A method for automatically constructing a fault tree based on a multi-signal flow graph

Abstract

The application discloses a kind of based on the automatic construction method of fault tree of multi-signal flow graph.The fault tree automatic construction method is aimed at solving the problems such as low construction efficiency, many logic blind areas, dynamic optimization difficulty in complex system in existing method.The fault tree automatic construction method is divided into three steps: multi-signal flow graph entity extraction based on ontology constraint, entity structure regularization based on flow graph connectivity and fault tree data automatic layout based on depth-first.The fault tree automatic construction method uses the node and attribute information in multi-signal flow graph, establishes dynamic corresponding relationship with fault tree node, realizes accurate mapping from entity to fault tree node;By skipping irrelevant node, completing associated relationship and automatically inserting "or" node, the integrity of fault tree logical link is ensured;By depth-first branch statistics and breadth-first automatic layout, global information is obtained and top-down layered layout and inter-node connection are realized, and finally the automatic drawing of fault tree is completed.This method supports complex system fault analysis requirements across components, across levels, significantly improves the efficiency and accuracy of fault tree construction, and provides a new technical means for modern reliability management.

Description

Technical Field

[0001] This invention relates to an automated method for constructing fault trees, and more particularly to an automated method for constructing fault trees based on multi-signal flow graphs. Background Technology

[0002] Fault tree analysis is one of the core methods in the safety analysis of complex systems, and it is widely used in high-reliability fields such as aerospace, nuclear power, and transportation. By decomposing the logical relationships of potential faults into an intuitive tree structure, fault trees can help engineers identify critical failure modes and their propagation paths. However, with the increasing complexity of modern systems, traditional manual fault tree construction methods are gradually facing many challenges and are unable to meet the needs of efficient and accurate analysis.

[0003] In complex systems, the interactions between functional modules are becoming increasingly close, and signal propagation paths often span multiple components and involve multi-level logical connections. Traditional fault tree analysis methods typically focus on the analysis of fault cases, capturing fault modes through depth-first analysis of fault mechanisms. However, this approach has certain blind spots, especially when dealing with observable manifestations of faults, often failing to comprehensively capture all possible fault propagation chains in the system. Particularly when there are complex interactions or multiple causal relationships between fault modes, traditional fault tree analysis methods often fail to effectively integrate these relationships, leading to omissions or misjudgments of fault propagation paths, further limiting the depth and breadth of fault tree analysis. Therefore, existing methods exhibit significant limitations when handling multiple fault modes or fault propagation across components and levels, and cannot fully meet the fault analysis needs of complex systems.

[0004] Furthermore, existing fault trees are typically constructed based on individual fault cases. This case-centric approach to analysis can easily lead to lengthy and fragmented fault trees. Because fault trees are built around specific cases, they lack the ability to dynamically integrate faults, especially in scenarios involving cross-component fault propagation. Traditional methods often fail to flexibly integrate the fault modes of various components and their interactions into a comprehensive cross-component fault tree. This not only affects the efficiency of analysis but also increases the difficulty of troubleshooting. With complex fault trees, engineers often need to spend a significant amount of time searching for specific fault modes or associated signals, which inherently increases the difficulty of maintenance and troubleshooting. Therefore, simplifying the fault tree structure and enabling it to dynamically adjust according to actual maintenance and troubleshooting scenarios is a key issue in improving the efficiency and operability of fault tree usage.

[0005] Therefore, fully leveraging the knowledge of multi-signal flow graphs that dynamically evolve throughout the entire lifecycle to empower fault tree construction and realize the dynamic evolution and global construction of fault trees is a crucial task for upgrading the reliability assurance of complex identification. This approach will provide a new technical path for the security analysis of complex systems, significantly improving analysis efficiency and the reliability of results, while also providing strong support for modern reliability management systems. Summary of the Invention

[0006] This invention addresses the problems of low efficiency, numerous logical blind spots, and difficulty in dynamic optimization associated with existing fault tree construction methods in complex systems. It proposes an automated fault tree construction method based on multi-signal flow graphs. This method fully leverages the dynamic evolution of multi-signal flow graphs throughout the product lifecycle. Through entity knowledge extraction, structure filtering, and tree layout generation, it achieves automated construction from multi-signal flow graphs to fault trees, thereby meeting the needs of modern complex systems for rapid fault tree generation, automatic improvement, and dynamic evolution.

[0007] The automated fault tree construction method based on multi-signal flow graphs comprises three main steps: entity extraction from multi-signal flow graphs based on ontology constraints, entity structure regularization based on flow graph connectivity, and automatic fault tree data layout based on depth-first search. The entity extraction method based on ontology constraints establishes a dynamic correspondence between signal flow graph nodes and fault tree nodes by parsing the types and attributes of these nodes, achieving accurate mapping from signal flow graph entities to fault tree node types. The entity structure regularization method based on flow graph connectivity ensures the integrity and simplicity of the fault tree logical links by skipping irrelevant nodes, completing association relationships, and automatically inserting OR nodes. The automatic fault tree data layout method based on depth-first search utilizes depth-first statistical branching and breadth-first iterative automatic layout to obtain global information and achieve top-down hierarchical layout and node connections, ultimately completing the automatic drawing of the fault tree.

[0008] The features of this invention are:

[0009] (1) By using the ontology knowledge structure extraction method based on multi-signal flow graph, the association modeling between fault modes, signals and components is realized, which effectively makes up for the logical blind spot caused by manual inference in the traditional fault tree construction method;

[0010] (2) By automatically filtering the fault propagation relationship within the multi-signal flow graph, redundant or invalid fault modes are eliminated, thus meeting the efficiency and accuracy requirements of industrial scenarios in the fault tree construction process.

[0011] (3) By utilizing the dynamic characteristics of multi-signal flow graphs, fault trees can be generated and dynamically optimized quickly through automated layout methods, supporting fault tree evolution throughout the product's lifecycle, thereby significantly improving the reliability analysis capabilities of complex systems. Attached Figure Description

[0012] Figure 1 This is the overall flowchart of the fault tree construction method based on multi-signal flow graphs.

[0013] Figure 2 This is a flowchart of a multi-signal flow graph entity extraction method based on ontology constraints.

[0014] Figure 3 This is a flowchart of an entity structure regularization method based on flow graph connectivity.

[0015] Figure 4 This is a flowchart of a depth-first fault tree data automatic layout method. Detailed Implementation

[0016] The following is a detailed description of the automated fault tree construction method based on multi-signal flow graphs provided by the present invention, with reference to the accompanying drawings.

[0017] This invention provides an automated fault tree construction method based on multi-signal flow graphs. In general, this method involves three main stages: entity extraction from the multi-signal flow graph based on ontology constraints, entity structure regularization based on flow graph connectivity, and automatic fault tree data layout based on depth-first search. It extracts three entity types—"fault mode," "AND," and "OR"—from the nodes and attribute information in the multi-signal flow graph, establishes a dynamic correspondence with fault tree nodes, and achieves accurate mapping from entities to fault tree nodes. Then, by skipping irrelevant nodes, completing association relationships, and automatically inserting "OR" nodes, it ensures the integrity of the fault tree logical links. Finally, it utilizes the acquired knowledge to obtain global information and achieves top-down hierarchical layout and inter-node connections, thus achieving the goal of automatically drawing the fault tree. The overall flowchart of the automated fault tree construction method based on multi-signal flow graphs is as follows: Figure 1 As shown.

[0018] 1. Entity Extraction from Multi-Signal Flow Graphs Based on Ontology Constraints

[0019] The ontology-constrained multi-signal flow graph entity extraction step parses the type and attributes of signal flow graph nodes through two steps: mapping multi-signal flow graph entities to fault tree structures and dynamically defining fault tree entities graphically. This establishes a dynamic correspondence between signal flow graph entities and fault tree nodes, achieving accurate mapping from signal flow graph entities to fault tree node types. The flowchart of the ontology-constrained multi-signal flow graph entity extraction method is shown below. Figure 2 As shown.

[0020] 1.1. Correspondence between multi-signal flow graph entities and fault tree structure

[0021] The multi-signal flow graph entities mainly consist of three entity types: "fault mode," "AND," and "OR," which correspond to the fault tree. Among them, "fault mode" corresponds to the fault propagation node in the fault tree, while "AND" and "OR" represent two condition judgment nodes in the fault tree.

[0022] The step of mapping multi-signal flow graph entities to fault tree structures involves traversing the entity list of the multi-signal flow graph based on the relationship between the entities. After modifying the attributes of the corresponding entities to "event", "AND", and "OR" fault tree nodes, the entities are loaded into a new list. This process initially extracts the entity list of the fault tree, providing structured data that is easy to call in subsequent steps.

[0023] 1.2. Graphical Dynamic Definition of Fault Tree Entities

[0024] The dynamic graphical definition step for fault tree entities is supported by underlying node classification processing logic. Utilizing the systematic mapping between types determined in the previous step, it further dynamically defines nodes based on their category identifiers, dynamically assigning specific icons or styles to different types of nodes during the mapping process. This dynamic graphical definition of nodes requires user interaction using editable image drawing software such as Visio. Users provide basic entity definition information as shown in Table 1 within the style. The corresponding step between the multi-signal flow graph entity and the fault tree structure automatically adds dynamic information such as entity image elements and input / output anchor points during the loading process, according to the lookup table, thereby achieving adaptive functionality of user-defined styles in the subsequent fault tree drawing process.

[0025] Table 1. Comparison of Basic Entity Definition Information

[0026]

[0027] 2. Regularized entity structure based on flow graph connectivity

[0028] The entity structure regularization step based on flow graph connectivity skips irrelevant nodes, completes associations, and automatically inserts OR nodes through two steps: entity association mining and automatic OR node completion. This ensures the integrity and simplicity of the fault tree logical link. The flowchart of the entity structure regularization method based on flow graph connectivity is shown below. Figure 3 As shown.

[0029] 2.1. Entity Association Mining

[0030] The entity association mining step establishes direct connections from source nodes to target nodes by traversing the edge set of the signal flow graph. During construction, this step automatically ignores nodes that do not participate in the main logic (such as switches, input, or output nodes), focusing on the core connections between entities. This process is implemented by dynamically maintaining an entity jump relationship mapping table, which records all upstream connected nodes of each target node. By reading the information in the table and traversing all edges, edges directly connected to the target node are filtered out and retained. This preserves the relationships between fault modes that conform to the logical rules and removes other connections that do not conform to the fault tree construction specifications. This method ensures that redundant information is automatically skipped while completing the actual logical relationships between entities, making the fault tree topology clearer and more concise.

[0031] 2.2. Auto-completion of "OR" nodes

[0032] The automatic completion step for OR nodes dynamically generates corresponding OR nodes by traversing the upstream connections of each target node and inserts them into the fault tree. For each target node requiring OR node completion, the code generates a unique identifier as the ID of the new OR node and connects all upstream nodes to the target node through this OR node. During generation, the method stores the source nodes of the target node that are not part of the OR node's edge in a mapping table and establishes a mapping relationship between each target node and all its input nodes to ensure that the OR node can correctly combine multiple inputs, thereby forming an effective fault propagation path. This method ensures logical integrity and significantly reduces the workload of manually completing OR nodes.

[0033] 3. Automatic layout of fault tree data based on depth-first search

[0034] The described depth-first fault tree data automatic layout step involves two steps: depth-first branch statistics and breadth-first iteration automatic layout. These steps acquire global information and implement a top-down hierarchical layout and node connections, ultimately completing the automatic drawing of the fault tree. The flowchart of the depth-first fault tree data automatic layout method is shown below. Figure 4 As shown in Figure 3.1. Branch Statistics Based on Depth-First Search

[0035] The depth-first branching statistics step calculates the total number of points from each node to a leaf node, providing global information about the distribution of nodes at the same level during subsequent automatic layout. Specifically, the depth-first branching statistics step first constructs a record table of visited nodes. Then, it traverses the path depth-wise to the leaf nodes. During each visit, if a node is already in the visited node record table, its downstream branches are updated by adding the branch count of that node to the branch count of all nodes along the path except for that node; otherwise, the branch count of each node along the path is incremented by one. After traversing all leaf nodes, the branch count for each node is completed.

[0036] 3.2. Automatic Layout Based on Breadth-to-Iteration

[0037] The breadth-based iterative automatic layout establishes a natural hierarchical relationship from top to bottom by recursively and dynamically inserting new nodes and generating edge connections for them. Specifically, the breadth-based iterative automatic layout method first constructs a list of already laid-out nodes, and then lays out nodes one by one at each level. The vertical position is calculated according to the depth level and the spacing between each level. At the same level, the breadth-based iterative automatic layout method sequentially sums the number of branches of all nodes at that depth, and places each node in the corresponding horizontal position according to the proportion of the cumulative number of branches of the node to the total number of branches at that depth. After obtaining the height position, each node is placed. If the node is already in the list of already laid-out nodes, the node position is not updated; otherwise, the node position is updated and the node is loaded into the list of already laid-out nodes. After laying out nodes at each depth, node image element instances are created according to the node positions, and the connections between nodes are completed according to the connection relationships, thus completing the automatic drawing of the fault tree.

Claims

1. An automated fault tree construction method based on multi-signal flow graphs, characterized in that: By extracting entities, regularizing structures, and automatically laying out multi-signal flow graphs, a logically complete and dynamically optimizable fault tree is quickly generated, improving the efficiency and reliability of fault analysis for complex systems. The automated fault tree construction method includes an entity extraction step based on ontology constraints, an entity structure regularization step based on flow graph connectivity, and an automatic fault tree data layout step based on depth-first search. The entity extraction step based on ontology constraints is as follows: 1.1 Correspondence between multi-signal flow graph entities and fault tree structure The multi-signal flow graph entities contain three entity types: "fault mode," "AND," and "OR," which correspond to the fault tree. "Fault mode" corresponds to the fault propagation node in the fault tree, while "AND" and "OR" represent two condition judgment nodes in the fault tree. The step of corresponding the multi-signal flow graph entities to the fault tree structure is based on the relationship between the entities. By traversing the entity list of the multi-signal flow graph, the entities of the corresponding type are modified to have their attributes changed to the three fault tree nodes: "event," "AND," and "OR," and then loaded into a new list. This process initially extracts the entity list of the fault tree, providing structured data that is easy to call in subsequent steps. 1.2 Dynamic graphical definition of fault tree entities The aforementioned dynamic definition step of the fault tree entity's graphics provides support for dynamic graphics definition through the underlying node classification processing logic. Utilizing the systematic mapping between types determined in the previous step, it further dynamically defines nodes based on their category identifiers. During the mapping process, specific icons or styles are dynamically assigned to nodes of different types. The dynamic definition of the node graphics uses editable image drawing software such as VISIO for user interaction. The correspondence step between the multi-signal flow graph entity and the fault tree structure automatically adds dynamic information such as entity image elements, input and output anchor points, etc., during the loading process according to the lookup table, thereby realizing the adaptive function of user-defined styles in the subsequent fault tree drawing process. The entity structure regularization step based on flow graph connectivity skips irrelevant nodes, completes association relationships, and automatically inserts OR nodes through two steps: entity association mining and automatic completion of "OR" nodes, ensuring the integrity and simplicity of the fault tree logical link. The aforementioned depth-first fault tree data automatic layout step obtains global information and achieves top-down hierarchical layout and node connections through two steps: depth-first branch statistics and breadth-first iteration automatic layout, ultimately completing the automatic drawing of the fault tree.

2. The automated fault tree construction method according to claim 1, characterized in that: The multi-signal flow graph entity extraction step in the automated fault tree construction method parses the node types and attributes in the multi-signal flow graph and uses ontology constraints to establish a dynamic correspondence between signal flow graph entities and fault tree nodes, thereby achieving accurate mapping of node types. This step includes classifying entities in the multi-signal flow graph into "event", "AND", "OR", and "OR" nodes, and dynamically defining graphical styles and connection relationships, thereby providing structured data support for fault tree construction and enabling user-defined visualization functions.

3. The automated fault tree construction method according to claim 1, characterized in that: The entity structure regularization step in the automated fault tree construction method analyzes the relationships between nodes in the multi-signal flow graph, removes irrelevant nodes, and completes the logical links to ensure the structural integrity of the fault tree. This step includes skipping irrelevant connection nodes, dynamically generating "OR" nodes to supplement undefined upstream relationships, and automatically maintaining the connection mapping table to optimize the fault propagation path, thereby improving the logical clarity and construction efficiency of the fault tree.

4. The automated fault tree construction method according to claim 1, characterized in that: The automatic fault tree data layout step in the described automatic fault tree construction method combines depth-first branch statistics and breadth-first iterative layout methods to complete the hierarchical layout and node connections of the fault tree. By counting the number of branches of each node to provide global information, and dynamically adjusting the vertical and horizontal positions according to the depth of the node in the tree and the proportion of branches, a top-down natural distribution and automatic layout are achieved, thereby quickly generating a logically rigorous fault tree graphical structure.

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