Method and device for evaluating reliability of engineering production system

Abstract

The invention relates to the technical field of reliability engineering, in particular to a reliability evaluation method and evaluation device for an engineering production system, and the method comprises the steps: obtaining a reliability block diagram of the engineering production system, determining the metadata of each function block, generating a reliability function network, marking a typical reliability structure unit, and obtaining a reliability function network; the method comprises the following steps: determining a function subnet, configuring a time domain strategy to generate a self-adaptive time grid, solving the equivalent reliability of the function subnet at a self-adaptive time point of the self-adaptive time grid to obtain a reliability sampling sequence of the engineering production system, and generating a reliability evaluation result. Therefore, the problems that in the related technology, due to the fact that semantic information corresponding to a reliability block diagram depends on manual disassembly, and a reliability evaluation result is calculated through fixed step size discrete approximation, missed judgment and misjudged are likely to occur due to complex topology, the calculation precision is poor in a high-risk time period, the calculation efficiency is low in a stable time period, and engineering decision is difficult to support are solved.

Description

Technical Field

[0001] This application relates to the field of reliability engineering technology, and in particular to a reliability assessment method and assessment device for an engineering production system. Background Technology

[0002] In the reliability analysis of complex equipment such as aero-engines and large industrial production lines, reliability block diagrams are the mainstream tool. They can intuitively express the reliability dependencies of system components through logic such as series, parallel, and k / n voting (k-out-of-n voting, taking k votes from n), which facilitates communication between engineering teams in design, test flight, operation and maintenance.

[0003] In related technologies, technical personnel need to manually decompose the reliability block diagram into a series of formal descriptions, such as identifying which parts are pure series subnets (all components must work), which parts are parallel subnets (the whole network can work if any branch works), and which parts are k / n voting subnets (at least k of the n channels need to be normal). Then, these structures are mapped into corresponding reliability function formulas, which are then input into the backend calculation module for solving to obtain reliability assessment results.

[0004] However, the relevant technologies rely on manually disassembling the semantic information corresponding to the reliability block diagram, which can easily lead to omissions and misjudgments due to complex topologies, resulting in calculation biases. In addition, the reliability assessment results are calculated by discrete approximation with a fixed step size, which results in poor calculation accuracy during high-risk periods (such as when the lifespan of critical components is close to the limit, an excessive fixed step size will underestimate the risk) and low calculation efficiency during stable periods, making it difficult to support engineering decisions. These issues urgently need to be addressed. Summary of the Invention

[0005] This application provides a reliability assessment method and device for engineering production systems to address the problems of related technologies that rely on manually disassembling the semantic information corresponding to the reliability block diagram and using fixed-step discrete approximation to calculate the reliability assessment results. These methods are prone to omissions and misjudgments due to complex topologies, resulting in poor calculation accuracy during high-risk periods and low calculation efficiency during stable periods, making it difficult to support engineering decision-making.

[0006] The first aspect of this application provides a reliability assessment method for an engineering production system, comprising the following steps: obtaining a reliability block diagram of the engineering production system and determining the metadata of each functional block in the reliability block diagram to generate a reliability functional network; labeling at least one typical reliability structural unit of the reliability functional network to determine at least one functional subnet and configuring a time domain strategy; generating an adaptive time grid based on the time domain strategy, and solving for the equivalent reliability of the functional subnet at the adaptive time point of the adaptive time grid to obtain a reliability sampling sequence of the engineering production system, and generating a reliability assessment result of the engineering production system.

[0007] Based on the above technical means, the embodiments of this application transform the reliability block diagram into a reliability functional network and decompose it into functional subnets, replacing manual decomposition, avoiding missed or misjudged cases under complex topologies, and reducing computational complexity. In addition, by generating an adaptive time grid, the sampling step size can be dynamically adjusted, ensuring the computational accuracy during high-risk periods and improving the computational efficiency during stable periods, balancing accuracy and computing power consumption. Furthermore, by outputting multi-dimensional reliability assessment results, it provides quantitative and reliable support for practical decisions such as maintenance and spare parts reserves of engineering production systems.

[0008] Optionally, in one embodiment of this application, generating the reliability assessment result of the engineering production system includes: performing smooth interpolation reconstruction on the reliability sampling sequence to obtain a continuous reliability curve; based on a preset error upper limit and convergence threshold, outputting at least one of the following parameters of the engineering production system: system reliability, mean time between failures (MTBF), failure rate, failure probability density, critical component importance, error range, and convergence report; and generating the reliability assessment result based on at least one of the following parameters: system reliability, MTBF, failure rate, failure probability density, critical component importance, error range, and convergence report.

[0009] Based on the above technical means, the embodiments of this application transform discrete sampling into a continuous reliability curve through smooth interpolation, intuitively displaying the trend of system reliability changing over time, and using upper limit of error and convergence threshold to constrain the output indicators, ensuring the accuracy and credibility of the results. At the same time, it outputs multi-dimensional reliability assessment results, which not only cover the quantitative description of system reliability, but also accurately locate weak links and clarify the credibility range of the results, fully supporting the actual decision-making needs of engineering production systems such as preventive maintenance, spare parts reserve, and risk control.

[0010] Optionally, in one embodiment of this application, generating the adaptive time grid based on the time domain strategy includes: establishing an initial reliability curve based on the time domain strategy and calculating the approximate curvature of the time interval to be analyzed; and determining, based on the approximate curvature, whether to add sampling points to the time interval to be analyzed in order to generate the adaptive time grid.

[0011] Based on the above technical means, the embodiments of this application can determine whether to increase sampling points in the time interval to be analyzed based on the approximate curvature. This can increase the density of sampling points in the time interval where the reliability changes too sharply, thereby improving the accuracy of reliability assessment. At the same time, it can retain a small number of sampling points in the interval where the reliability changes gently, thereby avoiding computational redundancy and improving the efficiency of reliability assessment.

[0012] Optionally, in one embodiment of this application, the formula for calculating the approximate curvature may be, but is not limited to: , in, For the approximate curvature, This refers to the starting time point of the time interval to be analyzed. This refers to the end point of the time interval to be analyzed. This refers to the midpoint of the time interval to be analyzed. The reliability of the initial reliability curve is given.

[0013] Based on the above technical means, the embodiments of this application use a formula for calculating the approximate curvature in the form of a quadratic difference to avoid performing derivative operations on the initial reliability curve, thereby reducing the computational power consumption of curvature calculation. At the same time, it can quantify the degree of drastic change in reliability within the time interval to be analyzed, providing a quantitative basis for whether to add sampling points in the future, and effectively supporting the optimization of the adaptive time grid between evaluation accuracy and computational efficiency.

[0014] Optionally, in one embodiment of this application, the formula for calculating the mean time between failures (MTBF) may be, but is not limited to: , in, To perform numerical integration over densely sampled areas, To perform numerical integration over the sparse sampling region, The time interval between two sampling points. The continuous reliability curve at time point The value of , The continuous reliability curve at time point The value of , The continuous reliability curve at time point The value of , The preset task limit, The continuous reliability curve function is... The mean time between failures (MTBF) is the average time between failures.

[0015] Based on the above technical means, the embodiments of this application perform numerical integration and summation on the dense sampling region and the sparse sampling region respectively. This ensures the fitting accuracy in the dense sampling region and makes up for the lack of sampling points in the sparse sampling region, thereby improving the accuracy and reliability of the results. At the same time, it reduces the consumption of computing power and improves the efficiency of reliability assessment.

[0016] A second aspect of this application provides a reliability assessment apparatus for an engineering production system, comprising: an acquisition module for acquiring a reliability block diagram of the engineering production system and determining the metadata of each functional block in the reliability block diagram to generate a reliability functional network; an annotation module for annotating at least one typical reliability structural unit of the reliability functional network to determine at least one functional subnet and configuring a time domain strategy; and an assessment module for generating an adaptive time grid based on the time domain strategy and solving for the equivalent reliability of the functional subnet at adaptive time points of the adaptive time grid to obtain a reliability sampling sequence of the engineering production system and generate a reliability assessment result of the engineering production system.

[0017] Based on the above technical means, the embodiments of this application transform the reliability block diagram into a reliability functional network and decompose it into functional subnets, replacing manual decomposition, avoiding missed or misjudged cases under complex topologies, and reducing computational complexity. In addition, by generating an adaptive time grid, the sampling step size can be dynamically adjusted, ensuring the computational accuracy during high-risk periods and improving the computational efficiency during stable periods, balancing accuracy and computing power consumption. Furthermore, by outputting multi-dimensional reliability assessment results, it provides quantitative and reliable support for practical decisions such as maintenance and spare parts reserves of engineering production systems.

[0018] Optionally, in one embodiment of this application, the evaluation module includes: a first generation unit, configured to perform smooth interpolation reconstruction on the system curve corresponding to the reliability sampling sequence to obtain a continuous reliability curve; an output unit, configured to output at least one of the following based on the continuous reliability curve: system reliability, mean time between failures (MTBF), failure rate, failure probability density, critical component importance, error range, and convergence report of the engineering production system, according to a preset error upper limit and convergence threshold; and a second generation unit, configured to generate the reliability evaluation result based on at least one of the following: system reliability, MTBF, failure rate, failure probability density, critical component importance, error range, and convergence report.

[0019] Based on the above technical means, the embodiments of this application transform discrete sampling into a continuous reliability curve through smooth interpolation, intuitively displaying the trend of system reliability changing over time, and using upper limit of error and convergence threshold to constrain the output indicators, ensuring the accuracy and credibility of the results. At the same time, it outputs multi-dimensional reliability assessment results, which not only cover the quantitative description of system reliability, but also accurately locate weak links and clarify the credibility range of the results, fully supporting the actual decision-making needs of engineering production systems such as preventive maintenance, spare parts reserve, and risk control.

[0020] Optionally, in one embodiment of this application, the evaluation module includes: a setup unit, configured to establish an initial reliability curve based on the time domain strategy and calculate the approximate curvature of the time interval to be analyzed; and a third generation unit, configured to determine, based on the approximate curvature, whether to add sampling points to the time interval to be analyzed in order to generate the adaptive time grid.

[0021] Based on the above technical means, the embodiments of this application can determine whether to increase sampling points in the time interval to be analyzed based on the approximate curvature. This can increase the density of sampling points in the time interval where the reliability changes too sharply, thereby improving the accuracy of reliability assessment. At the same time, it can retain a small number of sampling points in the interval where the reliability changes gently, thereby avoiding computational redundancy and improving the efficiency of reliability assessment.

[0022] Optionally, in one embodiment of this application, the formula for calculating the approximate curvature may be, but is not limited to: , in, For the approximate curvature, This refers to the starting time point of the time interval to be analyzed. This refers to the end point of the time interval to be analyzed. This refers to the midpoint of the time interval to be analyzed. The reliability of the initial reliability curve is given.

[0023] Based on the above technical means, the embodiments of this application use a formula for calculating the approximate curvature in the form of a quadratic difference to avoid performing derivative operations on the initial reliability curve, thereby reducing the computational power consumption of curvature calculation. At the same time, it can quantify the degree of drastic change in reliability within the time interval to be analyzed, providing a quantitative basis for whether to add sampling points in the future, and effectively supporting the optimization of the adaptive time grid between evaluation accuracy and computational efficiency.

[0024] Optionally, in one embodiment of this application, the formula for calculating the mean time between failures (MTBF) may be, but is not limited to: , in, To perform numerical integration over densely sampled areas, To perform numerical integration over the sparse sampling region, The time interval between two sampling points. The continuous reliability curve at time point The value of , The continuous reliability curve at time point The value of , The continuous reliability curve at time point The value of , The preset task limit, The continuous reliability curve function is... The mean time between failures (MTBF) is the average time between failures.

[0025] Based on the above technical means, the embodiments of this application perform numerical integration and summation on the dense sampling region and the sparse sampling region respectively. This ensures the fitting accuracy in the dense sampling region and makes up for the lack of sampling points in the sparse sampling region, thereby improving the accuracy and reliability of the results. At the same time, it reduces the consumption of computing power and improves the efficiency of reliability assessment.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the reliability assessment method for an engineering production system as described in the above embodiments.

[0027] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the reliability assessment method for the engineering production system described above.

[0028] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the reliability assessment method for the engineering production system described above.

[0029] This application's embodiments transform the reliability block diagram into a reliability functional network and decompose it into functional subnets, replacing manual decomposition. This avoids missed or misjudged cases under complex topologies and reduces computational complexity. Furthermore, by generating an adaptive time grid, the sampling step size can be dynamically adjusted, ensuring computational accuracy during high-risk periods while improving computational efficiency during stable periods, balancing accuracy and computational power consumption. In addition, by outputting multi-dimensional reliability assessment results, it provides quantitative and reliable support for practical decisions such as maintenance and spare parts reserves in engineering production systems. Therefore, it solves the problems of related technologies that rely on manually decomposing the semantic information corresponding to the reliability block diagram and using fixed-step discrete approximation to calculate reliability assessment results, which easily leads to missed or misjudged cases due to complex topologies, poor computational accuracy during high-risk periods, and low computational efficiency during stable periods, making it difficult to support engineering decisions.

[0030] Additional aspects and advantages of this application 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 this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1This is a flowchart of a reliability assessment method for an engineering production system provided according to an embodiment of this application; Figure 2 This is a schematic diagram illustrating topology resolution and functional subnet identification according to an embodiment of this application; Figure 3 This is a schematic diagram of adaptive time grid generation according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the statistical relative error between analytical results and numerical flow results for comparing functional subnets with purely series and purely parallel structures, according to an embodiment of this application. Figure 5 This is a schematic diagram illustrating error control and convergence determination according to an embodiment of this application; Figure 6 This is a block diagram of a reliability assessment device for an engineering production system provided according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 60 - Reliability assessment device for engineering production systems; 100 - Acquisition module; 200 - Labeling module; 300 - Assessment module; 701 - Memory; 702 - Processor; 703 - Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The reliability assessment method and apparatus for an engineering production system according to embodiments of this application are described below with reference to the accompanying drawings. Addressing the problems mentioned in the background art, which rely on manually disassembling the semantic information corresponding to the reliability block diagram and using fixed-step discrete approximation to calculate reliability assessment results, leading to omissions and misjudgments due to complex topologies, poor calculation accuracy during high-risk periods, and low calculation efficiency during stable periods, making it difficult to support engineering decision-making, this application provides a reliability assessment method for an engineering production system. In this method, the reliability block diagram is transformed into a reliability functional network and then divided into functional subnets, replacing manual disassembly. This avoids omissions and misjudgments under complex topologies and reduces computational complexity. Furthermore, by generating an adaptive time grid, the sampling step size can be dynamically adjusted, ensuring calculation accuracy during high-risk periods while improving calculation efficiency during stable periods, balancing accuracy and computational power consumption. Additionally, by outputting multi-dimensional reliability assessment results, quantitative and reliable support is provided for practical decisions such as maintenance and spare parts reserves in engineering production systems. This solves the problem that related technologies rely on manually disassembling the semantic information corresponding to the reliability block diagram and using fixed-step discrete approximation to calculate the reliability assessment results, which can easily lead to missed or incorrect judgments due to complex topologies, poor calculation accuracy during high-risk periods, and low calculation efficiency during stable periods, making it difficult to support engineering decision-making.

[0035] Specifically, Figure 1 This is a flowchart of a reliability assessment method for an engineering production system provided according to an embodiment of this application.

[0036] like Figure 1 As shown, the reliability assessment method for this engineering production system includes the following steps: In step S101, a reliability block diagram of the engineering production system is obtained, and the metadata of each functional block in the reliability block diagram is determined to generate a reliability functional network.

[0037] In one embodiment of this application, an engineering production system can be understood as a complex system in an industrial setting, which is composed of functional units such as processing equipment, transmission devices, and control components according to a predetermined production process, and is used to stably complete production tasks (such as assembly line processing).

[0038] A reliability block diagram can be understood as a graphical tool used by those skilled in the art to intuitively express the reliability logic of an engineering production system. It typically uses boxes to represent functional blocks and lines to represent the dependencies between functional blocks.

[0039] A reliability functional network can be understood as a structured digital model that is transformed from a reliability block diagram and can be recognized and calculated by a computer. It is used to transform the reliability block diagram from a merely visualized drawing into a digital model that can be automatically solved, supporting subsequent processes such as subnet splitting and reliability calculation.

[0040] In practical implementation, those skilled in the art can first draw a reliability block diagram of the engineering production system and enter or bind metadata (such as distribution type parameters and empirical reliability curves) for each functional block. Furthermore, embodiments of this application can transform the reliability block diagram into a reliability functional network. The reliability functional network consists of a set of nodes (a set of functional blocks) with attribute information and a set of directed edges (a set of signal flows), and adopts a unified structured format (such as JSON / Protobuf) to ensure subsequent computational processing.

[0041] It should be noted that a node can be understood as a single functional block, which can be a basic component or an independent subsystem. Each node has its own attributes, such as the attribute of a sensor node, which is that its lifetime follows a Weibull distribution.

[0042] For example, this application provides a component / subsystem (i.e., function block, node) component library. When a person skilled in the art drags and drops a component / subsystem onto the canvas, the "Lifetime-Reliability" configuration page is simultaneously displayed in the right-hand property panel. Fields use engineering terminology (e.g., ...). FC , FH Calendar lifespan, etc.) and statistical caliber ( , , It features a dual-scale display and allows for unit switching.

[0043] Specifically, for distributed nodes, the attribute panel has built-in templates such as exponential, Weibull, and log-normal. Those skilled in the art only need to fill in a few parameters after selecting the distributed type, and the embodiments of this application will provide a real-time preview within a small window. , The curve (to avoid "blindly filling in") provides common-sense engineering tips for the recommended parameter range.

[0044] For experience-based nodes, those skilled in the art can import discrete points from experiments or historical evaluations. In this embodiment, data cleaning (duplicate removal, sorting, and monotonicity checking) is performed in real time. When local non-monotonicity occurs, the system prompts the user to select "monotonic spline (PCHIP)" reconstruction to eliminate ringing.

[0045] For subsystem-type nodes, the embodiments of this application can directly bind the equivalent curves obtained from the upstream solution. Alternatively, it can be a standardized curve file exported from an external system. Furthermore, to ensure traceability, embodiments of this application may record the source hash and version number.

[0046] All nodes display their data status (parameters complete / verification warning / to be supplemented) on the canvas using a logo / color bar. When node attributes change, this embodiment sends a "to be recalculated" flag to the downstream link, but does not immediately recalculate to avoid frequent jitter.

[0047] Furthermore, after those skilled in the art click "Save / Export", the reliability block diagram is losslessly converted into a reliability functional network, which can be represented as a directed graph with attributes. Among them, nodes It is both a square on the canvas and a "queryable reliability object": carrying a type marker (distribution / experience / subsystem), parameters or spline control points, and a logical role (normal, voter); edges Expresses functional dependencies / signal flow, and by default does not carry probabilistic semantics (to avoid ambiguity). "Enable conditions" are only defined on edges when bypass / degradation modes are required for modeling.

[0048] It should be noted that, in the embodiments of this application, topology normalization (node ​​ID standardization, level labeling, and removal of redundant edges) is performed during export, and canvas coordinates that are not related to calculation are stripped off to ensure that the backend receives a computable model rather than an image.

[0049] In step S102, at least one typical reliability structural unit of the reliability functional network is labeled to determine at least one functional subnet, and a time domain strategy is configured.

[0050] In one embodiment of this application, a typical reliability structural unit can be understood as a basic structural module with clear and standardized reliability logic in the reliability functional network of an engineering production system. It may include, but is not limited to, series, parallel, k / n voting, bypass branch, etc., and is used to decompose the complex system reliability logic into standardized basic units, providing a unified structural template for the identification and calculation of subsequent functional subnets.

[0051] A functional subnet can be understood as a structural unit whose reliability can be solved independently, which is separated from the reliability functional network of an engineering production system. A functional subnet contains at least one node, and a functional subnet can serve as an equivalent node of a higher-level subnet (e.g., a 2 / 3 voting subnet can be encapsulated as a node and participate in a higher-level serial subnet).

[0052] In actual implementation, the embodiments of this application can perform topology traversal and pattern recognition on the reliability functional network, and automatically label typical reliability structural units: series, parallel, k / n voting, bypass branch, cascaded subsystem, etc., thereby decomposing the complex network into several functional subnets that can be solved independently, and retaining the dependencies corresponding to the functional subnets.

[0053] Specifically, for a serial subnet, the determination rule is: on a path (excluding the beginning and end), the in-degree and out-degree of all intermediate nodes are 1.

[0054] The formula for calculating the equivalent reliability of a series subnet can be, but is not limited to, the following: , in, Let be the set of all intermediate nodes in the serial subnet. At the current time point, For the first in the serial subnet Each node at a given time point Reliability, To connect subnets at time points The equivalent reliability.

[0055] The formula for calculating the equivalent failure rate of a series subnet can be, but is not limited to, the following: , in, For the first in the serial subnet Each node at a given time point failure rate, To connect subnets at time points The equivalent failure rate.

[0056] For parallel subnets, the determination rule is: when several paths share the same source (same starting node) and the same sink (same ending node).

[0057] The formula for calculating the equivalent reliability of parallel subnets can be, but is not limited to, the following: , in, The set of all nodes in the parallel subnet. For the first in the parallel subnet Each node at a given time point Reliability, For the first in the parallel subnet Each node at a given time point The equivalent reliability.

[0058] For k / n voting subnets, the embodiments of this application address the use of ( k , n The semantic nodes are updated using DP (Dynamic Programming) in a rolling manner. There are exactly 1 branch The probability of "available". To obtain the equivalent reliability, its expression can be, but is not limited to, as follows: , in, For the first Branches at time points Reliability, As a main branch, The minimum number of branches available for normal operation. For the k / n voting subnet at time point The equivalent reliability.

[0059] It should be noted that when using dynamic programming (DP) to calculate the equivalent reliability of the k / n voting subnet in the embodiments of this application, the complexity is O(n). This is used to avoid combinatorial explosion. For extremely large-scale redundant k / n voting subnets, the embodiments of this application retain the approximate calculation path of MC (Monte Carlo) sampling.

[0060] Furthermore, nodes from different sources may include, but are not limited to, basic component nodes (given distribution or empirical curves), equivalent subsystem nodes (reliability curves obtained from the previous level), and nodes with incomplete data (using default priors or interpolation fitting). To unify the use of nodes from different sources, embodiments of this application can perform semantic unification on the nodes.

[0061] In some cases, embodiments of this application can encapsulate nodes from different sources into queryable functions. Its expression can be, but is not limited to, as: = , in, For nodes Failure rate (where lifetime follows an exponential distribution), For nodes Characteristic lifetime (which follows a Weibull distribution) For nodes The shape parameters of (lifetime follows a Weibull distribution) For nodes ( Nodes at historical points in time Reliability The set, This is a cubic spline interpolation operation.

[0062] When the node provides time-varying hazard rate At that time, the formula for the query function can be, but is not limited to, the following: , in, For nodes At the point of time The time-varying hazard rate function.

[0063] Furthermore, the integral of the time-varying hazard rate function is approximated using piecewise quadrature. The cubic spline of the empirical curve will select natural or damping boundaries, and monotonic constraints will be enabled when necessary to prevent unreasonable fluctuations (ringing) in the curve. It caches data in the form of vectorized functions, supports batch input of time points, and quickly returns reliability results.

[0064] For example, such as Figure 2 As shown in the figure, this application embodiment is for an automotive assembly line control system, and briefly introduces how to output functional subnets and corresponding dependencies based on the reliability functional network.

[0065] S201: Input reliability function network.

[0066] In particular, the embodiments of this application are aimed at automotive assembly line control systems, which can generate a reliable functional network that includes nodes (power modules, programmable logic controllers, position sensor groups, actuator groups, emergency stop modules) and edges (control signal flows between nodes).

[0067] S202: Topological sorting.

[0068] In this embodiment of the application, the nodes in the automotive assembly line control system are sorted topologically according to the control flow sequence. The sorting result can be a power module, a programmable logic controller, a position sensor group, and an actuator group. In addition, the emergency stop module is an independent branch, which is connected between the programmable logic controller and the actuator group.

[0069] S203: Serial subnet identification.

[0070] In this application embodiment, for automotive assembly line control systems, the path in the reliability functional network where the in-degree and out-degree of the intermediate node are both 1 can be identified as a serial subnet, and the identification result can be a power module or a programmable logic controller.

[0071] S204: Parallel subnet identification.

[0072] In this embodiment of the application, for the automotive assembly line control system, the path that shares the same source and sink in the reliability function network is identified as a parallel subnet, and the identification result can be position sensor 1 and position sensor 2.

[0073] S205: Bypass / Degradation Path Labeling.

[0074] In this embodiment of the application, for automotive assembly line control systems, the corresponding tags are added to branches in the reliability function network that do not participate in the main process but only access specific scenarios, and the identification result can be an emergency stop module.

[0075] S206: Subnet encapsulation and encoding.

[0076] In this application embodiment, for the automotive assembly line control system, the identified typical reliability structural units are encapsulated into functional subnets and assigned corresponding codes: the code for the series subnet can be SI1 (power module, programmable logic controller), the code for the parallel subnet can be SI2 (position sensor 1, position sensor 2), and the code for the bypass / degradation path can be KI1.

[0077] S207: Generate functional subnet dependencies.

[0078] In this application embodiment, the dependencies between upstream and downstream functional subnets can be sorted out, such as SI1 being the upstream of SI2.

[0079] S208: Output functional subnets and their corresponding dependencies.

[0080] In step S103, an adaptive time grid is generated based on the time domain strategy, and the equivalent reliability of the functional subnet is solved at the adaptive time point of the adaptive time grid to obtain the reliability sampling sequence of the engineering production system and generate the reliability assessment result of the engineering production system.

[0081] In the embodiments of this application, the time domain strategy can be understood as a set of configurations formulated by those skilled in the art from the time dimension, used to provide time boundaries and accuracy standards for adaptive time grid generation.

[0082] An adaptive time grid can be understood as a set of time points that can be adaptively adjusted according to the intensity of changes in reliability.

[0083] An adaptive time point can be understood as a single time point in an adaptive time grid. Each time point corresponds to a calculation of system reliability and is used to construct a reliability sampling sequence.

[0084] Equivalent reliability can be understood as the reliability calculated at a certain point in time after treating the functional subnet as an "equivalent node," which is used to reduce the complexity of system reliability calculation.

[0085] The reliability sampling sequence can be understood as a set of discrete system reliability values ​​calculated sequentially according to the order of each time point in the adaptive time grid, with each element corresponding to a time point and the system reliability at that time point.

[0086] Reliability assessment results can be understood as a multi-dimensional information set that integrates the entire process of calculation data and meets the needs of engineering decision-making. This may include, but is not limited to, system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report.

[0087] Optionally, in one embodiment of this application, generating an adaptive time grid based on a time domain strategy includes: establishing an initial reliability curve based on the time domain strategy and calculating the approximate curvature of the time interval to be analyzed; and determining, based on the approximate curvature, whether to add sampling points in the time interval to be analyzed in order to generate an adaptive time grid.

[0088] In the embodiments of this application, the initial reliability curve can be understood as a continuous approximate curve used to describe the trend of system reliability changing over time, calculated based on the equivalent reliability of the functional subnet according to the time domain strategy and fitted by interpolation according to the dependency relationship of the functional subnet.

[0089] In actual implementation, those skilled in the art can first configure the time-domain strategy, which may include, but is not limited to, configuring the task limit. With precision parameters, where precision parameters may include, but are not limited to, curvature thresholds. Convergence threshold Reliability truncation (like (i.e., the task is considered complete), numerical stability constant .

[0090] Furthermore, embodiments of this application can construct an initialization time grid based on the configured time domain strategy. ,in, It can contain , With each node's characteristic time points (mean lifetime, quantile, and inflection point of the empirical curve).

[0091] Furthermore, embodiments of this application may be implemented in... First, obtain a rough initial reliability curve. (For example, by calling subnet calculation formulas such as series, parallel, and k / n voting, the equivalent reliability at the initial time point is solved to obtain the discrete reliability data point set, and then the initial reliability curve is fitted by cubic spline interpolation.) Then, any time interval to be analyzed is selected. Calculate the corresponding approximate curvature .

[0092] Furthermore, those skilled in the art can define a certain curvature threshold. This is used in embodiments of the application to determine whether to add sampling points (i.e., adaptive time points) within the time interval to be analyzed: If the reliability change is too steep within the time interval to be analyzed, the analysis can be performed at the midpoint of the time interval. Insert a new sampling point and recalculate the system reliability corresponding to that point; This indicates that the reliability changes smoothly within the time interval to be analyzed, and no additional sampling points are needed.

[0093] Furthermore, the embodiments of this application can repeatedly calculate the approximate curvature, determine whether to add sampling points, and adjust the sampling point operation process until the approximate curvature of all time intervals to be analyzed satisfies the requirements. Thus, an adaptive time grid is obtained.

[0094] It should be noted that, in order to avoid computational redundancy caused by too many sampling points, the maximum number of iterations can be set in this embodiment of the application to prevent the time interval to be analyzed from being infinitely divided. At the same time, global convergence can be verified based on the rate of change of key indicators (such as reliability).

[0095] The embodiments of this application can determine whether to increase sampling points in the time interval to be analyzed based on the approximate curvature. It can increase the density of sampling points in the time interval where the reliability changes too sharply, thereby improving the accuracy of reliability assessment. At the same time, it can retain a small number of sampling points in the interval where the reliability changes gently, thereby avoiding computational redundancy and improving the efficiency of reliability assessment.

[0096] Optionally, in one embodiment of this application, the formula for calculating the approximate curvature may be, but is not limited to: , in, For approximate curvature, This is the starting point of the time interval to be analyzed. This is the end point of the time interval to be analyzed. This represents the midpoint of the time interval to be analyzed. This represents the reliability of the initial reliability curve.

[0097] Furthermore, the formula for calculating the midpoint of the time interval to be analyzed can be, but is not limited to, the following: .

[0098] In actual implementation, the embodiments of this application can utilize the reliability of the initial reliability curves corresponding to the start time point, middle time point and end time point of the time interval to be analyzed to construct a quadratic difference form (the curvature of the continuous curve is positively correlated with the second derivative, and the quadratic difference is an approximation of the second derivative), and combine it with the square of the time interval to be analyzed for normalization processing, which is used to characterize the curvature of the reliability continuous curve within the interval.

[0099] This application embodiment uses a quadratic difference form of approximate curvature calculation formula to avoid performing derivative operations on the initial reliability curve, thereby reducing the computational cost of curvature calculation. At the same time, it can quantify the drastic degree of reliability change within the time interval to be analyzed, providing a quantitative basis for whether to add sampling points in the future, and effectively supporting the optimization of the adaptive time grid between evaluation accuracy and computational efficiency.

[0100] Based on the description of other embodiments, exemplarily, such as Figure 3 As shown in the embodiments of this application, this application briefly describes how to generate an adaptive time grid based on an initialized time grid.

[0101] S301: Construct the initialization time grid.

[0102] The initial time grid can be determined based on the upper limit of the task analysis time domain and the node feature time points. For example, if the upper limit of the task analysis time domain can be set to 2000 and the node feature time points can be set to 1000, then the initial time grid can be {0, 1000, 2000}.

[0103] S302: Obtain the initial reliability curve or an approximate reliability representation.

[0104] In this embodiment, the calculation formulas of subnets such as series, parallel, and k / n voting can be called at each time point of the initialization time grid to solve the equivalent reliability at the corresponding time point, obtain the discrete reliability data point set, and construct the reliability curve / approximate representation by interpolation or fitting methods (such as piecewise linear interpolation, cubic spline interpolation, etc.) for subsequent curvature evaluation and point addition decision.

[0105] S303: Evaluate the approximate curvature of the time interval to be analyzed.

[0106] In this embodiment, the analysis interval determined by adjacent sampling points can be selected, such as the interval [1000, 2000], and the approximate curvature of the interval can be calculated based on the reliability curve / approximate representation (e.g., estimated by the second derivative or obtained by the difference between the interval endpoints and the midpoint). For example, the approximate curvature of the interval can be 1×10⁻⁶. -8 .

[0107] S304: Determine whether the approximate curvature exceeds the curvature threshold.

[0108] The curvature threshold can be determined by the task accuracy requirements or error budget configuration, and can be exemplarily set to 5×10. -9 When the approximate curvature of the interval [1000, 2000] exceeds the curvature threshold, at least one sampling point is added within the interval, for example, 1500 sampling points are added, thereby updating the time grid to {0, 1000, 1500, 2000}.

[0109] S305: Local recalculation of system reliability.

[0110] In this embodiment, reliability calculations can be performed on newly added sampling points and their corresponding affected subnets / nodes, and the combined results related to the interval can be updated to obtain the system reliability at the newly added time point (e.g., 1500).

[0111] S306: Judge whether the indicator has converged.

[0112] In this embodiment, the system reliability (or other target indicators, such as mean time between failures, failure rate, etc.) before and after the addition of points can be compared. When the rate of change is less than the convergence threshold, convergence is determined. For example, the convergence threshold can be 0.1%, and the current adaptive time grid is obtained accordingly. When the rate of change is greater than or equal to the convergence threshold, the process returns to S303 to continue the addition of points iteration.

[0113] Optionally, in one embodiment of this application, generating a reliability assessment result for an engineering production system includes: performing smooth interpolation reconstruction on the reliability sampling sequence to obtain a continuous reliability curve; based on a preset error upper limit and convergence threshold, outputting at least one of the following parameters for the engineering production system: system reliability, mean time between failures (MTBF), failure rate, failure probability density, importance of key components, error range, and convergence report; and generating a reliability assessment result based on at least one of the following parameters: system reliability, MTBF, failure rate, failure probability density, importance of key components, error range, and convergence report.

[0114] In actual implementation, the embodiments of this application can be based on an adaptive time grid, and for functional subnets, according to their topology (series, parallel, kn-voting, etc.), the corresponding reliability calculation rules are adopted to obtain the time point of the functional subnet in the adaptive time grid. Equivalent reliability .

[0115] Furthermore, embodiments of this application will... As an "equivalent node" (that is, treating the functional subnet as a single node with reliability attributes), it is passed on to its upstream parent network (that is, a higher-level functional subnet whose topology contains the functional subnet).

[0116] Furthermore, in this embodiment, the operation of repeatedly solving for the equivalent reliability and propagating the equivalent nodes upwards is repeated until the reliability sampling sequence of the engineering production system is obtained. .

[0117] It should be noted that, for the ultra-large k / n voting function subnet, the present application's embodiments employ a scenario-based calculation strategy because directly calculating the combination probability of "at least k channels being normal" would result in a combination explosion.

[0118] In this application embodiment, at regular time points, the MC sampling method can be used to estimate the equivalent reliability, and its expression can be, but is not limited to, as follows: , , in, The equivalent reliability of MC sampling estimation; The total number of samples is denoted by . The larger the value, the closer the estimated result is to the true value, but the computational cost is also greater. The sampling sequence number; For indicator functions, when the condition inside the parentheses is met... =1, when the condition inside the parentheses is not met. =0; For the first In the second sampling Each channel at a specific time point The work status; Channel number; This represents the minimum number of normal channels required for the subnet to function properly. This represents the total number of channels in the subnet. For the first Each channel at a specific time point The inherent reliability; It follows a Bernoulli distribution.

[0119] In the embodiments of this application, a DP / exact combination calculation method can be used at key time points (DP / exact combination calculation and MC sampling estimation can be coordinated through mixed error).

[0120] Furthermore, the embodiments of this application include a reliability sampling sequence. Cubic spline interpolation can be used to obtain the continuous reliability curve. Among them, spline boundaries can be set as natural boundaries. Alternatively, the slope estimation boundary can be set according to the scenario to reduce endpoint bias.

[0121] Furthermore, embodiments of this application can be based on It calculates the system reliability, mean time between failures, failure rate, and failure probability density of the engineering production system for subsequent output.

[0122] In one embodiment of this application, the formula for calculating the mean time between failures (MTBF) may be, but is not limited to, the following: , in, To perform numerical integration over densely sampled areas, To perform numerical integration over the sparse sampling region, The time interval between two sampling points. For the continuous reliability curve at time point The value of , For the continuous reliability curve at time point The value of , For the continuous reliability curve at time point The value of , The preset task limit, For continuous reliability curve functions, Mean time between failures (MTBF) is the average time between failures (MTBF).

[0123] It is understandable that the adaptive time grid has partitioning characteristics of dense sampling regions and sparse sampling regions. The corresponding numerical integration methods can be used according to the characteristics: for the dense sampling region, the area of ​​the trapezoid formed by two adjacent sampling points can be used for approximate calculation, while for the sparse sampling region, the area of ​​the parabola fitted by three sampling points can be used for approximate calculation. Then, the integration results of the dense sampling region and the sparse sampling region are summed to obtain the mean time between failures.

[0124] This application embodiment improves the accuracy and reliability of the results by performing numerical integration and summation on the dense sampling region and the sparse sampling region respectively. This ensures the fitting accuracy in the dense sampling region and makes up for the lack of sampling points in the sparse sampling region, while reducing the computational power consumption and improving the efficiency of reliability assessment.

[0125] Furthermore, the failure density and failure rate are given by the spline derivative, and the expression for the failure density can be, but is not limited to, as follows: , in, Failure density reflects the failure rate of the engineering production system at a given time point. The probability of failure occurring within a unit of time in the vicinity.

[0126] The expression for failure rate can be, but is not limited to, as follows: , in, Failure rate reflects the failure rate of the engineering production system at a given time point. Assuming it was not previously invalid, at the point in time The probability of failure occurring within a unit of time.

[0127] In some cases, embodiments of this application can be used for... Use short-window smoothing (such as a 3-point moving average) to suppress numerical noise.

[0128] It should be noted that, before outputting the system reliability, mean time between failures, failure rate, failure probability density, critical component importance, error range, and convergence report of the engineering production system, the embodiments of this application can control the error based on the upper limit of error and the convergence threshold: 1. In this application, the embodiments are for functional subnets with pure series and pure parallel structures. The analytical results are compared with the numerical flow results, and the relative errors are statistically analyzed.

[0129] 2. This application's embodiment targets the ultra-large k / n voting function subnet, at critical moments. Give the results of DP / exact combination. Results approximating MC To control relative error. The formula for judging relative error can be, but is not limited to, the following: , in, The relative error between the DP / exact combination result and the MC approximation result.

[0130] The embodiments of this application can automatically increase the number of MC samplings or revert to the DP / precise combination method when the relative error exceeds 1%.

[0131] 3. In this embodiment, the calculation results from two rounds of adaptive time grids are used to determine whether the calculation results are stable. Stability indicates convergence. The determination formula may be, but is not limited to, the following: , , in, For the first The mean time between failures (MTBF) calculated by round-robin For the first The calculation results in a certain task limit. The reliability.

[0132] The embodiments of this application can achieve an absolute change in mean time between failures (MTBF) or reliability of less than [a certain value]. When global convergence is considered, the addition of sampling points to the adaptive time grid is stopped.

[0133] Furthermore, embodiments of this application can indicate the confidence level of MC sampling results through standard error. The expression for standard error can be, but is not limited to, as follows: , in, The standard error is the smaller the standard error, the higher the confidence level of the MC sampling results. The reliability obtained from MC sampling; The number of times MC is sampled.

[0134] For example, such as Figure 4 As shown in the embodiments of this application, this application briefly describes how to compare analytical results with numerical flow results and statistically analyze the relative error for functional subnets with pure series and pure parallel structures.

[0135] In this embodiment, a functional subnet is assumed to contain only two independent elements A and B, which can form a purely series subnet or a purely parallel subnet. A critical moment is selected. Perform comparative calculations.

[0136] S401: Set critical moment and component reliability input.

[0137] Among them, key moments can be taken as characteristic time points, for example Components A and B are in The reliability can be assessed using a lifetime distribution model or monitoring data, for example: , .

[0138] S402: Calculate the analytical equivalent reliability of a pure series subnet.

[0139] The analytical expression for a purely series system is: , Substituting the example number into the value: .

[0140] S403: Calculate the equivalent reliability of a pure serial subnet at critical moments using a numerical process.

[0141] The numerical process can obtain numerical results of component reliability through methods such as discrete sampling, simulation estimation, and recursive calculation. Obtained from: , , The equivalent reliability of the series connection under numerical flow is: .

[0142] S404: Calculate the relative error between the analytical expression and the numerical process of a pure series subnet.

[0143] The relative error can be defined as follows: , Substituting the example number into the value: .

[0144] S405: Calculate the analytical equivalent reliability of a pure parallel subnet.

[0145] The analytical expression for a pure parallel system (with at least one channel functioning normally) is: , Substituting the example number into the value: .

[0146] S406: Calculation of a pure parallel subnet using numerical procedures The equivalent reliability is given and the relative error is provided.

[0147] Among them, the component reliability under the numerical process , ,have to: , The relative error can be defined as: .

[0148] For example, such as Figure 5 As shown in the embodiments of this application, this application briefly describes how to control the error based on the upper limit of error and the convergence threshold.

[0149] S501: Read input.

[0150] In this embodiment, the calculation results of the adaptive time grid and the two rounds of adaptive time grid, the upper limit of error, and the convergence threshold are read.

[0151] S502: Verify consistency at critical moments.

[0152] In this embodiment, at critical moments, for the ultra-large k / n voting function subnet, the results of DP / exact combination are compared with the results of MC approximation to determine whether the consistency at critical moments is met.

[0153] S503: Determine whether consistency at critical moments meets the standard.

[0154] In this embodiment, when the consistency at critical moments fails to meet the standard, the accuracy of the results at critical moments can be improved (e.g., by increasing the number of MC samplings or rolling back to DP / precise combination calculation), and the consistency at critical moments can be re-verified. When the consistency at critical moments meets the standard, the process can proceed to the next step.

[0155] S504: Estimate local errors.

[0156] In this embodiment, after the consistency verification at a critical moment is completed, the error can be evaluated based on the time interval (such as comparing the approximate curvature with a certain curvature threshold).

[0157] S505: Determine if there is a time interval that exceeds the threshold.

[0158] In this embodiment, when there is a time interval exceeding the threshold, sampling points are added to the time interval exceeding the threshold, the system reliability corresponding to the point is recalculated, and the local error is re-estimated to iteratively optimize the adaptive time grid. When there is no time interval exceeding the threshold, the process proceeds to the next step.

[0159] S506: Calculate the global rate of change.

[0160] In this embodiment, the global rate of change can be calculated based on the calculation results of two rounds of adaptive time grids to determine whether the convergence condition is met.

[0161] S507: Determine whether the convergence condition is met.

[0162] In this embodiment, when the convergence condition is not met, the adaptive time grid is encrypted and the consistency of key points is re-verified to improve accuracy; when the convergence condition is met, subsequent output is performed.

[0163] It is understood that the embodiments of this application can summarize and output the system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report of the engineering production system to obtain reliability assessment results.

[0164] The error range and convergence report may include, but are not limited to, confidence intervals for evaluation metrics (such as the 95% confidence interval for mean time between failures), descriptions of key point states (such as whether nodes are calculated precisely or approximated at critical moments), and precision quantification data (such as the standard error of MC sampling and the iteration history of the adaptive time grid).

[0165] The importance of critical components can be used to identify weak points. The following section details how to analyze the importance of critical components.

[0166] Importance is based on the Birnbaum definition, and its expression can be, but is not limited to, as follows: , in, For the first The importance of each node, For the first The reliability of each node, For the reliability of engineering production systems.

[0167] In a serial subnet, the failure of any node will cause the system to fail. The simplified form of this can be, but is not limited to, the following: , in, For the first The reliability of each node.

[0168] In a parallel subnet, the system fails only when all nodes fail. Its simplified form can be, but is not limited to, as follows: .

[0169] General subnets (such as hybrid subnets containing serial, parallel, and k / n voting) propagate on a DAG (Directed Acyclic Graph) using the chain rule, or are approximated by perturbations. Their expressions can be, but are not limited to, as follows: , in, For at a certain point in time Assign reliability to a node Apply small perturbation After that, the reliability of the engineering production system.

[0170] This application embodiment can perform tasks within a task space. Take the average / peak values ​​to form a TOP-N list of critical components (such as the top 5 most important components) to support engineering decisions, such as prioritizing preventative maintenance of critical components or increasing spare parts inventory for critical components.

[0171] This application's embodiments transform discrete sampling into continuous reliability curves through smooth interpolation, intuitively displaying the trend of system reliability changing over time. It also outputs indicators constrained by upper limit of error and convergence threshold to ensure the accuracy and reliability of the results. At the same time, it outputs multi-dimensional reliability assessment results, which not only cover the quantitative description of system reliability, but also accurately locate weak links and clarify the range of credibility of the results, fully supporting the actual decision-making needs of engineering production systems such as preventive maintenance, spare parts reserves, and risk control.

[0172] The principle of the reliability assessment method for engineering production systems proposed in this application is illustrated below with a specific embodiment.

[0173] Those skilled in the art can first draw a reliability block diagram of the engineering production system and enter or bind metadata (such as distribution type parameters and empirical reliability curves) for each functional block. Then, the embodiments of this application can transform the reliability block diagram into a reliability functional network.

[0174] Furthermore, embodiments of this application can encapsulate nodes from different sources into queryable functions to unify semantics. In addition, embodiments of this application can perform topology traversal and pattern recognition on the reliability functional network and automatically label typical reliability structural units to determine functional subnets.

[0175] Furthermore, those skilled in the art can first configure the time domain strategy, which may include, but is not limited to, configuring the task upper limit and accuracy parameters, and then construct an initial time grid to obtain a rough initial reliability curve. Then, the approximate curvature of the time interval to be analyzed is calculated, and based on the approximate curvature and a certain curvature threshold, it is determined whether to add sampling points in the time interval to be analyzed to obtain an adaptive time grid.

[0176] Furthermore, in this embodiment, a reliability sampling sequence can be generated based on an adaptive time grid. After smoothing and interpolating the reliability sampling sequence, a continuous reliability curve is obtained. Based on a certain upper limit of error and a convergence threshold, the system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report of the engineering production system are output as reliability assessment results.

[0177] The reliability assessment method for engineering production systems proposed in this application transforms the reliability block diagram into a reliability functional network and decomposes it into functional subnets, replacing manual decomposition. This avoids missed or misjudged cases under complex topologies and reduces computational complexity. Furthermore, by generating an adaptive time grid, the sampling step size can be dynamically adjusted, ensuring computational accuracy during high-risk periods while improving computational efficiency during stable periods, balancing accuracy and computational power consumption. In addition, by outputting multi-dimensional reliability assessment results, it provides quantitative and reliable support for practical decisions such as maintenance and spare parts reserves of engineering production systems. Therefore, this method solves the problems of related technologies that rely on manually decomposing the semantic information corresponding to the reliability block diagram and using fixed-step discrete approximation to calculate reliability assessment results, which easily leads to missed or misjudged cases due to complex topologies, poor computational accuracy during high-risk periods, and low computational efficiency during stable periods, making it difficult to support engineering decisions.

[0178] Next, the reliability assessment apparatus for an engineering production system proposed according to an embodiment of this application is described with reference to the accompanying drawings.

[0179] Figure 6 This is a block diagram of a reliability assessment device for an engineering production system provided according to an embodiment of this application.

[0180] like Figure 6 As shown, the reliability assessment device 60 of the engineering production system includes: an acquisition module 100, an annotation module 200, and an assessment module 300.

[0181] The acquisition module 100 is used to acquire the reliability block diagram of the engineering production system and determine the metadata of each functional block in the reliability block diagram in order to generate a reliability functional network.

[0182] The annotation module 200 is used to annotate at least one typical reliability structural unit of the reliability functional network to identify at least one functional subnet and configure a time domain strategy.

[0183] Evaluation module 300 is used to generate an adaptive time grid based on a time domain strategy, and solve the equivalent reliability of the functional subnet at the adaptive time point of the adaptive time grid to obtain the reliability sampling sequence of the engineering production system and generate the reliability evaluation result of the engineering production system.

[0184] Optionally, in one embodiment of this application, the evaluation module 300 includes: a first generation unit, an output unit, and a second generation unit.

[0185] The first generation unit is used to perform smooth interpolation reconstruction on the system curve corresponding to the reliability sampling sequence to obtain a continuous reliability curve.

[0186] The output unit is used to output at least one of the following based on the continuous reliability curve: system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report of the engineering production system, according to a preset error upper limit and convergence threshold.

[0187] The second generation unit is used to generate reliability assessment results based on at least one of the following: system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report.

[0188] Optionally, in one embodiment of this application, the evaluation module 300 includes: a building unit and a third generation unit.

[0189] The establishment unit is used to establish an initial reliability curve based on the time domain strategy and calculate the approximate curvature of the time interval to be analyzed.

[0190] The third generation unit is used to determine whether to add sampling points in the time interval to be analyzed based on the approximate curvature, so as to generate an adaptive time grid.

[0191] Optionally, in one embodiment of this application, the formula for calculating the approximate curvature may be, but is not limited to: , in, For approximate curvature, This is the starting point of the time interval to be analyzed. This is the end point of the time interval to be analyzed. This represents the midpoint of the time interval to be analyzed. This represents the reliability of the initial reliability curve.

[0192] Optionally, in one embodiment of this application, the formula for calculating the mean time between failures (MTBF) may be, but is not limited to: , in, To perform numerical integration over densely sampled areas, To perform numerical integration over the sparse sampling region, The time interval between two sampling points. For the continuous reliability curve at time point The value of , For the continuous reliability curve at time point The value of , For the continuous reliability curve at time point The value of , The preset task limit, For continuous reliability curve functions, Mean time between failures (MTBF) is the average time between failures (MTBF).

[0193] It should be noted that the foregoing explanation of the reliability assessment method embodiment for engineering production systems also applies to the reliability assessment device for the engineering production system in this embodiment, and will not be repeated here.

[0194] The reliability assessment device for engineering production systems proposed in this application transforms the reliability block diagram into a reliability functional network and decomposes it into functional subnets, replacing manual decomposition. This avoids missed or misjudged cases under complex topologies and reduces computational complexity. Furthermore, by generating an adaptive time grid, the sampling step size can be dynamically adjusted, ensuring computational accuracy during high-risk periods while improving computational efficiency during stable periods, balancing accuracy and computational power consumption. In addition, by outputting multi-dimensional reliability assessment results, it provides quantitative and reliable support for practical decisions such as maintenance and spare parts reserves in engineering production systems. Therefore, it solves the problems of related technologies that rely on manually decomposing the semantic information corresponding to the reliability block diagram and using fixed-step discrete approximation to calculate reliability assessment results, which easily leads to missed or misjudged cases due to complex topologies, poor computational accuracy during high-risk periods, and low computational efficiency during stable periods, making it difficult to support engineering decisions.

[0195] Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0196] When the processor 702 executes the program, it implements the reliability assessment method for the engineering production system provided in the above embodiments.

[0197] Furthermore, electronic devices also include: Communication interface 703 is used for communication between memory 701 and processor 702.

[0198] The memory 701 is used to store computer programs that can run on the processor 702.

[0199] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0200] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0201] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0202] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0203] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the reliability assessment method for the engineering production system described above.

[0204] This application also provides a computer program product, including a computer program that, when executed, implements the reliability assessment method for the engineering production system described above.

[0205] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0206] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0207] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0208] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0209] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0210] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0211] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0212] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A reliability assessment method for an engineering production system, characterized in that, Includes the following steps: Obtain the reliability block diagram of the engineering production system and determine the metadata of each functional block in the reliability block diagram to generate a reliability functional network; Label at least one typical reliability structural unit of the reliability functional network to identify at least one functional subnet and configure a time-domain strategy; Based on the time domain strategy, an adaptive time grid is generated, and the equivalent reliability of the functional subnet is solved at the adaptive time point of the adaptive time grid to obtain the reliability sampling sequence of the engineering production system and generate the reliability assessment result of the engineering production system.

2. The method according to claim 1, characterized in that, The generation of the reliability assessment results for the engineering production system includes: The reliability sampling sequence is reconstructed by smooth interpolation to obtain a continuous reliability curve; Based on the preset error upper limit and convergence threshold, output at least one of the following from the continuous reliability curve: system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report of the engineering production system. The reliability assessment result is generated based on at least one of the following: system reliability, mean time between failures, failure rate, failure probability density, critical component importance, error range, and convergence report.

3. The method according to claim 1, characterized in that, The process of generating the adaptive time grid based on the time domain strategy includes: Based on the aforementioned time-domain strategy, an initial reliability curve is established, and the approximate curvature of the time interval to be analyzed is calculated. Based on the approximate curvature, it is determined whether to add sampling points to the time interval to be analyzed in order to generate the adaptive time grid.

4. The method according to claim 3, characterized in that, The formula for calculating the approximate curvature is: ， in, For the approximate curvature, This refers to the starting time point of the time interval to be analyzed. This refers to the end point of the time interval to be analyzed. This refers to the midpoint of the time interval to be analyzed. The reliability of the initial reliability curve is given.

5. The method according to claim 2, characterized in that, The formula for calculating the mean time between failures (MTBF) is: ， in, To perform numerical integration over densely sampled areas, To perform numerical integration over the sparse sampling region, The time interval between two sampling points. The continuous reliability curve at time point The value of , The continuous reliability curve at time point The value of , The continuous reliability curve at time point The value of , The preset task limit, The continuous reliability curve function is... The mean time between failures (MTBF) is the average time between failures.

6. A reliability assessment device for an engineering production system, characterized in that, include: The acquisition module is used to acquire the reliability block diagram of the engineering production system and determine the metadata of each functional block in the reliability block diagram to generate a reliability functional network. The annotation module is used to annotate at least one typical reliability structural unit of the reliability functional network to identify at least one functional subnet and configure a time domain strategy. The evaluation module is used to generate an adaptive time grid based on the time domain strategy, and solve the equivalent reliability of the functional subnet at the adaptive time point of the adaptive time grid to obtain the reliability sampling sequence of the engineering production system and generate the reliability evaluation result of the engineering production system.

7. The apparatus according to claim 6, characterized in that, The evaluation module includes: The first generation unit is used to perform smooth interpolation reconstruction on the system curve corresponding to the reliability sampling sequence to obtain a continuous reliability curve; The output unit is used to output at least one of the following based on the continuous reliability curve: system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report of the engineering production system, according to the preset error upper limit and convergence threshold. The second generation unit is used to generate the reliability assessment result based on at least one of the following: system reliability, mean time between failures, failure rate, failure probability density, importance of key components, error range, and convergence report.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the reliability assessment method for an engineering production system as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the reliability assessment method for the engineering production system as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the reliability assessment method for the engineering production system as described in any one of claims 1-5.

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