that the complexity of the stress analysis formulas corresponding to different components is also different. The above component stress analysis formula (12) can be appropriately adjusted according to the additional working environment or special composition. Taking discrete semiconductor components as an example, its working failure rate can be shown as the following formula (15): Formula (15) in, Indicates the basic failure rate of the component; represents the quality coefficient; represents the environmental stress coefficient; represents the application coefficient, Indicates the rated power or rated current factor, represents the structural coefficient, Indicates the rated power factor, Varies with the variety, such as GaAs field effect tube .

[0130] Formula (16) The failure rate of discrete semiconductor components can be calculated as shown in the above formula (16), where A represents the adjustment coefficient, and p is the shape parameter, Indicates the maximum allowable junction temperature, Indicates the maximum allowable temperature when full and The difference between the working electrical stress and the rated electrical stress is S = working electrical stress / rated electrical stress.

[0131] That is, in the embodiment of the present application, the S34 may include: S3421, obtaining a list of components in the rail transit signal product, the list of components including at least one component; S3422, obtaining the basic failure rate, quality coefficient and environmental stress coefficient of each component in the at least one component; S3423, calculating the failure rate of each component according to the basic failure rate, quality coefficient and environmental stress coefficient of each component; S3424, obtaining the expected failure rate of the rail transit signal product according to the failure rate of each component.

[0132] S4: Analyze and process the fault information of rail transit signal products, and output the on-site operation data of rail transit signal products.

[0133] It should be noted that the on-site operation data may include on-site operation time and the number of associated faults. The on-site operation time may indicate the time that the rail transit product has been running at the current moment.

[0134] S5: Use Bayesian theorem to perform consistency test on prior information and posterior information to obtain consistency test results.

[0135] It should be noted that the a priori information includes reliability prediction information, and the a posteriori information includes field operation data, that is, the a posteriori information includes field operation time and the number of associated failures.

[0136] S6: When the compatibility test result indicates that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the prior information and the posterior information to obtain the fused posterior distribution.

[0137] It should be noted that when the compatibility test results indicate that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the field operation data and the reliability prediction information to obtain the fused posterior distribution.

[0138] In the specific implementation process, the smallest unit of field operation data recording in the embodiments of the present application is generally a board card, and the target objects of the above reliability prediction, prior information, field operation data, a posteriori information and other execution processes are all based on the board card as the smallest unit. The compatibility test in the above embodiments is also based on the board card as the object, and is tested one by one.

[0139] S7: Convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product.

[0140] It should be noted that because the failure rate is mainly used as the input data of each unit of the reliability model, the posterior information corresponding to the fused posterior distribution is converted into the failure rate.

[0141] S8: Input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product, and output the RAM performance evaluation result of the rail transit signal product.

[0142] It should be noted that the reliability model has been constructed during the design and development stage and has passed the product safety certification and can be used directly. The failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product to output the RAM performance evaluation results of the rail transit signal product.

[0143] It should be noted that the embodiments of the present application fully consider the design characteristics of rail transit signal products and the actual engineering projects, and have strong engineering operability, so that under certain engineering constraints, such as a small number of equipment, a short on-site operation time and the limitation of early indicator compliance certification, the RAM performance verification of rail transit signal products can be carried out. It has great economic benefits. The verification work on the RAM performance of rail transit signal products during the operation and maintenance stage of the warranty period in the embodiments of the present application has important reference value, and because the embodiments of the present application refer to the Electronic Equipment Reliability Prediction Manual, the solutions provided in the embodiments of the present application are not only limited to the rail transit field and rail transit signal products, but also have strong versatility in the electronics field, so it can be promoted and applied to reliability verification tests of electronic or electromechanical products in other fields.

[0144] Embodiment 3 In the embodiment of the present application, the specific execution process of analyzing and processing the fault information of the rail transit signal product and outputting the on-site operation data of the rail transit signal product is specifically described.

[0145] S1: Obtain rail transit signal products in the rail transit signal system.

[0146] S2: Conduct reliability analysis on rail transit signal products through FMECA and obtain reliability analysis results.

[0147] It should be noted that the embodiments of the present application may be to perform reliability analysis on circuits, boards, and components in rail transit signal products to ultimately obtain reliability analysis results of rail transit signal products, wherein the circuit includes boards, and the boards include components.

[0148] S3: Carry out reliability prediction of rail transit signal products based on reliability analysis results to obtain reliability prediction information.

[0149] It should be noted that the reliability prediction information represents the probability of failure of rail transit signal products per unit time.

[0150] S4: Analyze and process the fault information of rail transit signal products, and output the on-site operation data of rail transit signal products.

[0151] It should be noted that the on-site operation data may include on-site operation time and the number of associated faults. The on-site operation time may indicate the time that the rail transit product has been running at the current moment.

[0152] In one possible implementation, FRACAS may be used to perform closed-loop management of field operation data.

[0153] It should be noted that the Failure Reporting, Analysis and Corrective Action System (FRACAS) is a fault recording, analysis and reporting system that reports, analyzes and corrects equipment or system faults to improve the reliability and stability of equipment or systems. The input information of FRACAS can be fault reports, and the output information of FRACAS can be corrective measures, so FRACAS can also be regarded as an information system. Through a set of standardized and strict management procedures, FRACAS ensures that the scattered fault information of products and their components during various tests and use can be collected in a timely, accurate and complete manner, providing a scientific basis for analyzing, evaluating and improving product reliability.

[0154] Then, in the embodiment of the present application, S4 may include: S41: Collect fault information of rail transit signal products.

[0155] S42: Use FRACAS to analyze and process fault information and output on-site operation data of rail transit signal products.

[0156] It should be noted that the FRACAS provided in the embodiment of the present application predefines the failure types and failure frequency statistics principles that affect RAM performance.

[0157] Furthermore, precisely because the FRACAS of the embodiment of the present application clearly defines the fault classification and statistical principles that affect RAM performance, FRACAS can provide more accurate on-site operation data.

[0158] It should be noted that, according to the cause of the fault, the fault type is generally divided into the following two types: associated fault and unassociated fault, that is, the fault type includes associated fault and unassociated fault.

[0159] Associated failures are failures that are directly related to the structure, design, manufacturing or use of the product itself, while unassociated failures are failures that are not directly related to the structure, design, manufacturing or use of the product itself.

[0160] Among them, related failures may include failures caused by hardware design defects or manufacturing process defects, failures caused by defects in parts and components, and failures with unknown causes. Non-related failures may include failures caused by improper installation or maintenance, failures caused by accidents or misoperation, subordinate failures caused by other equipment, intermittent failures of the same component that occur for the second time or successively, and failures for which corresponding effective corrective measures have been taken after failure analysis and sufficient evidence proves that the corrective measures are completely effective.

[0161] It should be noted that the number of associated failures is counted according to the statistical principles of the number of failures, where the statistical principles of the number of failures may include: the intermittent occurrence of the same failure is counted as only one failure; in the case of simultaneous failure of multiple components or units, when it cannot be proved that one failure caused other failures, each failure is counted as an independent failure; a reported failure that reoccurs due to failure to be truly repaired should be counted as one failure together with the originally reported failure; dependent failures caused by independent failures are not counted in the number of product failures; minor defects in components, which do not lose the specified functions and can be repaired on-site (without causing disassembly) through pre-work inspection and post-work inspection in accordance with maintenance procedures, are not counted in the number of failures; failures that have been confirmed as non-associated failures are not counted in the number of failures.

[0162] Finally, the on-site operation time and related fault counts of rail transit signal products in the rail transit signal system are output.

[0163] S5: Use Bayesian theorem to perform consistency test on prior information and posterior information to obtain consistency test results.

[0164] It should be noted that the a priori information includes reliability prediction information, and the a posteriori information includes field operation data, that is, the a posteriori information includes field operation time and the number of associated failures.

[0165] S6: When the compatibility test result indicates that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the prior information and the posterior information to obtain the fused posterior distribution.

[0166] It should be noted that when the compatibility test results indicate that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the field operation data and the reliability prediction information to obtain the fused posterior distribution.

[0167] In the specific implementation process, the smallest unit of field operation data recording in the embodiments of the present application is generally a board card, and the target objects of the above reliability prediction, prior information, field operation data, a posteriori information and other execution processes are all based on the board card as the smallest unit. The compatibility test in the above embodiments is also based on the board card as the object, and is tested one by one.

[0168] S7: Convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product.

[0169] It should be noted that because the failure rate is mainly used as the input data of each unit of the reliability model, the posterior information corresponding to the fused posterior distribution is converted into the failure rate.

[0170] S8: Input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product, and output the RAM performance evaluation result of the rail transit signal product.

[0171] It should be noted that the reliability model has been constructed during the design and development stage and has passed the product safety certification and can be used directly. The failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product to output the RAM performance evaluation results of the rail transit signal product.

[0172] Compared with the prior art, the embodiment of the present application fully mines RAM performance data during the entire life cycle of rail transit signal products, adopts a data statistical analysis method based on Bayesian theorem, performs compatibility test on prior data and a posteriori information, and when compatible, integrates the on-site operation data of the operation phase and the reliability prediction data of the design and development phase, so as to quickly and effectively verify the actual RAM performance of rail transit signal products.

[0173] Embodiment 4 In the embodiment of the present application, the specific execution process of using Bayesian theorem to perform compatibility check on prior information and posterior information to obtain the compatibility check result is specifically described.

[0174] S1: Obtain rail transit signal products in the rail transit signal system.

[0175] S2: Conduct reliability analysis on rail transit signal products through FMECA and obtain reliability analysis results.

[0176] It should be noted that the embodiments of the present application may be to perform reliability analysis on circuits, boards, and components in rail transit signal products to ultimately obtain reliability analysis results of rail transit signal products, wherein the circuit includes boards, and the boards include components.

[0177] S3: Carry out reliability prediction of rail transit signal products based on reliability analysis results to obtain reliability prediction information.

[0178] It should be noted that the reliability prediction information represents the probability of failure of rail transit signal products per unit time.

[0179] S4: Analyze and process the fault information of rail transit signal products, and output the on-site operation data of rail transit signal products.

[0180] It should be noted that the on-site operation data may include on-site operation time and the number of associated faults. The on-site operation time may indicate the time that the rail transit product has been running at the current moment.

[0181] S5: Use Bayesian theorem to perform consistency test on prior information and posterior information to obtain consistency test results.

[0182] It should be noted that the a priori information includes reliability prediction information, and the a posteriori information includes field operation data, that is, the a posteriori information includes field operation time and the number of associated failures.

[0183] In a possible implementation, the field operation data may include field operation time and the number of associated faults. Then S5 in the embodiment of the present application may include: S51: Construct a prior distribution based on prior information.

[0184] S52: Based on the field operation time and the number of associated failures, the confidence interval of the Bayesian interval estimation is constructed using Bayes' theorem.

[0185] Here, the confidence interval is the confidence interval of the prior distribution.

[0186] S53: Perform a compatibility check on the prior information and the a posteriori information according to a preset check method.

[0187] S54: If the prior information is within the confidence interval, obtaining a compatibility test result indicates that the prior information and the posterior information are compatible.

[0188] S55: If the prior information is not within the confidence interval, obtaining a compatibility test result indicates that the prior information and the posterior information are incompatible.

[0189] It should be noted that if the compatibility test result indicates compatibility, it means that the compatibility hypothesis is accepted, and the a posteriori information can be corrected by the prior information. If the compatibility test result indicates that the test fails, the compatibility hypothesis is rejected.

[0190] It should be noted that the preset test method may include a non-parametric test method and a parametric test method. The non-parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are unknown, and the parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are known.

[0191] That is to say, in the embodiments of the present application, if the properties of the distribution function of the population are known, a parametric test method is used to perform a compatibility test; if the properties of the distribution function of the population are unknown, a non-parametric test method is used to perform a compatibility test.

[0192] In combination with the specific implementation process, steps S51 to S55 are described below using a statistical method based on the Bayesian theorem. The statistical method based on the Bayesian theorem mainly includes two steps: In the first step, the available information is used to construct a prior distribution for the parameters.

[0193] In the embodiment of the present application, firstly, a prior distribution is constructed by predicting the reliability of prior information.

[0194] The reliability prediction information of rail transit signal products is converted into a prior distribution. For details, please refer to the following formula (17) to determine the parameter values ​​in the distribution, that is, to determine the values ​​of α and β. Referring to the standards of aerospace and military industries, the prior distribution parameters of general electronic products are estimated at a confidence level of 0.6.

[0195] Formula (17) The second step is to perform a compatibility test on the prior information and the posterior information according to the preset test method to verify whether the prior information is within the confidence interval of the prior distribution.

[0196] In an embodiment of the present application, the field operation time and the number of associated faults of the rail transit signal product are used to construct a confidence interval for Bayesian interval estimation, and it is checked whether the prior information in the previous step is within the constructed confidence interval.

[0197] It should be noted that the compatibility test uses a statistic to test whether the two matrices are significantly different (or compatible). When the difference is not significant, the two matrices can be considered to be the same, so prior information can be used; otherwise, prior information cannot be used. When performing a compatibility test on reliability prediction information, if the form of the distribution function of the population is known, a parametric method can be used to perform a compatibility test on the population; if the form of the distribution function of the population is unknown, a non-parametric test method is suitable for performing a compatibility test.

[0198] ① The following is an explanation of the specific execution process of compatibility testing using the parameter testing method: Among them, the parameter testing method may include a confidence interval method and a hypothesis testing method. Of course, other parameter testing methods may also be used to perform overall compatibility testing. The embodiments of the present application do not limit the specific parameter testing methods.

[0199] In the specific implementation process, the confidence interval method is used to perform compatibility test.

[0200] The basic principle of the confidence interval method is as follows: Assume that the known population follows a normal distribution , distribution parameters and Unknown. Field operation data with existing prior distribution , the available sample mean To estimate , please refer to formula (18) for details, the sample variance is used to estimate , please refer to the following formula (19) for details.

[0201] Formula (18) Formula (19) Where n is the sample size, that is, the number of field operation data. S is the sample standard deviation (when using t distribution, the sample standard deviation is usually used to estimate the population standard deviation).

[0202] It should be noted that due to and Not dependent on , so we get the following formula (20).

[0203] Formula (20) So we get The confidence level is The confidence interval of is expressed as the following formula (21).

[0204] Formula (21) Where P{} represents probability; t2α(n−1) represents the upper 2α quantile of the t distribution with n−1 degrees of freedom; the significance level α represents the risk level of falsely rejecting the null hypothesis that you are willing to bear. For example, if α=0.05, there is a 5% risk of falsely believing that there is a significant difference between the sample mean and the population mean.

[0205] If the reliability prediction information falls within the above range, it is considered that the reliability prediction information and the field operation data have consistent parameter distribution, that is, the prior information can be used as input for RAM performance evaluation. Otherwise, the reliability prediction data cannot be used and it is necessary to return to step S3.

[0206] ② The specific implementation process of the compatibility test using the non-parametric test method is described below: In the embodiment of the present application, for the case where the sample quantity is small, the sample is, for example, subway signal product engineering project equipment.

[0207] For example, the non-parametric test method that can be used is the rank sum test method. It should be noted that the rank sum test method can be used for a variety of overall distributions with a small sample size or unclear distributions, and is therefore suitable for subway signal products.

[0208] In the specific implementation process, in order to verify the test data and prior samples Whether they belong to the same population, introduce the competitive hypothesis: : and belong to the same population; : and Not belonging to the same population; By mixing and sorting the two sub-samples of field operation data and reliability prediction information (from small to large), the order statistics can be obtained as shown in the following formula (22).

[0209] Formula (22) Among them, , that is, the kth element in the prior sample In the mixed sorting, it is ranked in the jth position, that is, The rank of is j, denoted by . Then the rank sum T of the prior samples is as shown in the following formula (23).

[0210] Formula (23) Then the following relationship can be established as shown in the following formula (24): Formula (24) in, represents the probability of rejecting the truth. under, and You can check the rank sum test table to get it; so, when you get the sub-sample and After that, the rank sum T is calculated, at the test level Under, if Adopt , that is, X and Y belong to the same population; if not If within the range, reject , that is, X and Y do not belong to the same population.

[0211] The third step is to use the reliability prediction information to correct the field operation data based on the above embodiment if the verification is passed, that is, the prior information corrects the posterior information, which is usually called Bayesian correction.

[0212] The following is a specific example to illustrate the first and second steps. Take a subway signal electronic product for cumulative on-site operation time T and the number of associated faults. , reliability predicted failure rate predicted value For example, the product follows an exponential distribution.

[0213] Convert the reliability failure rate prediction value into prior information and select , the equivalent time confidence level is 0.6 for compression. The specific process is shown in the following formula (25): Formula (25) So,( ) is the prior information.

[0214] Prior information Compatibility analysis is performed with the field operation data (T, r), and the statistical significance test interval of the bilateral interval estimation can be constructed based on the latter as shown in the following formula (26): Formula (26) In formula (27), α represents the significance level, and its value range is 0.01~0.1.

[0215] If the ratio of prior information In the interval shown in the above formula (27), the compatibility assumption is accepted and can be used for calculation with the posterior information. If the ratio of the prior information is If it is not within the range shown in the above formula (27), the compatibility assumption is rejected, the prior information is discarded, and only the a posteriori information (i.e., the field operation data) is used for calculation.

[0216] S6: When the compatibility test result indicates that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the prior information and the posterior information to obtain the fused posterior distribution.

[0217] It should be noted that when the compatibility test results indicate that the prior information and the posterior information are compatible, the Bayesian theorem is used to fuse the field operation data and the reliability prediction information to obtain the fused posterior distribution.

[0218] In the specific implementation process, the posterior distribution parameters are set to , the estimated values ​​of the distribution parameters obtained from multiple sources are Assuming parameters With an initial prior distribution , the posterior distribution expression is obtained by fusion through the Bayesian method as shown in the following formula (28): Formula (28) It should be noted that the key to the fusion method lies in the likelihood function The calculation of likelihood function is shown in the following formula (29): Formula (29) That is, to establish a general probability model for the posterior distribution parameters , The determination requires in-depth understanding of the specific problem and full consideration of engineering experience and objective information.

[0219] For electronic products, the probability model That is, the exponential distribution model. Then the field operation data that passes the compatibility test ( ) and reliability prediction information ( ), the post-test feature quantity is shown in the following formula (30) and formula (31): Formula (30) Formula (31) The posterior distribution in the embodiment of the present application is shown in the following formula (32): Formula (32) It should be noted that the posterior distribution generally obeys the negative log-gamma distribution, as shown in the following formula (33): Formula (33) If the product follows other distribution forms, such as beta distribution β(α, β) and gamma distribution Γ(α, β), the mean, variance and other parameters of the posterior distribution should be calculated accordingly as distribution characteristics. For details, please refer to Table 2 below.

[0220] Table 2

[0221] In the specific implementation process, the smallest unit of field operation data recording in the embodiments of the present application is generally a board card, and the target objects of the above reliability prediction, prior information, field operation data, a posteriori information and other execution processes are all based on the board card as the smallest unit. The compatibility test in the above embodiments is also based on the board card as the object, and is tested one by one.

[0222] S7: Convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product.

[0223] It should be noted that because the failure rate is mainly used as the input data of each unit of the reliability model, the posterior information corresponding to the fused posterior distribution is converted into the failure rate.

[0224] S8: Input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product, and output the RAM performance evaluation result of the rail transit signal product.

[0225] It should be noted that the reliability model has been constructed during the design and development stage and has passed the product safety certification and can be used directly. The failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product to output the RAM performance evaluation results of the rail transit signal product.

[0226] Taking the subway interlocking product as an example of rail transit signal products, the subway interlocking product is composed of boards and cards, which include a wiring board, a first safety platform motherboard, a first safety input and output board, a first safety isolation board, a first communication expansion board, a second safety platform motherboard, a second safety input and output board, a second safety isolation board, a second communication expansion board, a switching unit (Switching Module Unit‌, SMU), a safety platform motherboard, a first switch board, a second switch board, a first interface management unit (Interface Management Unit‌, IMU) and a second IMU. Among them, the wiring board is respectively connected to the first safety platform main board and the first safety input and output board; the first safety platform main board is connected to the first safety input and output board, the first safety input and output board is connected to the first safety isolation board, and the first safety isolation board is connected to the first communication expansion board; the second safety platform main board is connected to the second safety input and output board, the second safety input and output board is connected to the second safety isolation board, and the second safety isolation board is connected to the second communication expansion board; the first communication expansion board and the second communication expansion board are jointly connected to the SMU, the SMU is connected to the safety platform motherboard, and the safety platform motherboard is respectively connected to the first switch board and the second switch board; the first switch board and the second switch board are connected in parallel and then connected in series with the parallel first IMU and the second IMU.

[0227] Substitute the failure rate of each board converted from the a posteriori information into the reliability model to calculate the actual RAM performance data of the subway interlocking product, and compare it with the engineering RAM index of the subway interlocking product to obtain the comparison compliance conclusion, that is, the RAM performance evaluation result. For details, please refer to Figure 2 A schematic diagram of obtaining RAM performance evaluation results based on a reliable model is shown.

[0228] Compared with the prior art, the embodiment of the present application effectively mines RAM performance data with higher credibility and provides a data fusion verification method for the data, which can effectively solve practical engineering problems caused by insufficient on-site operation data in the early stage of operation and maintenance, and achieve the purpose of quickly verifying the RAM performance of subway products.

[0229] Embodiment 5 The RAM performance evaluation method of the rail transit signal product provided in the embodiment of the present application is described below with reference to a specific embodiment.

[0230] See also Figure 3, which is a flow chart of an embodiment of a RAM performance evaluation method for a rail transit signal product provided in an embodiment of the present application.

[0231] iData collection and application The basic failure rate data is obtained through reliability prediction and used as prior information.

[0232] Through FRACAS, field operation data is collected and processed to obtain the effective operation time and fault conditions of the equipment, which are used as a posteriori information to construct the significance test interval based on the field data.

[0233] ii. Perform a compatibility test on the prior information and the posterior information. If the test fails, regression reliability prediction or field data analysis is performed.

[0234] iii If the test passes, the prior information and the a posteriori information are fused, and the fused a posteriori information is input into the reliability model for RAM evaluation. If the verification passes, it indicates that the RAM performance requirements are met.

[0235] It should be noted that the applicant has found through research that the key problem of the existing technical solutions is that the RAM performance data available for statistical analysis is not fully exploited. In the product design and development stage, the hardware basic failure rate and the dangerous failure rate at the board level and channel level are formed by carrying out reliability prediction and FMECA at the component, board and system level, which are usually ignored in the RAM performance verification work at the operation and maintenance stage.

[0236] Because the above RAM data is usually obtained by design and development personnel based on the statistical analysis of domestic and international rail transit standards such as GJB / Z 299D, EN50129, GB / T 28809, and has been evaluated by an independent third-party safety certification agency. In other words, the basic failure rate and dangerous failure rate have high accuracy and credibility.

[0237] However, there is a compatibility problem in statistical analysis between the data obtained through reliability prediction, FMECA and other methods (i.e., prior information) and the field operation data. The two data actually come from different sample populations, and whether they approximately obey the same statistical distribution population needs to be identified through compatibility testing. If the difference between the prior information and the field data is not significant, then at a certain confidence level, the two can be regarded as the same sample population, so that the prior information can be used for further analysis, effectively improving the adequacy of the statistical analysis data and more realistically reflecting the RAM performance of the signal system products. On the contrary, if the difference is significant, it cannot be used.

[0238] Therefore, the embodiments of the present application provide a RAM statistical analysis method that has been widely recognized and applicable to rail transit signal products. It can integrate the a priori predicted data that has passed the compatibility test with the on-site operation data, solve the problem that it is difficult to verify its RAM performance indicators due to insufficient on-site operation data in the early stage of operation and maintenance, and quickly and effectively verify the actual RAM performance of the product.

[0239] The embodiment of the present application also provides a RAM performance evaluation device 1000 for a rail transit signal product, the device 1000 comprising: An acquisition unit 1001 is used to acquire a rail transit signal product in a rail transit signal system; The reliability analysis unit 1002 is used to perform reliability analysis on the rail transit signal product through FMECA to obtain a reliability analysis result; the reliability analysis result represents the reliability level of the rail transit signal product; The reliability prediction unit 1003 is used to perform reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information; the reliability prediction information represents the probability of failure of the rail transit signal product within a unit time; The on-site operation data acquisition unit 1004 is used to analyze and process the fault information of the rail transit signal product and output the on-site operation data of the rail transit signal product; A compatibility checking unit 1005 is used to perform a compatibility check on the prior information and the a posteriori information using the Bayesian theorem to obtain a compatibility check result; the prior information includes the reliability prediction information, and the a posteriori information includes the field operation data; A fusion unit 1006, configured to fuse the prior information and the posterior information using the Bayesian theorem to obtain a fused posterior distribution when the compatibility test result represents compatibility; A conversion unit 1007, configured to convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product; The output unit 1008 is used to input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product, and output the RAM performance evaluation result of the rail transit signal product.

[0240] Optionally, the reliability prediction unit 1003 is specifically configured to: Obtaining component information of components in the rail transit signal product; Select a component failure rate analysis method corresponding to the rail transit signal product; Calling the component failure rate analysis method, performing component failure rate analysis on the components in the rail transit signal product according to the reliability analysis result and the component information, and obtaining the failure rate of the components in the rail transit signal product; Obtaining an estimated failure rate of the rail transit signal product according to the failure rate of components in the rail transit signal product; The reliability prediction information including the predicted failure rate of the rail transit signal product is obtained.

[0241] Optionally, the component failure rate analysis method includes a component counting method or a component stress analysis method.

[0242] Optionally, the reliability analysis unit 1002 is specifically configured to: Performing component failure mode analysis on the components in the rail transit signal product through FMECA to obtain the failure mode of the components in the rail transit signal product; Determining the severity level corresponding to the failure mode based on the degree of influence of the failure mode of the components in the rail transit signal product on the rail transit signal system; According to the severity level corresponding to the failure mode, a hazard analysis is performed on the components in the rail transit signal product to obtain a hazard analysis result; The reliability analysis results including failure modes of components in the rail transit signal product and the criticality analysis results are obtained.

[0243] Optionally, the field operation data acquisition unit 1004 is specifically used to: Collecting the fault information of the rail transit signal product; The fault information is analyzed and processed by using FRACAS to output the field operation data of the rail transit signal product; wherein the FRACAS predefines the fault type and fault frequency statistical principle that affect RAM performance.

[0244] Optionally, the field operation data includes field operation time and the number of associated faults. The compatibility verification unit 1005 is specifically used to: Constructing a prior distribution based on the prior information; According to the on-site operation time and the number of associated faults, the confidence interval of the Bayesian interval estimation is constructed using the Bayesian theorem; the confidence interval is the confidence interval of the prior distribution; Performing a compatibility check on the prior information and the a posteriori information according to a preset checking method; If the prior information is within the confidence interval, obtaining a compatibility test result indicates that the prior information and the posterior information are compatible; If the prior information is not within the confidence interval, obtaining the compatibility test result indicates that the prior information and the a posteriori information are incompatible.

[0245] Optionally, the preset test method includes a non-parametric test method and a parametric test method, the non-parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are unknown, and the parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are known.

[0246] Optionally, the fault type includes associated faults and unassociated faults, wherein the associated faults represent faults directly related to the rail transit signal product itself or the use process, and the unassociated faults represent faults not directly related to the structure, design, manufacturing or use process of the rail transit signal product itself.

[0247] Optionally, the compatibility verification unit 1005 is further used to: When the compatibility check result indicates that the a priori information and the a posteriori information are incompatible, the process returns to execute reliability prediction of the rail transit signal product according to the reliability analysis result.

[0248] It should be noted that the specific implementation method of the device 1000 and the technical effects achieved can be found in Figure 1 or Figure 3 The relevant description in the method shown.

[0249] In addition, the present application embodiment also provides an electronic device 1100, such as Figure 5 As shown, the electronic device 1100 includes a processor 1101 and a memory 1102: The memory 1102 is used to store computer programs; The processor 1101 is used to execute according to the computer program Figure 1 or Figure 3 The method provided.

[0250] In addition, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method provided by the embodiment of the present application.

[0251] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, and the computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0252] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is only schematic, in which the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the goals of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0253] The above is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. It should be noted that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the present application, and these improvements and modifications should also be regarded as the protection scope of the present application.

Claims

1. A RAM performance evaluation method for rail transit signal products, characterized in that: The method comprises: Obtain rail transit signal products in rail transit signal systems; Perform reliability analysis on the rail transit signal product through failure mode and effect analysis and criticality analysis FMECA to obtain reliability analysis results; the reliability analysis results represent the reliability level of the rail transit signal product; Performing reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information; the reliability prediction information represents the probability of failure of the rail transit signal product within a unit time; Analyze and process the fault information of the rail transit signal product, and output the on-site operation data of the rail transit signal product; Using Bayesian theorem to perform compatibility test on prior information and a posteriori information to obtain compatibility test result; the prior information includes the reliability prediction information, and the a posteriori information includes the field operation data; wherein, using Bayesian theorem to perform compatibility test on prior information and a posteriori information to obtain compatibility test result includes: constructing a prior distribution according to the prior information; constructing a confidence interval of Bayesian interval estimation according to the field operation time and the number of associated failures using Bayesian theorem; the confidence interval is the confidence interval of the prior distribution; performing compatibility test on the prior information and the a posteriori information according to a preset test method; if the prior information is within the confidence interval, obtaining the compatibility test result indicates that the prior information and the a posteriori information are compatible; if the prior information is not within the confidence interval, obtaining the compatibility test result indicates that the prior information and the a posteriori information are incompatible; When the compatibility test result indicates that the prior information and the posterior information are compatible, the prior information and the posterior information are fused using the Bayesian theorem to obtain a fused posterior distribution; Converting the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product; The failure rate of the rail transit signal product is input into the reliability model of the rail transit signal product, and the reliability, availability and maintainability RAM performance evaluation results of the rail transit signal product are output.

2. The method according to claim 1, characterized in that The performing reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information includes: Obtaining component information of components in the rail transit signal product; Select a component failure rate analysis method corresponding to the rail transit signal product; Calling the component failure rate analysis method, performing component failure rate analysis on the components in the rail transit signal product according to the reliability analysis result and the component information, and obtaining the failure rate of the components in the rail transit signal product; Obtaining an estimated failure rate of the rail transit signal product according to the failure rate of components in the rail transit signal product; The reliability prediction information including the predicted failure rate of the rail transit signal product is obtained.

3. The method according to claim 2, characterized in that The component failure rate analysis method includes a component counting method or a component stress analysis method.

4. The method according to claim 1, characterized in that: The reliability analysis of the rail transit signal product is performed through the failure mode and effect analysis and criticality analysis FMECA to obtain the reliability analysis results, including: Performing component failure mode analysis on components in the rail transit signal product through the FMECA to obtain failure modes of components in the rail transit signal product; Determining the severity level corresponding to the failure mode based on the degree of influence of the failure mode of the components in the rail transit signal product on the rail transit signal system; According to the severity level corresponding to the failure mode, a hazard analysis is performed on the components in the rail transit signal product to obtain a hazard analysis result; The reliability analysis results including failure modes of components in the rail transit signal product and the criticality analysis results are obtained.

5. The method according to claim 1, characterized in that The analyzing and processing the fault information of the rail transit signal product and outputting the on-site operation data of the rail transit signal product includes: Collecting the fault information of the rail transit signal product; The fault information is analyzed and processed by using a fault reporting, analysis and corrective action system FRACAS, and the field operation data of the rail transit signal product is output; wherein the FRACAS predefines the fault types and fault frequency statistics principles that affect RAM performance.

6. The method according to claim 1, characterized in that The preset test method includes a non-parametric test method and a parametric test method. The non-parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are unknown, and the parametric test method is used to perform a compatibility test when the properties of the distribution function of the population are known.

7. A RAM performance evaluation device for a rail transit signal product, characterized in that: The device comprises: An acquisition unit, used for acquiring rail transit signal products in a rail transit signal system; A reliability analysis unit, used for performing reliability analysis on the rail transit signal product through FMECA to obtain a reliability analysis result; the reliability analysis result represents the reliability level of the rail transit signal product; A reliability prediction unit, used to perform reliability prediction on the rail transit signal product according to the reliability analysis result to obtain reliability prediction information; the reliability prediction information represents the probability of failure of the rail transit signal product within a unit time; A field operation data acquisition unit, used for analyzing and processing the fault information of the rail transit signal product, and outputting the field operation data of the rail transit signal product; A compatibility test unit is used to perform a compatibility test on the prior information and the posterior information using Bayes' theorem to obtain a compatibility test result; the prior information includes the reliability prediction information, and the posterior information includes the field operation data; wherein the compatibility test unit is specifically used to: construct a prior distribution according to the prior information; construct a confidence interval of the Bayesian interval estimation according to the field operation time and the number of associated failures using the Bayes' theorem; the confidence interval is the confidence interval of the prior distribution; perform a compatibility test on the prior information and the posterior information according to a preset test method; if the prior information is within the confidence interval, the compatibility test result is obtained, indicating that the prior information and the posterior information are compatible; if the prior information is not within the confidence interval, the compatibility test result is obtained, indicating that the prior information and the posterior information are incompatible; A fusion unit, used to fuse the prior information and the posterior information using the Bayesian theorem to obtain a fused posterior distribution when the compatibility test result represents compatibility; A conversion unit, used to convert the posterior information corresponding to the fused posterior distribution to obtain the failure rate of the rail transit signal product; The output unit is used to input the failure rate of the rail transit signal product into the reliability model of the rail transit signal product and output the RAM performance evaluation result of the rail transit signal product.

8. An electronic device, characterized in that: The electronic device comprises a processor and a memory: The memory is used to store computer programs; The processor is configured to execute the method according to any one of claims 1 to 6 according to the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

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