FMECA automatic construction method based on multi-signal flow graph

Abstract

The invention discloses an FMECA automatic construction method based on a multi-signal flow graph. According to the FMECA automatic construction method, an ontology knowledge structure based on a multi-signal flow graph is constructed, so that an automatic generation process from a complex signal flow graph to an FMECA analysis table is realized. The FMECA automatic construction method comprises three steps: basic fault mode screening based on a propagation relationship, multi-signal flow graph structured knowledge extraction based on ontology constraint, and FMECA automatic generation based on structured knowledge. According to the FMECA automatic construction method, a signal interaction relationship of a multi-signal flow graph is analyzed through fault propagation logic, effective fault modes are screened, and standardized FMECA entries are generated in combination with structured ontology knowledge, so that efficient and automatic construction of FMECA files in a complex system is realized. According to the method, the FMECA file can be dynamically updated, the collaborative improvement of the whole life cycle is supported, and the construction efficiency and accuracy are remarkably improved.

Description

Technical Field

[0001] The invention relates to an automatic construction method of an FMECA file, in particular to an automatic construction method of an FMECA file based on a multi-signal flow graph. Background Art

[0002] FMECA (Failure Mode, Effects, and Criticality Analysis) is an analysis method widely used in the design and evaluation of complex systems. It aims to identify potential failure modes, analyze their impact on the system and evaluate their criticality. It is of great significance to the reliability assurance of complex products.

[0003] However, with the gradual expansion of the scale of complex systems and the increasing sophistication of functional structures in multiple fields such as aviation and aerospace, the difficulty of constructing FMECA files has gradually increased. The FMECA construction method directly driven by expert experience often has a large analysis blind spot in failure modes involving multi-component cross-coupling, and cannot fully cover reliability requirements. At the same time, the construction of traditional FMECA files is often limited to the design stage of the product, and the large amount of fault reasoning knowledge accumulated during the operation stage cannot be fed back to the FMECA file, resulting in a lack of complete reliability knowledge that is incrementally coordinated with experience in the design and maintenance of subsequent products. Due to the above defects, the traditional FMECA construction method is difficult to fully meet the current high-quality reliability assurance needs of complex equipment, and a new FMECA construction method with automatic improvement and incremental capabilities is urgently needed.

[0004] At the same time, in the modern reliability life cycle process, the trend of model management based on multi-signal flow graphs is becoming more and more obvious. Figure 1 On the one hand, it can be modified incrementally, so that it can be effectively improved with the accumulation of product fault diagnosis knowledge; on the other hand, it can integrate fragmented fault propagation knowledge through the graph structure of the model, so as to effectively infer more abundant potential fault propagation knowledge. The automated construction of FMECA based on multi-signal flow graphs can realize the collaborative improvement and incremental function of FMECA in the whole life cycle of products at the lowest cost under the modern reliability assurance framework, which has important engineering significance.

[0005] Therefore, the FMECA construction method based on multi-signal flow graphs needs to be further optimized to achieve the automated construction from multi-signal flow graphs to FMECA analysis tables. On the one hand, the method needs to have the ability to parse complex signal flow graphs to ensure that the interaction logic and fault propagation paths between signals can be accurately identified; on the other hand, it is necessary to lower the usage threshold through framework tools so that personnel in the development and use stages can quickly generate FMECA analysis tables and intuitively display the analysis results. This method can not only improve the efficiency of FMECA analysis of complex systems, but also provide important technical support for intelligent fault analysis.

[0006] In summary, in the context that the existing FMECA methods are gradually unable to adapt to the needs of complex multi-signal systems, this paper proposes a generalized FMECA generation framework based on multi-signal flow graphs, and provides an automated construction method from multi-signal flow graphs to FMECA tables. This method can not only meet the multi-signal analysis needs of complex systems, but also realize the automation and generalization of the analysis process, significantly improving the construction efficiency and accuracy of FMECA. Summary of the invention

[0007] The present invention provides an automatic FMECA construction method based on a multi-signal flow graph to solve the problems of high construction difficulty, large knowledge blind spot, and difficulty in dynamic evolution in the existing FMECA construction method in complex systems. The method makes full use of the multi-signal flow graph that evolves synchronously with the product life cycle knowledge, and realizes the automatic construction function of FMECA based on product reliability knowledge through ontology knowledge extraction, screening and transformation, thereby meeting the modern complex system reliability requirements for automatic improvement and evolution of FMECA during the product life cycle.

[0008] The FMECA automatic construction method based on multi-signal flow graph includes three main steps: basic fault mode screening based on propagation relationship, multi-signal flow graph structured knowledge extraction based on ontology constraint, and FMECA automatic generation based on structured knowledge. The basic fault mode screening step based on propagation relationship is mainly responsible for intelligently screening invalid or redundant fault modes using fault propagation logic and association relationship, and extracting a streamlined basic fault mode set; the multi-signal flow graph structured knowledge extraction step based on ontology constraint is mainly responsible for parsing the signal propagation path and interaction logic in the multi-signal flow graph, constructing three ontology knowledge structures, and providing an efficient and consistent knowledge basis for subsequent FMECA generation; the FMECA automatic generation step based on structured knowledge is mainly responsible for automatically generating FMECA entries using standardized ontology knowledge structure and the screened basic fault mode set, and realizing efficient automation of the entire process from multi-signal flow graph to FMECA analysis.

[0009] The characteristics of the present invention are:

[0010] (1) Through the knowledge entities and associated ontology constraints, a correspondence method between the multi-signal flow graph structure information and FMECA items was constructed, thereby breaking through the blind spots of FMECA documents caused by manual fault analysis based on the structural information of the multi-signal flow graph;

[0011] (2) Through ontology analysis of the fault propagation structure in the multi-signal flow graph, the fault entities in the graph structure are screened for necessity, thus meeting the requirements of industrial scenarios for the simplification of items in FMECA files;

[0012] (3) Through the automated structured knowledge filling method, the automatic construction function of FMECA files based on multi-signal flow graphs is realized, thereby leveraging the dynamic evolution characteristics of multi-signal flow graphs to realize the dynamic evolution of FMECA files throughout the product life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is an overall flow chart of an FMECA automation construction method based on multi-signal flow graph.

[0014] Figure 2 Flowchart of the structured knowledge extraction method based on ontology constraints of multi-signal flow graph.

[0015] Figure 3 The flowchart of the basic failure mode screening method based on propagation relationship is shown in Figure 2.

[0016] Figure 4 Automatically generate method flow chart for FMECA based on structured knowledge. DETAILED DESCRIPTION

[0017] The following specifically describes an FMECA automated construction method based on a multi-signal flow graph provided by the present invention in conjunction with the accompanying drawings.

[0018] The present invention provides an automatic construction method for FMECA based on a multi-signal flow graph. Overall, the method realizes the conversion process of a multi-signal flow graph to FMECA through three main stages: structured knowledge extraction of a multi-signal flow graph based on ontology constraints, basic fault mode screening based on propagation relationships, and automatic generation of FMECA based on structured knowledge. The method is based on the propagation relationships in the multi-signal flow graph to form three ontological knowledge structures: fault mode-fault mode, fault mode-signal, and fault mode-component, and screens out irrelevant fault modes in the screening stage, and finally generates complete FMECA entries based on the screened fault mode set. The overall flow chart of the automatic construction method for FMECA based on a multi-signal flow graph is as follows: Figure 1 shown.

[0019] 1. Structured knowledge extraction of multi-signal flow graphs based on ontology constraints

[0020] The multi-signal flow graph structured knowledge extraction step based on ontology constraints extracts structured knowledge from the multi-signal flow graph. Structured knowledge extraction is the process of parsing a complex multi-signal flow graph into an ontology knowledge structure that is easy to operate, mainly including multi-signal flow graph-oriented Figure 3 The five steps are as follows: preliminary extraction of multi-signal flow graph knowledge of the core ontology knowledge structure, generation of fault mode-fault mode ontology knowledge structure, generation of fault mode-signal ontology knowledge structure, generation of fault mode-component ontology knowledge structure, and comprehensive extraction of structured knowledge. The flowchart of the multi-signal flow graph structured knowledge extraction method based on ontology constraints is as follows: Figure 2 shown.

[0021] 1.1. Preliminary Extraction of Multi-Signal Flow Graph Knowledge

[0022] The preliminary extraction step of the multi-signal flow graph knowledge performs preliminary entity extraction on the multi-signal flow graph through the ontology structure of fault propagation. The ontology structure of fault propagation is mainly divided into entities and entity associations, which correspond to nodes and edges in the multi-signal flow graph respectively. The entities mainly include two types: fault mode and signal, which correspond to the two concepts of "fault" and "measurement point" in the concept of multi-signal flow graph respectively; and in the extraction process, the entity only retains the text and type information of the multi-signal flow graph node as the name and type of the entity. The entity relationship mainly includes two fault propagation relationships between fault modes and fault modes to signals, which are consistent with the edge relationship definition between the corresponding entities of the multi-signal flow graph; and in the extraction process, the entity association only retains the source entity text and target entity text information of the multi-signal flow graph node.

[0023] 1.2. Generation of Failure Mode-Failure Mode Ontology Knowledge Structure

[0024] The step of generating the fault mode-fault mode ontology knowledge structure records the cause-effect relationship between each fault mode by parsing the signal propagation path. The method recursively checks the connection path in the edge list, determines whether it is a fault mode by the node type, traverses all propagation paths in the signal flow graph, traces back from the output end of the signal to the input end, and marks the corresponding upstream fault mode and downstream fault mode one by one. Through the graph traversal algorithm, each pair of fault modes with a direct propagation relationship is recorded, and an ontology knowledge structure in the form of upstream fault mode: [downstream fault mode 1, downstream fault mode 2...] is generated. 1.3. Generation of fault mode-signal ontology knowledge structure

[0025] The generation step of the fault mode-signal ontology knowledge structure is to extract the measurement point number related to the current fault mode by calling the measurement point number extraction function in the auxiliary function when parsing the signal flow graph, obtain the text field representing the signal information of the measurement point, and form the association between the fault mode and the signal. Analyze the signal range affected by each fault mode one by one, and mark which fault modes each signal is controlled by. The generated ontology knowledge structure is fault mode: [signal 1, signal 2...], which provides a basis for subsequent judgment of whether a signal is related to a specific fault mode.

[0026] 1.4. Generation of Failure Mode-Component Ontology Knowledge Structure

[0027] The generation step of the fault mode-component ontology knowledge structure further identifies the component to which each fault mode belongs by extracting the module information and the propagation relationship between the modules in the signal flow graph. By mapping the node and the system name, the step binds each fault mode to the system (component) to which it belongs. If a fault mode node is not mapped to the system, it means that it is not associated with a component. The generated ontology knowledge structure is in the form of fault mode: component name, which is convenient for excluding the fault mode of non-target components in the screening stage.

[0028] 1.5. Comprehensive Extraction of Structured Knowledge

[0029] Through the above steps, the multi-signal flow graph structured knowledge extraction step based on ontology constraints converts the multi-signal flow graph into three ontology knowledge structures through recursion and screening rules. Among them, the fault mode-fault mode directly extracts the connection relationship through the edge list; the fault mode-signal uses the measurement point information to associate the signal; the fault mode-component associates the component according to the system mapping.

[0030] 2. Basic failure mode screening based on propagation relationship

[0031] The basic fault mode screening step based on the propagation relationship simplifies the fault mode set through the screening rules based on the propagation relationship, and mainly includes five steps: initialization of screening conditions, screening based on the fault mode-component table, screening based on the fault mode-signal table, screening based on the fault mode-fault mode table, and comprehensive screening results. The flowchart of the basic fault mode screening method based on the propagation relationship is as follows: Figure 3 shown.

[0032] 2.1. Initialization of screening conditions

[0033] The initialization step of the screening condition reads the structured knowledge, namely, fault mode-fault mode (propagation relationship table between fault modes), fault mode-signal (association table between fault mode and signal), and fault mode-component (association table between fault mode and component).

[0034] 2.2. Filtering based on failure mode-parts list

[0035] In the screening step based on the fault mode-component table, if the fault mode does not belong to the target component, it is deleted from the candidate set. In the specific implementation, it is necessary to traverse the candidate fault mode set and find the corresponding component information for each fault mode. If the component of the fault mode is not within the target component range, the fault mode is deleted.

[0036] 2.3. Screening based on the failure mode-signal table

[0037] In the screening step based on the fault mode-signal table, if the fault mode does not affect any signal, it is considered a redundant fault mode and should be removed from the candidate set. In a specific implementation, for each candidate fault mode, its corresponding signal impact list fault mode-signal is searched. If the signal list is empty, it means that the fault mode does not affect any signal, and the fault mode is deleted.

[0038] 2.4. Screening based on failure mode-failure mode table

[0039] In the screening step based on the fault mode-fault mode table, if a certain fault mode has no downstream propagation relationship (i.e., it does not affect other fault modes) and does not directly or indirectly affect the propagation path of the target fault mode, it is considered a redundant fault mode; if a certain fault mode has multiple downstream fault modes, but no binding signal, and the fault mode does not affect the target fault mode, then the fault mode can be deleted. In the specific implementation, it is necessary to traverse the candidate fault mode set and check the downstream fault mode information in the fault mode-fault mode table. If a certain fault mode has no downstream fault mode and is not on any impact path, remove the fault mode; if a certain fault mode has multiple downstream fault modes and no binding signal, and its downstream mode has no effect on the target mode, delete the mode.

[0040] 2.5. Comprehensive screening results

[0041] In the comprehensive screening result step, the screened failure mode set will be returned as the screened "basic failure mode set". These failure modes are directly related to the target components, signals and propagation paths. This set will serve as the basic data for the next FMECA generation.

[0042] 3. Automatic generation of FMECA based on structured knowledge

[0043] The automatic generation of FMECA based on structured knowledge is based on the screened set of fault modes and combines three ontology knowledge structures to generate standardized FMECA items. It mainly includes three steps: preprocessing of ontology knowledge structure, automatic filling of FMECA items, and merging and generating FMECA files. The flow chart of the automatic generation method of FMECA based on structured knowledge is as follows: Figure 4 shown.

[0044] 3.1. Preprocessing of ontology knowledge structure

[0045] The preprocessing step of the ontology knowledge structure first traverses each system configuration and creates a mapping table from node number to system name for each node, so that the system name to which it belongs can be quickly located by node number when generating the FMECA table; then, according to the type of node, the valid nodes and measuring point nodes are screened out and the valid node graph and valid measuring point graph are generated. The valid node graph contains all valid node numbers (fault nodes and test nodes), and the valid measuring point graph only contains measuring point node numbers (test nodes and switch nodes).

[0046] 3.2.Automatic filling of FMECA items

[0047] The automatic filling step of the FMECA entry first traverses each node. If the node type is a faulty node, the number of all the measuring point nodes is recursively extracted from the edges associated with the current node using the function of obtaining the measuring point number, and the text information is obtained. The detection method is represented by the text of the measuring point node; then, the upstream connection edge of the current node is traversed, and the text of the upstream node (which is also a faulty node) is extracted as the cause of the fault; finally, the downstream nodes of the current faulty node are traversed, and multi-level influences (local influence, higher-level influence, and final influence) are extracted recursively. Among them, the hierarchical relationship is recursive level by level, and at most three levels of influence are recorded. If there is no downstream node at a certain level, it is filled with an empty string.

[0048] 3.3. Merge generation of FMECA documents

[0049] The merging and generating step of the FMECA file saves all analysis results and calls the merge function to generate a formatted Excel table. When outputting, cells with duplicate contents are merged and formatted in the center. The FMECA table fills in the structured entry information formed in the previous step in accordance with the filling specifications shown in Table 1. The FMECA formatted Excel table synthesizes the filled entry information according to the specifications of Table 2 to generate an Excel table.

[0050] Table 1 FMECA table column definition and content

[0051]

[0052] Table 2FMECA example

[0053]

[0054] After the export is completed, the FMECA automated construction method based on the multi-signal flow graph is completed.

Claims

1. A FMECA automated construction method based on a multi-signal flow graph, characterized in that: The FMECA automatic construction method realizes efficient automatic generation and dynamic evolution of FMECA items through ontology knowledge extraction and automatic fault mode screening of multi-signal flow graphs; the FMECA automatic construction method includes a multi-signal flow graph structured knowledge extraction step based on ontology constraints, a basic fault mode screening step based on propagation relationships, and an FMECA automatic generation step based on structured knowledge.

2. The FMECA automated construction method according to claim 1, characterized in that: The multi-signal flow graph structured knowledge extraction step in the FMECA automated construction method constructs a correspondence method between the multi-signal flow graph structure information and the FMECA items through knowledge entities and associated ontology constraints, thereby breaking through the blind spots of the FMECA file caused by manual fault analysis based on the structural information of the multi-signal flow graph.

3. The FMECA automated construction method according to claim 1, characterized in that: The basic failure mode screening step in the FMECA automated construction method performs necessity screening on the failure entities in the graph structure through ontology analysis of the failure propagation structure in the multi-signal flow graph, thereby meeting the requirements of industrial scenarios for the simplification of entries in the FMECA file.

4. The FMECA automated construction method according to claim 1, characterized in that: The FMECA automatic generation step in the FMECA automatic construction method realizes the automatic construction function of FMECA files based on multi-signal flow graphs through an automated structured knowledge filling method, thereby realizing the dynamic evolution of FMECA files in the whole life cycle of the product by means of the dynamic evolution characteristics of the multi-signal flow graph.

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