Instrument system safety barrier effectiveness evaluation method based on extended FRAM

Abstract

The invention discloses an extended FRAM-based instrument system safety barrier effectiveness evaluation method, which comprises the steps of determining a target function and function parameters of a system, establishing a system function FRAM model based on the target function and the function parameters, performing resonance effect analysis on the system function FRAM model to obtain a resonance effect analysis result, and evaluating the effectiveness of an instrument system safety barrier based on the resonance effect analysis result. And establishing a safety barrier of the system function FRAM model based on a resonance effect analysis result, determining a safety performance index of the system based on the safety barrier of the system function FRAM model, and performing safety barrier performance evaluation of the system based on the safety performance index. According to the invention, a safety function, a performance index function and a performance evaluation function are added on the basis of a traditional FRAM model. The interaction between human factors and system factors in the system is analyzed by considering functional resonance, the effectiveness of the safety barrier is quantitatively evaluated by analyzing the interaction relationship among parameters such as functions, tasks and resources in the system, and the evaluation result is more accurate by considering the coupling relationship among the factors.

Description

Technical Field

[0001] This invention belongs to the technical field of instrument system safety barrier effectiveness assessment, and particularly relates to an instrument system safety barrier effectiveness assessment method based on extended FRAM. Background Technology

[0002] In the petrochemical industry, safety barriers are commonly used to prevent or mitigate accidents, protecting personnel and the environment. Since most accidents are caused by a combination of hazardous events and safety barrier failure, availability assessment and reliability analysis of safety barriers are crucial. Safety instrumented systems (SIS) are vital safety barriers preventing hazardous accidents in industrial systems. However, in actual operation, SIS exhibit functional vulnerabilities such as malfunctions and false trips. To reduce accidents, the safety performance of in-service chemical process safety instruments needs to be analyzed and tested for integrity level assessment. Current integrity assessment methods primarily focus on SIS at the design stage. Since SIS is a critical piece of equipment at a specific process node in the process industry, and given the continuous and dynamic nature of its process characteristics, studying the integrity assessment of in-service SIS is more practically significant. Various assessment methods and indicator systems have been established for evaluating the performance of in-service SIS to quantify the protective effect of safety barriers. However, there is a lack of research on the effectiveness analysis of safety instrumented systems (SAS). Furthermore, performance evaluations of SAS are mostly at the design stage, and current research tends to assess the overall risk reduction effect of the SAS without analyzing each individual barrier or considering the impact of each barrier on the entire system. No specific and effective method for analyzing the effectiveness of safety barriers has been developed. While methods such as Bayesian, bowtie, and system safety process theory can qualitatively analyze each safety barrier and its effectiveness to some extent, they cannot determine the degree of effectiveness of each barrier or provide optimization suggestions based on its effectiveness. Summary of the Invention

[0003] To address the aforementioned issues, this invention provides a method for evaluating the effectiveness of safety barriers in instrumentation systems based on extended FRAM. This method solves the problems of current methods for evaluating the effectiveness of safety barriers in safety instrumentation systems, which only assess the overall system and fail to effectively analyze each barrier, or consider the impact of each barrier on the entire system, thus failing to form specific and effective safety barriers.

[0004] A method for evaluating the effectiveness of safety barriers in instrumentation systems based on extended FRAM, comprising:

[0005] Determine the system's target functions and functional parameters;

[0006] Establish a system functional FRAM model based on the target function and functional parameters;

[0007] Resonance effect analysis was performed on the FRAM model of the system function, and the results of the resonance effect analysis were obtained.

[0008] A safety barrier based on the resonance effect analysis results is established using a system functional FRAM model.

[0009] The security performance indicators of the system are determined based on the security barrier of the system function FRAM model.

[0010] The system's security barrier performance is evaluated based on security performance indicators.

[0011] According to a specific embodiment of the present invention, determining the target function and functional parameters of the system includes:

[0012] Determine the system's target functions based on the system's execution objectives and operating methods;

[0013] The functional parameters associated with each target function are determined based on the target function.

[0014] According to a specific embodiment of the present invention, the target function includes a main function and an auxiliary function. The main function is the function directly related to the system target, and the auxiliary function is the additional function required to perform the main function.

[0015] According to a specific embodiment of the present invention, the functional parameters include input, output, execution conditions, resources, control, and duration.

[0016] According to a specific embodiment of the present invention, establishing a system functional FRAM model based on the target function and functional parameters includes:

[0017] Based on the target function and functional parameters, upstream and downstream functions are determined, as well as the functional parameters related to upstream and downstream functions are determined, wherein the parameters of the downstream function are used as input parameters of the upstream function.

[0018] Determine the interaction relationships between parameters of upstream and downstream functions, and establish interaction paths between upstream and downstream functions based on the interaction relationships to form a system function FRAM model, wherein the parameters of the upstream function establish interaction paths with the parameters of at least one downstream function.

[0019] According to a specific embodiment of the present invention, resonance effect analysis is performed on the system functional FRAM model, and the resonance effect analysis results include:

[0020] The output variability of upstream functions and the impact of upstream function output variability on downstream functions are determined based on the system function FRAM model.

[0021] The output variability of the upstream function is as follows:

[0022]

[0023] In the formula, OV j For the output variability of upstream function j, The score for the time variability of upstream function j. The score for the accuracy variability of upstream function j;

[0024] The impact of the output variability of upstream functions on downstream functions is as follows:

[0025]

[0026] In the formula, This represents the impact of the time variability of upstream function j on downstream function i. This represents the impact of the accuracy variability of upstream function j on downstream function i;

[0027] Based on the output variability of upstream functions and the impact of upstream function output variability on downstream functions, the aggregation variability of upstream functions is determined as follows:

[0028]

[0029] In the formula, CV ij Indicates aggregation variability;

[0030] Based on the aggregation variability of upstream functions, the coupling variability between upstream and downstream functions is determined as follows:

[0031]

[0032] In the formula, DLFCV ij This indicates the variability of the coupling between upstream function j and all downstream functions i;

[0033] Based on the coupling variability of upstream and downstream functions, the impact of all upstream functions on the variability of downstream functions is determined as follows:

[0034]

[0035] In the formula, ULFCV ij This represents the effect of the output of all upstream functions j on the variability of downstream function i.

[0036] According to a specific embodiment of the present invention, the output variability includes time variability and precision variability.

[0037] According to a specific embodiment of the present invention, the safety barrier for establishing a system functional FRAM model based on resonance effect analysis results includes:

[0038] Based on the resonance effect analysis results, the system functional FRAM model was determined to have multiple safety functions and related parameters.

[0039] A security barrier FRAM diagram is generated based on multiple security functions and their related parameters, where the security functions include preventative security barriers and protective security barriers.

[0040] According to a specific embodiment of the present invention, determining the security performance indicators of a system based on a security barrier using a system function FRAM model includes:

[0041] Based on the analysis results of the system function FRAM model, the parameters related to security functions in the system function FRAM model are converted into security performance indicators that can be quantified. The relevant parameters of security functions include input, resources, control and execution conditions.

[0042] According to a specific embodiment of the present invention, the performance evaluation of a system's security barrier based on security performance indicators includes:

[0043] A system security performance evaluation model is established, and the security function variability of the system function FRAM model is quantitatively calculated and evaluated in combination with security performance indicators. The system security performance evaluation model is a mathematical model composed of the cascading of relevant parameters of multiple levels of target functions and security functions.

[0044] The system security performance evaluation model is expressed as follows:

[0045]

[0046] In the formula, SC p,i W represents the prediction of the variability of the i-th level objective function at a specific time. i,j ΔV represents the input weight of the j-th function. i,j W represents the variability score of the j-th functional input at time t. R,j ΔV represents the resource weight of the j-th function. R,j W represents the variability score of the j-th functional resource at time t. PC,j ΔV represents the weight of the execution condition of the j-th function. PC,j W represents the variability score of the execution condition of the j-th function at time t. C,j ΔV represents the control weight for the j-th function. C,j This represents the variability score of the j-th function control at time t, where m is the total number of relevant safety functions;

[0047]

[0048] In the formula, ΔV i This represents the output function, resources, execution conditions, and their related downstream functions at level i-1. i Control, W O It is a downstream function df i The weight of the output function, ΔV O It is an output function df i The accuracy or time variation, W R It is a downstream function df i Resource weights, ΔV R It is the variability of resource performance, W PC It is a downstream function df i The weight of the execution condition, ΔV PC It is the variability of performance under execution conditions, W C It is a downstream function df i Control weights, ΔV C It is a downstream function df i The variability of control performance, where n is the total number of downstream functions.

[0049] Compared with existing technologies, the instrument system safety barrier effectiveness evaluation method based on extended FRAM provided by this invention has the following advantages:

[0050] 1. The extended FRAM method proposed in this invention adds the functions of safety function identification, safety function performance index formulation, and safety function performance evaluation to the traditional FRAM method. The safety barrier effectiveness evaluation method based on extended FRAM overcomes the limitations of mainstream methods such as Bayesian and Bow-Tie, which can only analyze single incidents, ignore the coupling or interaction between various factors, and address failures caused by multiple reasons. This method generates more specific details by considering functional resonance processes, analyzes the interaction between human factors and system factors in the system, and quantitatively evaluates the effectiveness of safety barriers by analyzing the interaction relationships between parameters such as functions, tasks, and resources in the system. Furthermore, it considers the coupling relationships between various factors, making the evaluation results more accurate and providing valuable reference for managers to propose measures and suggestions.

[0051] 2. This invention determines the safety barriers required by the system by constructing an FRAM model based on the main functions of the system, and constructs corresponding preventive barrier FRAM models and protective barrier FRAM models. This not only allows us to understand which factors affect these barriers, but also allows us to quantitatively evaluate the performance of the safety barriers. Based on the evaluation scores, we can analyze the effectiveness of the preventive and protective safety barriers.

[0052] 3. Addressing two functional safety hazards in the actual operation of safety instrumented systems—failure to operate and false tripping—FRAM decomposition of system functions is employed. Each function considers inputs, outputs, timing, control, execution conditions, and resources. Functional relationships can represent human, hardware, and organizational behaviors and their interrelationships. Functional variability is described as output timing and accuracy. The model shows the contribution of each element to the function's outcome. Each aspect has a different perspective and contribution to the function's execution. Using FRAM for barrier identification reveals insights into how to enhance safety measures and other relevant requirements for function execution. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 This is a flowchart of a method for evaluating the effectiveness of a safety barrier in an instrument system based on an extended FRAM, according to an embodiment of the present invention.

[0055] Figure 2 This is a flowchart of a method for determining the target function and functional parameters of a system according to an embodiment of the present invention.

[0056] Figure 3 This is a flowchart of a system function FRAM model establishment method according to an embodiment of the present invention.

[0057] Figure 4 This is a flowchart of a method for analyzing the resonance effect of a system functional FRAM model according to an embodiment of the present invention.

[0058] Figure 5 This is a flowchart of a method for establishing a security barrier in a system functional FRAM model according to an embodiment of the present invention.

[0059] Figure 6 This is a schematic diagram of the coupling between functions in an FRAM according to an embodiment of the present invention.

[0060] Figure 7 This is a schematic diagram illustrating the contribution of multiple levels of functionality to the final target functionality according to an embodiment of the present invention. Detailed Implementation

[0061] To enable those skilled in the art to more clearly understand the concepts and ideas of the present invention, the present invention is described in detail below with reference to specific embodiments. It should be understood that the embodiments given herein are only a part of all possible embodiments of the present invention. Those skilled in the art, after reading this specification, are capable of making improvements, modifications, or substitutions to parts or the entirety of the following embodiments, and such improvements, modifications, or substitutions are also included within the scope of protection claimed by the present invention.

[0062] In this document, the terms "first," "second," and other similar words are not intended to imply any order, quantity, or importance, but are merely used to distinguish different elements. The terms "one," "a," and other similar words are not intended to indicate the existence of only one thing, but rather that the description pertains to only one of the things, which may have one or more. The terms "contains," "includes," and other similar words are intended to indicate a logical relationship, not a spatial one. For example, "A includes B" means that logically B belongs to A, not that spatially B is located inside A. Furthermore, the meanings of the terms "contains," "includes," and other similar words should be considered open-ended, not closed. For example, "A includes B" means that B belongs to A, but B does not necessarily constitute all of A; A may also include other elements such as C, D, and E.

[0063] In this document, the terms "embodiment," "this embodiment," "an embodiment," and "one embodiment" do not imply that the description applies only to one specific embodiment, but rather that such description may also be applicable to one or more other embodiments. Those skilled in the art will understand that any description made herein with respect to one embodiment can be substituted, combined, or otherwise combined with the descriptions in one or more other embodiments. New embodiments resulting from such substitutions, combinations, or other combinations are readily conceived by those skilled in the art and fall within the scope of protection of this invention.

[0064] Example 1

[0065] Additional aspects and advantages of embodiments of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of the invention. Figures 1-7 This invention provides a method for evaluating the effectiveness of safety barriers in instrument systems based on extended FRAM, comprising:

[0066] S1: Determine the system's target functions and functional parameters.

[0067] S2: Establish a system function FRAM model based on the target function and function parameters.

[0068] S3: Perform resonance effect analysis on the system functional FRAM model and obtain the resonance effect analysis results.

[0069] S4: Establish a safety barrier for the system function FRAM model based on the resonance effect analysis results.

[0070] S5: Determine the system's security performance indicators based on the security barrier of the system function FRAM model.

[0071] S6: Evaluate the system's security barrier performance based on security performance indicators.

[0072] The extended FRAM (Functional Random Access Memory) method for evaluating the effectiveness of safety barriers in instrument systems provided by this invention determines the required safety barriers by constructing an FRAM model based on the system's main functions. It also constructs corresponding preventative and protective barrier FRAM models. This not only reveals the factors affecting these barriers but also quantitatively evaluates their performance. Based on the evaluation scores, the effectiveness of preventative and protective safety barriers is analyzed. Furthermore, the coupling relationships between various factors are considered, resulting in more accurate evaluation results and providing valuable reference for managers to propose corrective measures.

[0073] Specifically, step S1, determining the system's target function and functional parameters, includes:

[0074] S11: Determine the system's target functions based on the system's execution objectives and operating methods. Target functions include primary functions and auxiliary functions. Primary functions are those directly related to the system's objectives, while auxiliary functions are additional functions required to execute the primary functions.

[0075] S12: Determine the functional parameters associated with each target function based on the target functions. These functional parameters include inputs, outputs, execution conditions, resources, control, and duration.

[0076] In a specific embodiment of the present invention, the main functions of the system are determined based on the goals to be achieved by the system and the system's operating mode. The entity performing the function can be technology, human resources, or an organization. In this embodiment of the invention, functions directly related to the system goals are defined as "main functions," and additional functions are required to perform the main functions; these are defined as "auxiliary functions." For example, in... Figure 6 In this context, F3 is the main function directly related to the system objective, while F1 and F2 are auxiliary functions of F3. The F3 function will be executed after F1 and F2.

[0077] In a specific embodiment of the present invention, after determining the main functions and auxiliary functions, it is necessary to determine the functional parameters related to each function, including input I, output O, execution condition P, resource R, control C, and duration T. Output O is the result of a function related to the current target task of the system or the next target task; input I is the initial task for starting or outputting the function; execution condition P is the condition that must be met to execute the function; resource R is the resource required to execute the function; control C is any device or personnel used to monitor or control the function; and time T is a factor related to the duration of the output function.

[0078] Specifically, step S2, which establishes the system functional FRAM model based on the target function and functional parameters, includes:

[0079] S21: Determine upstream and downstream functions based on target functions and function parameters, and determine the function parameters related to upstream and downstream functions, wherein the parameters of downstream functions serve as input parameters for upstream functions.

[0080] S22: Determine the interaction relationship between parameters of upstream and downstream functions, and establish the interaction path between upstream and downstream functions based on the interaction relationship to form a system function FRAM model, wherein the parameters of the upstream function establish an interaction path with the parameters of at least one downstream function.

[0081] In a specific embodiment of the present invention, the interactions between target functions are first determined based on the target functions and the parameters associated with each target function. That is, upstream and downstream functions, as well as the parameters associated with them, are determined based on the target characteristics implemented by the system. Each parameter of an upstream function points to one or more downstream functions; that is, the parameters of an upstream function must be implemented by the execution of one or more downstream functions. This determines the interaction relationships between the parameters of upstream and downstream functions. Connecting the parameters of related upstream and downstream functions forms the interaction path between them. The coupling relationship diagram between upstream and downstream functions is then displayed visually, constituting a system function FRAM model. This FRAM model treats the system as a whole, describing the system's functions and interaction paths. For example... Figure 6 The diagram illustrates the coupling between functions in the FRAM. Functions F1 and F2 are both downstream functions of F3 and also input parameters of F3. The resource parameter R of F3 is related to the "resource arrival" function F6, therefore, the resource R of F3 is connected to the output O of F6. The execution condition of function F3 is related to the "PC satisfied" function F5, therefore, the execution condition P of F3 is connected to the output O of F5. The control of function F3 is related to the "control arrival" function F4, therefore, the control C of F3 is connected to the output O of F4.

[0082] Specifically, step S3 performs resonance effect analysis on the system functional FRAM model, and the resonance effect analysis results include:

[0083] S31: Based on the system function FRAM model, determine the output variability of upstream functions and the impact of the output variability of upstream functions on downstream functions, where output variability includes time variability and precision variability.

[0084] In a specific embodiment of the present invention, the variability of each function and parameter is first determined. Each function is variable, and time is divided into four categories according to the degree of change in function variability: timely, too early, too late, and not occurred, with corresponding score values ​​of 1, 2, 3, and 4, respectively. Accuracy is divided into four categories: accurate, acceptable, inaccurate, and erroneous, with corresponding score values ​​of 1, 2, 3, and 4, respectively. This determines the possible variations in each function and parameter. The embodiment of the present invention classifies and scores time and accuracy according to the above classification method, resulting in the function variation scoring criteria shown in Table 1.

[0085] Table 1 Functional Change Scoring Table

[0086]

[0087] Because the output variability (time, accuracy) of a function is highly correlated with other parameters of the same function (inputs, execution conditions, resources, and control), any change in the performance of these parameters will affect the output function, and thus the associated objectives. When the output variability of multiple functions resonates, the results of upstream functions may change unexpectedly. While the output variability of a single function is usually insufficient to cause an accident on its own, when the output variability of multiple functions resonates, the variability of multiple functions may exceed standard limits, leading to an accident. The impact of the output variability of upstream functions on downstream functions is shown in Table 2.

[0088] Table 2. Impact of Upstream Functional Output Variability on Downstream Functions

[0089]

[0090]

[0091] In Table 2, 1 indicates that "the variability of upstream function output has no impact on downstream function", 2 indicates that "the variability of upstream function output has an amplifying effect on downstream function", and 0.5 indicates that "the variability of upstream function output has a damping effect on downstream function".

[0092] Combining Tables 1 and 2, the formula for calculating the variability of upstream function output is defined as follows:

[0093]

[0094] In the formula, OV j For the output variability of upstream function j, The score for the time variability of upstream function j. The score represents the accuracy variability of the upstream function j.

[0095] Define the effect of the output variability of upstream function j on downstream function i as follows:

[0096]

[0097] In the formula, This represents the impact of the time variability of upstream function j on downstream function i. This represents the impact of the accuracy variability of upstream function j on downstream function i.

[0098] S32: Based on the output variability of upstream functions and the impact of the output variability of upstream functions on downstream functions, the aggregate variability of upstream functions is determined as follows:

[0099]

[0100] In the formula, CV ij This indicates aggregation variability.

[0101] S33: Based on the aggregation variability of upstream functions, the coupling variability between upstream and downstream functions is determined as follows:

[0102] DLFCV ij =Σ i CV ij (5)

[0103] In the formula, DLFCV ij This indicates the variability of the coupling between upstream function j and all downstream functions i.

[0104] S34: Based on the coupling variability of upstream and downstream functions, the impact of all upstream functions on the variability of downstream functions is determined as follows:

[0105] ULFCV ij =∑ j CV ij (6)

[0106] In the formula, ULFCV ij This represents the effect of the output of all upstream functions j on the variability of downstream function i.

[0107] In this embodiment of the invention, the root cause of variability is related to other functions, so the resonance effect or influencing factors of variables can be determined by aggregating the potential variability of each function.

[0108] Specifically, the safety barriers in step S4, which establishes the system functional FRAM model based on the resonance effect analysis results, include:

[0109] S41: Based on the resonance effect analysis results, determine the multiple safety functions of the system functional FRAM model and the relevant parameters of the safety functions.

[0110] S42: Generate a security barrier FRAM diagram based on multiple security functions and their related parameters, where the security functions include preventative security barriers and protective security barriers.

[0111] In a specific embodiment of this invention, the required security functions and their related parameters are first determined to avoid variability in these functions and parameters. Security functions are related to the secure execution of primary and auxiliary functions, and their settings are determined by the resonance effect of variables. Each security function represents a security barrier; when a relevant security function cannot be executed in a timely manner, the system's security is reduced. Security functions include the following three types:

[0112] (1) Safety function SF1 is used to eliminate the cause of abnormal parameter states due to resonance with downstream functions and parameters.

[0113] (2) Safety function SF2, used to eliminate the cause of abnormal parameter status due to external influences.

[0114] (3) Safety function SF3 used to mitigate abnormal parameter states that affect upstream functions.

[0115] The embodiments of the present invention consider all possible security functions to establish security redundancy and avoid changes in functions and parameters. For example, three security functions are set, namely SF1, SF2 and SF3, for precise execution of function F3.

[0116] In another embodiment of the invention, the required safety function only considers safety function SF1, which is used to eliminate the cause of abnormal parameter states due to resonance with downstream functions and parameters.

[0117] Once the safety functions are determined, each parameter related to a safety function is defined. The achievement of the system objective depends on the state of the output functions, which in turn depends on the state of the input functions, execution conditions, resources, control, and time. These functional states depend on the execution of the safety functions. If a safety function fails to execute, it will affect other functions.

[0118] Specifically, step S5, which determines the system's security performance indicators based on the system function FRAM model, includes:

[0119] Based on the analysis results of the system function FRAM model, the parameters related to security functions in the system function FRAM model are converted into security performance indicators of quantifiable attributes. The relevant parameters of security functions include input, resources, control and execution conditions.

[0120] In a specific embodiment of the present invention, the system's security performance indicators are determined based on the analysis results of the system function FRAM model obtained in step S4. This involves converting the relevant parameters of the security functions into quantifiable quantities to determine the security performance indicators. During the conversion, the input functions, resources, or controls related to the security functions are transformed into measurable attributes, which serve as security performance indicators. For example, the performance indicators of the security function parameters used by a certain enterprise are shown in Table 3:

[0121] Table 3 Performance Indicators of Safety Function Parameters

[0122]

[0123]

[0124] Specifically, step S6, which evaluates the system's security barrier performance based on security performance indicators, includes:

[0125] A system security performance evaluation model is established, and the security function variability of the system function FRAM model is quantitatively calculated and evaluated in combination with security performance indicators. The system security performance evaluation model is a mathematical model composed of the cascading of relevant parameters of multiple levels of target functions and security functions.

[0126] In a specific embodiment of the present invention, Figure 7 The diagram illustrates the contribution of multiple levels of functionality to the final target function. The system consists of multiple levels, and the performance of the target function depends on the contributions of various parameters from different levels. That is, the simultaneous execution of functions at each level depends on the contributions of multiple levels of functionality to the final target function, such as... Figure 7 As shown, F3 is the main function directly related to the objective. Among the relevant parameters of F3, resource R, control C, execution condition P, and input function I are connected to this function at level i-1. Each parameter is related to other required functions at level i-1, and each function at level i-1 is related to other functions at level i-2. Therefore, two factors can be used to evaluate the safety performance of the system: parameter weights and deviations.

[0127] The parameter weights can be divided into three levels: Level 1, Level 2, and Level 3. Level 1 represents a low weight. When a parameter weight is 1, it indicates that the function is not directly related to the primary or auxiliary function, but rather to the safety function. The variability of this parameter will have minimal impact on the main output function or objective. For example... Figure 7In this context, parameters at level i-2 have lower weights, hence a level of 1. Level 2 represents a medium weight. When a parameter's weight is level 2, it indicates that the function is not directly related to the primary function but rather to the auxiliary function. In this case, the variability of the parameter will moderately or slightly affect the primary output function or objective. For example... Figure 7 In this model, parameters at level i-1 have medium weight, therefore a level of 2. Level 3 indicates high weight; when a parameter's weight is level 3, it means the function is directly related to the main function, and the variability of this parameter directly affects the main function or objective. For example... Figure 7 In this context, parameters at level i have higher weights, hence their level is 3.

[0128] Deviation is a variability score determined based on the current performance of the function and parameters and their ideal state. The current performance can be determined by monitoring performance metrics in previous states. If each parameter is in an ideal state, the variability will be 1, and the output will be accurate in both precision and time. Therefore, a variability score table from 1 to 4 can be created, as shown in Table 1, the function variation score table, to find the overall output score, where 4 represents the maximum variability state. A maximum variability of 4 means that the output function has no output, resources are completely missing, execution conditions are not met, or control is absent.

[0129] The system's security performance can be determined based on parameter weights and biases. The constructed system security performance evaluation model is as follows:

[0130]

[0131] In the formula, SC p,i W represents the prediction of the variability of the i-th level objective function at a specific time. i,j ΔV represents the input weight of the j-th function. i,j W represents the variability score of the j-th functional input at time t. R,j ΔV represents the resource weight of the j-th function. R,j W represents the variability score of the j-th functional resource at time t. PC,j ΔV represents the weight of the execution condition of the j-th function. PC,j W represents the variability score of the execution condition of the j-th function at time t. C,j ΔV represents the control weight for the j-th function. C,j This represents the variability score of the j-th function control at time t, where m is the total number of related safety functions.

[0132]

[0133] In the formula, ΔV i This represents the output function, resources, execution conditions, and their related downstream functions at level i-1. i Control, WO It is a downstream function df i The weight of the output function, ΔV O It is an output function df i The accuracy or time variation, W R It is a downstream function df i Resource weights, ΔV R It is the variability of resource performance, W PC It is a downstream function df i The weight of the execution condition, ΔV PC It is the variability of performance under execution conditions, W C It is a downstream function df i Control weights, ΔV C It is a downstream function df i The variability of control performance, where n is the total number of downstream functions.

[0134] In summary, the instrument system safety barrier effectiveness evaluation method based on extended FRAM described in this invention has the following advantages:

[0135] 1. The extended FRAM method proposed in this invention adds the functions of safety function identification, safety function performance index formulation, and safety function performance evaluation to the traditional FRAM method. The safety barrier effectiveness evaluation method based on extended FRAM overcomes the limitations of mainstream methods such as Bayesian and Bow-Tie, which can only analyze single incidents, ignore the coupling or interaction between various factors, and address failures caused by multiple reasons. This method generates more specific details by considering functional resonance processes, analyzes the interaction between human factors and system factors in the system, and quantitatively evaluates the effectiveness of safety barriers by analyzing the interaction relationships between parameters such as functions, tasks, and resources in the system. Furthermore, it considers the coupling relationships between various factors, making the evaluation results more accurate and providing valuable reference for managers to propose measures and suggestions.

[0136] 2. This invention determines the safety barriers required by the system by constructing an FRAM model based on the main functions of the system, and constructs corresponding preventive barrier FRAM models and protective barrier FRAM models. This not only allows us to understand which factors affect these barriers, but also allows us to quantitatively evaluate the performance of the safety barriers. Based on the evaluation scores, we can analyze the effectiveness of the preventive and protective safety barriers.

[0137] 3. Addressing two functional safety hazards in the actual operation of safety instrumented systems—failure to operate and false tripping—FRAM decomposition of system functions is employed. Each function considers inputs, outputs, timing, control, execution conditions, and resources. Functional relationships can represent human, hardware, and organizational behaviors and their interrelationships. Functional variability is described as output timing and accuracy. The model shows the contribution of each element to the function's outcome. Each aspect has a different perspective and contribution to the function's execution. Using FRAM for barrier identification reveals insights into how to enhance safety measures and other relevant requirements for function execution.

[0138] The concepts, principles, and ideas of the present invention have been described in detail above with reference to specific embodiments (including examples and instances). Those skilled in the art should understand that the embodiments of the present invention are not limited to those given above. After reading this application, those skilled in the art can make any possible improvements, substitutions, and equivalents to the steps, methods, systems, and components in the above embodiments. These improvements, substitutions, and equivalents should be considered to fall within the scope of the present invention, and the scope of protection of the present invention is limited to the claims.

Claims

1. A method for evaluating the effectiveness of safety barriers in instrument systems based on extended FRAM, characterized in that, include: Determine the system's target functions and functional parameters; Establish a system function FRAM model based on the target function and the function parameters; Resonance effect analysis was performed on the FRAM model of the system function, and the results of the resonance effect analysis were obtained. A safety barrier for the system function FRAM model is established based on the resonance effect analysis results; The security performance indicators of the system are determined based on the security barriers of the system's functional FRAM model. The system's security barrier performance is evaluated based on the aforementioned security performance indicators.

2. The method for evaluating the effectiveness of instrument system safety barriers based on extended FRAM according to claim 1, characterized in that, The determination of the system's target functions and functional parameters includes: Determine the system's target functions based on the system's execution objectives and operating methods; Based on the target functions, determine the functional parameters associated with each target function.

3. The method for evaluating the effectiveness of instrument system safety barriers based on extended FRAM according to claim 2, characterized in that, The target functions include primary functions and auxiliary functions. The primary functions are those directly related to the system objectives, and the auxiliary functions are additional functions required to perform the primary functions.

4. The method for evaluating the effectiveness of instrument system safety barriers based on extended FRAM according to claim 2, characterized in that, The functional parameters include input, output, execution conditions, resources, control, and duration.

5. The method for evaluating the effectiveness of instrument system safety barriers based on extended FRAM according to claim 1, characterized in that, The establishment of the system function FRAM model based on the target function and the function parameters includes: Based on the target function and the function parameters, upstream and downstream functions are determined, and function parameters related to the upstream and downstream functions are determined, wherein the parameters of the downstream function are used as input parameters of the upstream function. The interaction relationships between parameters of the upstream function and the downstream function are determined, and the interaction paths between the upstream function and the downstream function are established based on the interaction relationships to form a system function FRAM model, wherein the parameters of the upstream function establish an interaction path with the parameters of at least one of the downstream functions.

6. The method for evaluating the effectiveness of instrument system safety barriers based on extended FRAM according to claim 1, characterized in that, The resonance effect analysis of the system functional FRAM model yields the following results: The output variability of the upstream function and its impact on the downstream function are determined based on the system function FRAM model. The output variability of the upstream function is as follows: OV j =V i T *V j P In the formula, OV j For the output variability of upstream function j, The score for the time variability of upstream function j. The score for the accuracy variability of upstream function j; The impact of the output variability of the upstream function on the downstream function is as follows: In the formula, This represents the impact of the time variability of upstream function j on downstream function i. This represents the impact of the accuracy variability of upstream function j on downstream function i; Based on the output variability of the upstream function and its impact on the downstream function, the aggregate variability of the upstream function is determined as follows: In the formula, CV ij Indicates aggregation variability; Based on the aggregation variability of the upstream function, the coupling variability between the upstream function and the downstream function is determined as follows: In the formula, DLFCV ij This indicates the variability of the coupling between upstream function j and all downstream functions i; Based on the coupling variability between the upstream and downstream functions, the impact of all upstream functions on the variability of the downstream functions is determined as follows: In the formula, ULFCV ij This represents the effect of the output of all upstream functions j on the variability of downstream function i.

7. The method for evaluating the effectiveness of instrument system safety barriers based on extended FRAM according to claim 6, characterized in that, The output variability includes time variability and precision variability.

8. The method for evaluating the effectiveness of safety barriers in instrument systems based on extended FRAM according to claim 1, characterized in that, The safety barriers for establishing the system functional FRAM model based on the resonance effect analysis results include: Based on the resonance effect analysis results, the system functional FRAM model is determined to have multiple security functions and related parameters of the security functions. A security barrier FRAM diagram is generated based on multiple security functions and their related parameters, wherein the security functions include preventative security barriers and protective security barriers.

9. The method for evaluating the effectiveness of instrument system safety barriers based on extended FRAM according to claim 8, characterized in that, The security performance indicators of the system determined by the security barrier based on the system function FRAM model include: Based on the analysis results of the system function FRAM model, the parameters related to the security function in the system function FRAM model are converted into security performance indicators that can be quantified. The relevant parameters of the security function include input, resources, control and execution conditions.

10. The method for evaluating the effectiveness of safety barriers in instrument systems based on extended FRAM according to claim 1, characterized in that, The system's security barrier performance evaluation based on the aforementioned security performance indicators includes: A system security performance evaluation model is established, and the security function variability of the system functional FRAM model is quantitatively calculated and evaluated in conjunction with the security performance indicators. The system security performance evaluation model is a mathematical model constructed by cascading relevant parameters of multiple levels of target functions and security functions. The system security performance evaluation model is expressed as follows: In the formula, SC p,i W represents the prediction of the variability of the i-th level objective function at a specific time. i,j ΔV represents the input weight of the j-th function. i,j W represents the variability score of the j-th functional input at time t. R,j ΔV represents the resource weight of the j-th function. R,j W represents the variability score of the j-th functional resource at time t. PC,j ΔV represents the weight of the execution condition of the j-th function. PC,j W represents the variability score of the execution condition of the j-th function at time t. C,j ΔV represents the control weight for the j-th function. C,j This represents the variability score of the j-th function control at time t, where m is the total number of relevant safety functions; In the formula, ΔV i This represents the output function, resources, execution conditions, and their related downstream functions at level i-1. i Control, W O It is a downstream function df i The weights of the output function, ΔV O It is an output function df i The accuracy or time variation, W R It is a downstream function df i Resource weights, ΔV R It is the variability of resource performance, W PC It is a downstream function df i The weight of the execution condition, ΔV PC It is the variability of performance under execution conditions, W C It is a downstream function df i Control weights, ΔV C It is a downstream function df i The variability of control performance, where n is the total number of downstream functions.

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