Instrument control system fault analysis method, device, equipment, medium and product

Abstract

The invention discloses an instrument control system fault analysis method and device, equipment, a medium and a product, and relates to the technical field of instrument control system operation and maintenance, and the method comprises the steps: obtaining a fault mode rule base and a multi-dimensional dynamic evaluation matrix of an instrument control system; when the instrument control system breaks down, a diagnosis code of the current fault, fault equipment corresponding to the current fault, and equipment safety grading and real-time operation working conditions of the instrument control system are obtained; matching the diagnosis code with a fault code in a fault mode rule base to obtain a current fault mode; adjusting the initial weight of each evaluation dimension according to the fault equipment corresponding to the current fault, the equipment safety grade of the instrument control system and the real-time operation condition; and according to the dynamic weight of each evaluation dimension and the grade score of each evaluation dimension of the current fault mode, determining the risk priority number of the current fault and displaying the risk priority number. According to the invention, the maintenance decision-making efficiency and guarantee capability of the complex instrument control system are improved.

Description

Technical Field

[0001] This application relates to the field of instrumentation and control system operation and maintenance technology, and in particular to an instrumentation and control system fault analysis method, device, equipment, medium and product. Background Technology

[0002] As the nerve center of a nuclear power plant, the digital instrumentation and control system bears the core mission of monitoring and controlling the reactor shutdown system and its auxiliary systems. Its reliability and safety directly affect the safety baseline of nuclear energy utilization. System availability, a key indicator of instrumentation and control system reliability, depends not only on the inherent reliability of the equipment, such as Mean Time Between Failures (MTBF), but also on the efficiency of maintenance after a failure, such as Mean Time To Repair (MTTR). Current research focuses primarily on extending MTBF through redundant design and hardware upgrades, while relatively neglecting the important parallel path of improving availability by optimizing maintenance strategies to shorten MTTR. Therefore, the current field of instrumentation and control system maintenance technology suffers from the following technical shortcomings:

[0003] Fragmented fault identification: Traditional methods rely on expert experience to judge faults and lack systematic fault mode identification tools, resulting in untimely and incomplete discovery of potential fault modes and difficulty in constructing a complete fault map.

[0004] The assessment dimensions are too singular: Traditional assessment models often focus on a single dimension such as failure probability or severity of consequences, lacking analysis of the coupling effect of multiple dimensions such as failure detection difficulty and repair complexity, making it difficult to comprehensively assess the overall impact of failure.

[0005] Rigid Priority Assessment: A fixed weighting system is used for fault ranking, failing to establish a dynamic correlation mechanism between equipment classification and risk factors. When equipment classification is adjusted or operating conditions change, the static weighting system cannot accurately reflect the real-time risk situation.

[0006] Lack of dynamic adaptability: The failure to establish a real-time mapping relationship between equipment operating status and assessment strategies results in the assessment system being unable to reflect dynamic changes in the system, making it difficult to make preventive maintenance decisions in real time. Summary of the Invention

[0007] The purpose of this application is to provide a method, apparatus, equipment, medium, and product for fault analysis of instrumentation and control systems, which can improve the efficiency of maintenance decision-making and the assurance capability of complex instrumentation and control systems.

[0008] To achieve the above objectives, this application provides the following solution:

[0009] Firstly, this application provides a method for fault analysis of an instrumentation and control system, including:

[0010] The system acquires a fault mode rule base and a multi-dimensional dynamic evaluation matrix for the instrumentation and control system. The fault mode rule base includes multiple fault modes and fault codes corresponding to each fault mode. The multi-dimensional dynamic evaluation matrix includes the level scores of multiple evaluation dimensions for each fault mode and the initial weight of each evaluation dimension.

[0011] When a fault occurs in the instrumentation and control system, obtain the diagnostic code of the current fault, the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating status;

[0012] The diagnostic code is matched with the fault codes in the fault mode rule base to obtain the current fault mode;

[0013] Based on the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating conditions, the initial weight of each evaluation dimension is adjusted to obtain the dynamic weight of each evaluation dimension.

[0014] Based on the dynamic weight of each assessment dimension and the rating score of each assessment dimension of the current failure mode, the risk priority of the current failure is determined and displayed.

[0015] Secondly, this application provides a fault analysis device for an instrumentation and control system, comprising:

[0016] The pre-data acquisition module is used to acquire the fault mode rule base and multi-dimensional dynamic evaluation matrix of the instrumentation and control system. The fault mode rule base includes multiple fault modes and the fault code corresponding to each fault mode. The multi-dimensional dynamic evaluation matrix includes the level scores of multiple evaluation dimensions of each fault mode and the initial weight of each evaluation dimension.

[0017] The fault data acquisition module is used to acquire the diagnostic code of the current fault, the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating status when a fault occurs in the instrumentation and control system.

[0018] The fault mode matching module is used to match the diagnostic code with the fault codes in the fault mode rule base to obtain the current fault mode;

[0019] The weight adjustment module is used to adjust the initial weight of each evaluation dimension based on the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating conditions, so as to obtain the dynamic weight of each evaluation dimension.

[0020] The risk calculation module is used to determine and display the risk priority of the current failure based on the dynamic weight of each assessment dimension and the level score of each assessment dimension of the current failure mode.

[0021] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described instrumentation and control system fault analysis method.

[0022] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described instrumentation and control system fault analysis method.

[0023] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described instrumentation and control system fault analysis method.

[0024] According to the specific embodiments provided in this application, this application has the following technical effects:

[0025] This application provides a method, device, equipment, medium, and product for fault analysis of instrumentation and control systems. By pre-constructing a fault mode rule base and a multi-dimensional dynamic evaluation matrix for the instrumentation and control system, it can automatically identify and parse the fault codes of faulty equipment in the instrumentation and control system when a fault occurs. Combining the equipment safety classification and real-time operating conditions, and through the multi-dimensional dynamic evaluation matrix and adaptive dynamic weight adjustment, it can accurately calculate the risk priority number of the current fault, which helps to quickly repair the fault and improves the maintenance decision-making efficiency and assurance capability of complex instrumentation and control systems. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is an application environment diagram of a fault analysis method for an instrumentation and control system according to an embodiment of this application;

[0028] Figure 2 This application provides an overall flowchart of a fault analysis method for an instrumentation and control system according to an embodiment of the present application.

[0029] Figure 3 A detailed process diagram of a fault analysis method for an instrumentation and control system provided in an embodiment of this application;

[0030] Figure 4 A functional module diagram of an instrumentation and control system fault analysis device provided in an embodiment of this application;

[0031] Figure 5This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] The instrumentation and control system fault analysis method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send the diagnostic code of the current fault, the faulty device corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating status to server 104. After receiving the diagnostic code, the faulty device, the equipment safety classification of the instrumentation and control system, and the real-time operating status, server 104 matches the diagnostic code with the fault codes in the fault mode rule base to obtain the current fault mode; based on the faulty device, the equipment safety classification of the instrumentation and control system, and the real-time operating status, it adjusts the initial weight of each evaluation dimension to obtain the dynamic weight of each evaluation dimension; based on the dynamic weight of each evaluation dimension and the level score of each evaluation dimension of the current fault mode, it determines and displays the risk priority number of the current fault. Server 104 can provide feedback on the risk priority number of the current fault to terminal 102. In addition, in some embodiments, the instrumentation and control system fault analysis method can also be implemented separately by the server 104 or the terminal 102.

[0035] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0036] In one exemplary embodiment, such as Figure 2 and Figure 3 As shown, a fault analysis method for an instrumentation and control system is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes the following steps 201 to 208.

[0037] Step 201: Obtain the fault mode rule base and multi-dimensional dynamic evaluation matrix of the instrumentation and control system.

[0038] The fault mode rule base includes multiple fault modes and the corresponding fault codes for each fault mode. The fault mode rule base is pre-constructed based on the instrumentation and control system's structure tree, using Failure Mode, Effects and Criticality Analysis (FMECA) and Diagnosis Analysis (DA) methods.

[0039] In a specific application example, based on the equipment configuration and hierarchical structure of the instrumentation and control system, the FMECA and DA methods are used to identify all potential fault modes and their effects layer by layer from bottom to top, constructing a fault mode rule base for the instrumentation and control system, and assigning a unique fault code to each fault mode. Specifically, an 8-bit coding structure can be used: 2-bit system identifier - 2-bit subsystem identifier - 2-bit chassis identifier - 2-bit board slot identifier - 3-bit fault sequence number. Example: A fault in the acquisition board channel of slot 7 of the main control chassis of the reactor protection system station 1 has a fault code of "01-01-01-07-002".

[0040] First, based on the equipment configuration and hierarchical structure tree, the FMECA method is used to perform Fault Mode and Effects Analysis (FMECA). The analysis results are then used for diagnostic analysis using the DA method to obtain the fault mode rule base of the instrumentation and control system.

[0041] The multidimensional dynamic evaluation matrix includes the level scores of multiple evaluation dimensions for each fault mode and the initial weight of each evaluation dimension. The multidimensional dynamic evaluation matrix is ​​a pre-constructed multidimensional index evaluation matrix system for the fault priority of instrumentation and control system equipment, based on a fault mode rule base.

[0042] The assessment dimensions are fault severity, fault occurrence probability, fault detectability difficulty, and fault repair difficulty. The following section details the process for determining the grade scores for each assessment dimension.

[0043] (1) The severity level score of the fault is determined in advance using the risk matrix method.

[0044] Specifically, taking into account the impacts of safety, environment, and economy, a three-dimensional risk matrix was constructed for the failure modes of all instrumentation and control system equipment using an expert scoring method. The severity of all failure modes was evaluated from three dimensions, as shown in Table 1.

[0045] Table 1 Examples of Fault Severity Levels and Scores

[0046] Evaluation Dimensions Level Classification Range of values Safety Impact A A1~A10 1-10 Environmental Impact E E1~E10 1-10 Economic impact C C1~C10 1-10

[0047] Assuming that the weights of safety, environment, and economy in the severity level classification of failure modes are wA, wE, and wC respectively, the severity level score of the i-th failure mode is Si = wA*Ai + wE*Ei + wC*Ci, where Ai is the safety impact level of the i-th failure mode, Ei is the environmental impact level of the i-th failure mode, and Ci is the economic impact level of the i-th failure mode. Based on this formula and the rounding principle, the failure severity level scores of all failure modes are comprehensively evaluated, as shown in Table 2.

[0048] Table 2 Examples of Fault Severity Level Scores

[0049] Fault Severity Level Score S1 [0～1.5) S2 [1.5～2.5) S3 [2.5～3.5) S4 [3.5～4.5) S5 [4.5～5.5) S6 [5.5～6.5) S7 [6.5～7.5) S8 [7.5～8.5) S9 [8.5～9.5) S10 [9.5～10.0]

[0050] (2) The level score of the probability of the fault occurrence is determined in advance using the Bayesian network method.

[0051] Specifically, based on failure rate prediction data or experimental data, the exponential distribution is selected as the prior distribution, historical failure data is integrated to estimate prior parameters, and Bayesian analysis is used to infer the posterior distribution in combination with real-time data to dynamically assess the failure probability of each failure mode.

[0052] Assuming that the equipment fault data of the instrumentation and control system follows an exponential distribution, the determination process is as follows.

[0053] 1) The exponential distribution, as the prior distribution, has the following probability density function: f(λ) = βe^(-λ / λ). -λt Where λ is the failure rate, t is time, and β is the hyperparameter of the prior distribution, reflecting the prior confidence level of the failure rate, which is estimated using maximum likelihood estimation or the method of moments. For example, if the total failure time in historical data is T, the number of failures is n, and the total number of failures is N, then the prior estimate of the failure rate is... for: By choosing an appropriate β, the mean or variance of the prior distribution can be made consistent with the prior estimate of the failure rate.

[0054] 2) Assuming that k faults occur during the observation period, the likelihood function is:

[0055] 3) According to Bayes' theorem, the posterior distribution is: f(λ|k,t)∝f(λ)·L(λ|k,t); where ∝ indicates that the two are proportional.

[0056] 4) Substituting the prior distribution and likelihood function, we obtain the posterior distribution:

[0057] 5) After simplification, the gamma distribution is obtained: Where α' is the shape parameter, α' = k+1, β' is the rate parameter, β' = β+t, and Γ(α') is the gamma function.

[0058] 6) To calculate the probability of a failure occurring within time T, we can integrate over the posterior distribution:

[0059] The probability classification of failure modes is shown in Table 3.

[0060] Table 3 Examples of Failure Mode Occurrence Probability Levels and Scores

[0061] Fault Occurrence Probability Level probability value Score P1 (0-0.2] (0-2] P2 (0.2-0.4] (2-4] P3 (0.4-0.6] (4-6] P4 (0.7-0.8] (7-8] P5 (0.9-1.0] (9-10]

[0062] (3) The level score of the detectability difficulty of the fault is determined in advance using the fuzzy comprehensive evaluation method.

[0063] Specifically, the detectability difficulty of faults is categorized based on the effectiveness of the fault detection methods and approaches. First, based on the detection method, it is divided into manual detection, automated tooling testing, and in-machine self-diagnostic testing. The effectiveness of the detection measures is categorized into high, medium, and low levels based on the accuracy of fault detection. Then, membership degrees (0-1) are used to represent the contribution of each factor to the evaluation level. The final detectability difficulty level score is obtained through weighted summation, as shown in Table 4. The specific calculation process is as follows:

[0064] 1) Determine the factor set U = {u1, u2} and the level set D = {d1, d2, d3}; where u1 is the detection method, u2 is the detection effectiveness, and d1, d2, and d3 are three levels of the difficulty of fault detection.

[0065] 2) Construct the fuzzy evaluation matrix R = [r ij ], where r ij For factor u i For level d j The degree of membership.

[0066] 3) Calculate the overall evaluation value: Where a1 is the weight of the detection method and a2 is the weight of the detection effectiveness.

[0067] 4) Defuzzification calculation: D' = B·V TWhere D' is the level score of the detectability difficulty of each fault mode. The higher the level score, the more difficult the fault detection. V is a column vector that usually contains the membership standardization coefficients of each level, that is, the standard values ​​of d1, d2, and d3 or the values ​​of the membership interval.

[0068] Table 4 Examples of Fault Detection Difficulty Levels and Scores

[0069] Fault Detection Difficulty Level Level Description Score d1 easy (0-3] d2 middle (3-7] d3 Disaster (7-10]

[0070] (4) The level score of the fault repair difficulty is determined in advance using the entropy weight method. The higher the level score of the fault repair difficulty, the greater the fault repair difficulty.

[0071] Specifically, by combining maintenance procedure databases and spare parts supply chain data, key indicators affecting the difficulty of fault repair were identified: spare parts availability and repair time. Spare parts availability includes spare parts inventory levels, supplier response time, and spare parts prices; repair time includes repair steps, repair time, and repair technical difficulty. The following methods were then used to determine the level score of fault repair difficulty.

[0072] 1) Data Collection and Standardization. Relevant indicator data are extracted from the maintenance procedure database and spare parts supply chain data, and then standardized. The standardized matrix X' = (x') is obtained pq ) l×m , where x pq The index data is before standardization, x' pq The data represents standardized metrics, where l represents the number of failure modes and m represents the number of evaluation metrics.

[0073] 2) Calculate information entropy and weights. Based on the standardized data, calculate the weights, information entropy, and difference coefficients for each indicator:

[0074]

[0075] g q =1-e q ;

[0076]

[0077] Among them, P pq e represents the weight of the indicator. q Let g be the information entropy, h be a constant of the information entropy (usually taken as 1 / ln(I), where I is the number of samples), and g be the information entropy. q w is the difference coefficient. q The weights are the indicator weights.

[0078] 3) Calculate the comprehensive evaluation value. Use the weighted summation method to calculate the level score R for the difficulty of fault repair. p :

[0079] The initial weights for each evaluation dimension are determined in advance using the analytic hierarchy process, as follows.

[0080] 1) Constructing the judgment matrix: Assume that "F" is used. f "" represents one of the following factors: fault severity, fault occurrence probability, fault detectability difficulty, and fault repair difficulty. A judgment matrix is ​​constructed by comparing each of the four factors pairwise: Among them, F fg F represents f Relative element F g The relative importance of elements is represented by their reciprocals, where f = 1, 2, 3, 4 and g = 1, 2, 3, 4. In the judgment matrix, an element value of "1" indicates that the two elements are equally important; a value of "3" indicates that one element is slightly more important than the other; a value of "5" indicates that one element is significantly more important than the other; a value of "7" indicates that one element is very important than the other; and a value of "9" indicates that one element is extremely important than the other.

[0081] 2) Calculate the largest eigenvalue λ of the judgment matrix. max And the feature vector [v1,v2,v3,v4]. The element values ​​corresponding to the feature vector are the weight values ​​of each feature.

[0082] 3) Divide each element of the feature vector by the sum of all elements to obtain the normalized weights.

[0083] 4) Calculate the consistency index and random consistency index, and perform consistency checks on the judgment matrix to ensure the rationality of the judgment matrix.

[0084] Calculate the consistency index (CI): Where o is the order of the judgment matrix (i.e., the number of analysis elements).

[0085] Calculate the random consistency ratio (CR): Here, RI is a random consistency index that depends on the order of the matrix.

[0086] Table 5. RI values ​​of judgment matrices of orders 1-10

[0087]

[0088]

[0089] When CR < 0.1, the consistency of the judgment matrix is ​​good; when CR > 0.1, the judgment matrix needs to be readjusted.

[0090] Step 202: When a fault occurs in the instrumentation and control system, obtain the diagnostic code of the current fault, the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating status.

[0091] Step 203: Match the diagnostic code with the fault codes in the fault mode rule base to obtain the current fault mode.

[0092] Step 204: Based on the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating conditions, adjust the initial weight of each evaluation dimension to obtain the dynamic weight of each evaluation dimension.

[0093] In a specific application example, the instrumentation and control system's equipment has built-in fault diagnosis code. First, based on the faulty equipment corresponding to the current fault and the safety classification of the instrumentation and control system's equipment, the safety level of the currently faulty equipment is determined. Then, based on the safety level of the currently faulty equipment and its real-time operating conditions, adjustment factors are determined. Next, the initial weights of each evaluation dimension are adjusted according to the adjustment factors and normalized to obtain the dynamic weights of each evaluation dimension.

[0094] The specific adjustment formula is: W g =W g0 ×f g (g); where W g W represents the dynamic weight of the g-th evaluation dimension. g0 f is the initial weight for the g-th evaluation dimension. g (g) is the adjustment factor for the g-th evaluation dimension, f g (g)=1+ε·Δ g ε is the sensitivity coefficient of the adjustment factor, Δ g The adjustment range is dynamically adjusted based on the equipment classification and operating conditions controlled by the instrumentation and control system (e.g., increasing the weight when the equipment failure rate increases).

[0095] The adjusted weights are further normalized to ensure that the total weight is always 1. After normalization, the dynamic weights of each evaluation dimension are determined as W. S '、W P '、W D '、W M '; among which, W S 'The dynamic weight of its fault severity, W' P 'W' represents the dynamic weight of the probability of failure occurrence. D 'W' represents the dynamic weight of the difficulty in detecting faults. M 'This represents the dynamic weight of the difficulty in fault repair.'

[0096] Step 205: Determine and display the risk priority number of the current fault based on the dynamic weight of each assessment dimension and the level score of each assessment dimension of the current fault mode.

[0097] In a specific application example, a weighted comprehensive evaluation method is used to calculate the priority of failure modes of instrumentation and control system equipment. Specifically, a risk priority number is determined for each type of failure. The higher the risk priority number, the greater the impact of the failure, the greater the difficulty and economic cost of handling it, the higher the priority of the failure mode that needs to be addressed, and the more attention it needs to receive from operation and maintenance personnel.

[0098] The formula for calculating Risk Priority Number (RPN) is: RPN = S' × W S '+P'×W P '+D'×W D '+M'×W M '; where S' is the severity rating of the fault, P' is the probability rating of the fault occurrence, D' is the difficulty rating of the fault detection, and M' is the difficulty rating of the fault repair.

[0099] The number of current faults is one or more. When the number of current faults is multiple, the instrumentation and control system fault analysis method further includes: sorting the priorities of multiple current faults according to the risk priority number of each current fault.

[0100] This application can automatically identify and resolve fault codes of key equipment in an instrumentation and control system. Combining the safety classification and real-time operating conditions of different equipment, and through a multi-dimensional dynamic evaluation matrix and adaptive dynamic weight adjustment, it accurately calculates the fault risk priority number. When multiple faults exist simultaneously in the instrumentation and control system, it can prioritize these faults, providing a basis for on-site maintenance personnel to determine the order of fault repair, enabling rapid fault repair, improving maintenance efficiency, and making it suitable for large-scale industrial applications.

[0101] Based on the same inventive concept, this application also provides an instrumentation and control system fault analysis device for implementing the instrumentation and control system fault analysis method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the instrumentation and control system fault analysis device provided below can be found in the limitations of the instrumentation and control system fault analysis method described above, and will not be repeated here.

[0102] In one exemplary embodiment, such as Figure 4 As shown, a fault analysis device for an instrumentation and control system is provided, comprising: a pre-data acquisition module 401, a fault data acquisition module 402, a fault mode matching module 403, a weight adjustment module 404, and a risk calculation module 405.

[0103] The pre-data acquisition module 401 is used to acquire the fault mode rule base and multi-dimensional dynamic evaluation matrix of the instrumentation and control system. The fault mode rule base includes multiple fault modes and the fault code corresponding to each fault mode. The multi-dimensional dynamic evaluation matrix includes the level scores of multiple evaluation dimensions for each fault mode and the initial weight of each evaluation dimension.

[0104] In an optional implementation, the pre-data acquisition module 401 first associates fault modes with equipment attributes of the instrumentation and control system based on the instrumentation and control system structure tree. Utilizing the engineering configuration information of the distributed control system, it automatically parses and extracts fault rule base information and assigns a unique fault code to each fault mode. Furthermore, based on the equipment safety classification and real-time operating conditions of the instrumentation and control system, it dynamically adjusts the weights of multi-dimensional indicators for fault mode evaluation, integrating multiple algorithms from the aforementioned methods such as risk matrix, Bayesian network, and fuzzy evaluation to achieve efficient and accurate calculation and ranking of fault priorities in the instrumentation and control system.

[0105] The fault data acquisition module 402 is used to acquire the diagnostic code of the current fault, the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating status when a fault occurs in the instrumentation and control system.

[0106] The fault mode matching module 403 is used to match the diagnostic code with the fault codes in the fault mode rule base to obtain the current fault mode. Specifically, the fault mode matching module 403 establishes a mapping relationship to accurately associate the fault descriptions in the instrumentation and control system equipment fault diagnosis manual with the unique fault codes in the fault mode rule base, provides preventive maintenance strategy suggestions, and automatically generates maintenance work orders and operation and maintenance records.

[0107] The weight adjustment module 404 is used to adjust the initial weight of each evaluation dimension according to the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating conditions, so as to obtain the dynamic weight of each evaluation dimension.

[0108] The risk calculation module 405 is used to determine and display the risk priority of the current fault based on the dynamic weight of each assessment dimension and the level score of each assessment dimension of the current fault mode.

[0109] In a specific application example, the instrumentation and control system fault analysis device interacts with maintenance personnel through a visual decision-making terminal interface. This interface integrates multi-dimensional data visualization capabilities, displaying the risk priority of current faults in real time through an intuitive graphical interface. Maintenance personnel can dynamically filter and focus on high-priority faults based on equipment safety classification and real-time operating conditions. The device supports interactive operation; for example, clicking on a fault node expands detailed information (including fault code, fault severity, fault probability, fault detectability difficulty, and fault repair difficulty), and links to the instrumentation and control system fault troubleshooting manual, providing a one-click jump to maintenance recommendations. The visual decision-making terminal interface supports historical data review and trend analysis, assisting maintenance personnel in developing preventative maintenance strategies.

[0110] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores a fault mode rule base and a multi-dimensional dynamic evaluation matrix for the instrumentation and control system. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a fault analysis method for the instrumentation and control system.

[0111] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] In one exemplary embodiment, a computer-readable storage medium is provided, storing a computer program that, when executed by a processor, implements the steps described in the above-described method embodiments. The computer-readable storage medium serves as the core carrier, supporting a complete implementation system for improving the operational efficiency of instrumentation and control systems. The medium stores the algorithmic logic of fault analysis methods for instrumentation and control systems, multi-dimensional evaluation models (such as risk matrices and Bayesian networks) and their parameter configurations, and integrates executable program code, enabling automated completion of key steps such as fault mode analysis, dynamic weight adjustment, and priority calculation. This computer-readable storage medium supports multi-platform deployment, ensuring data security and efficient access, and providing underlying support for intelligent operation and maintenance of instrumentation and control systems.

[0113] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0114] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0115] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0116] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0117] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0118] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for fault analysis of an instrumentation and control system, characterized in that, The method includes: The system acquires a fault mode rule base and a multi-dimensional dynamic evaluation matrix for the instrumentation and control system. The fault mode rule base includes multiple fault modes and fault codes corresponding to each fault mode. The multi-dimensional dynamic evaluation matrix includes the level scores of multiple evaluation dimensions for each fault mode and the initial weight of each evaluation dimension. When a fault occurs in the instrumentation and control system, obtain the diagnostic code of the current fault, the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating status; The diagnostic code is matched with the fault codes in the fault mode rule base to obtain the current fault mode; Based on the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating conditions, the initial weight of each evaluation dimension is adjusted to obtain the dynamic weight of each evaluation dimension. Based on the dynamic weight of each assessment dimension and the rating score of each assessment dimension of the current failure mode, the risk priority of the current failure is determined and displayed.

2. The fault analysis method for instrumentation and control systems according to claim 1, characterized in that, The fault mode rule base is pre-constructed based on the instrumentation and control system's structure tree, using fault mode impact and hazard analysis methods and diagnostic analysis methods.

3. The fault analysis method for instrumentation and control systems according to claim 1, characterized in that, The assessment dimensions are fault severity, fault occurrence probability, fault detectability difficulty, and fault repair difficulty. The severity level score of the fault is determined in advance using the risk matrix method; The probability score of the fault occurrence is determined in advance using the Bayesian network method; The level score of the detectability difficulty of the fault is determined in advance using the fuzzy comprehensive evaluation method; The difficulty level of the fault repair is determined in advance using the entropy weight method.

4. The fault analysis method for instrumentation and control systems according to claim 1, characterized in that, The initial weights for each evaluation dimension are determined in advance using the analytic hierarchy process.

5. The fault analysis method for instrumentation and control systems according to claim 1, characterized in that, Based on the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating conditions, the initial weights of each evaluation dimension are adjusted to obtain the dynamic weights of each evaluation dimension, specifically including: Determine the safety level of the faulty equipment based on the equipment safety classification of the instrumentation and control system corresponding to the current fault; The adjustment factor is determined based on the safety level and real-time operating conditions of the currently faulty equipment; The initial weights of each evaluation dimension are adjusted according to the adjustment factor and then normalized to obtain the dynamic weights of each evaluation dimension.

6. The fault analysis method for instrumentation and control systems according to claim 1, characterized in that, The number of current faults is one or more; when the number of current faults is multiple, the instrumentation and control system fault analysis method further includes: The priorities of multiple current faults are sorted according to the risk priority number of each current fault.

7. A fault analysis device for an instrumentation and control system, characterized in that, The device includes: The pre-data acquisition module is used to acquire the fault mode rule base and multi-dimensional dynamic evaluation matrix of the instrumentation and control system. The fault mode rule base includes multiple fault modes and the fault code corresponding to each fault mode. The multi-dimensional dynamic evaluation matrix includes the level scores of multiple evaluation dimensions of each fault mode and the initial weight of each evaluation dimension. The fault data acquisition module is used to acquire the diagnostic code of the current fault, the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating status when a fault occurs in the instrumentation and control system. The fault mode matching module is used to match the diagnostic code with the fault codes in the fault mode rule base to obtain the current fault mode; The weight adjustment module is used to adjust the initial weight of each evaluation dimension based on the faulty equipment corresponding to the current fault, the equipment safety classification of the instrumentation and control system, and the real-time operating conditions, so as to obtain the dynamic weight of each evaluation dimension. The risk calculation module is used to determine and display the risk priority of the current failure based on the dynamic weight of each assessment dimension and the level score of each assessment dimension of the current failure mode.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the instrumentation and control system fault analysis method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the instrumentation and control system fault analysis method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the instrumentation and control system fault analysis method according to any one of claims 1-6.

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