Related fault diagnosis reasoning method and system based on TransH model

Abstract

The invention discloses an associated fault diagnosis reasoning method and system based on a TransH model, and belongs to the technical field of fault prediction. The method comprises the following steps: sorting a fault mode aiming at each fault phenomenon of a system according to expert experience, dividing each fault mode into a plurality of fault elements, sorting a fault reason aiming at each fault element, and constructing an initial fault knowledge graph according to the fault phenomena, the fault elements and the fault reasons; utilizing a TransH model to carry out vectorization on the relationship and the edge in the initial fault knowledge graph to obtain a fault vector knowledge graph; obtaining a to-be-detected fault phenomenon; according to the vector value of the to-be-detected fault phenomenon, calculating the similarity between the to-be-detected fault phenomenon and an entity in a fault vector knowledge graph, the entity set which is directly connected with the entities in the fault vector knowledge graph through the relationship and has the highest similarity with the fault phenomenon to be detected is regarded as the one-hop entity set which is connected with the fault phenomenon to be detected through the relationship. According to the invention, comprehensive fault reasons for fault phenomena can be diagnosed.

Description

Technical Field

[0001] The invention relates to a TransH model-based associative fault diagnosis reasoning method and system, belonging to the technical field of fault prediction and health management. Background Art

[0002] In the field of aerospace, with the continuous improvement of equipment functions and effectiveness, the structure and function of avionics equipment have become more and more complex, and the difficulty of maintenance and repair of avionics equipment has also increased significantly. Therefore, in order to ensure the normal operation of aviation equipment and improve the reliability of aviation equipment, it has become one of the important research topics to locate and solve the faults in a timely and accurate manner and to predict the faults in time before the faults occur in order to ensure the normal operation of aviation equipment and improve the reliability of aviation equipment.

[0003] The Chinese invention patent application with publication number CN108509483A discloses a method for constructing a mechanical fault diagnosis knowledge base based on a knowledge graph, which includes the following steps: 1) Knowledge collection and organization: Mechanical fault diagnosis knowledge exists in various structured and unstructured diagnostic reports and case libraries, and these contents need to be uniformly collected and organized as the basis for knowledge construction; 2) Constructing a triple data set: Based on the construction mode of the knowledge graph, the collected and organized fault diagnosis knowledge is expressed in the form of entity relationship-entity triples to establish a knowledge graph for mechanical fault diagnosis; 3) Determining the embedding dimension: Determine the embedding dimension of the knowledge representation based on the content and scale of the data set; 4) Initializing the vector: Initialize the triplet and encode it into a vector of a certain dimension; 5) Constructing training samples: Use the correct triplet as a positive sampling sample, and replace the correct triplet head entity or tail entity to construct a negative sampling sample, and use the constructed sampling sample as the input of the training model; 6) Use SGD for training and learning: The training and learning obtain the triplet representation vector; 7) Use the test set to test the vectorized representation result of the triplet.

[0004] However, the technical solution disclosed in the patent application cannot find out all the causes of the fault according to the fault phenomenon. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the present invention provides an associative fault diagnosis reasoning method and system based on the TransH model, which decomposes the fault mode of each fault phenomenon into multiple fault elements, and queries multiple fault causes for each fault element, so as to diagnose comprehensive fault causes for the fault phenomenon.

[0006] To achieve the above-mentioned object of the invention, the present invention provides a correlation fault diagnosis reasoning method based on the TransH model, which comprises the following steps: Step 1: Based on expert experience, sort out the fault mode for each fault phenomenon of the system, divide each fault mode into multiple fault elements, sort out the fault cause for each fault element, take the fault phenomenon, fault element and fault cause as entities, and use the relationship between them as edges to build the initial fault knowledge graph; Step 2: Use the TransH model to vectorize the entities and edges in the initial fault knowledge graph to obtain the fault vector knowledge graph; Step 3: Obtain the fault phenomenon to be detected, and query the vector value of the fault phenomenon from the mapping table between entities and vectors; Step 4: Divide the entities in the fault vector knowledge graph into multiple regions according to the related fault phenomena, fault elements and fault causes, calculate the similarity between the fault phenomenon to be detected and the entities in multiple regions in parallel, obtain the entities most similar to the fault phenomenon in multiple regions respectively, and obtain multiple better similarities; sort the multiple better similarities, obtain the entities and regions with the maximum similarity, and the entity set directly connected by the edges of the entities with the maximum similarity is regarded as the one-hop entity set connected by the relationship of the fault phenomenon to be detected.

[0007] To achieve the objective of the invention, the present invention also provides a system, which includes a storage medium and one or more processors, wherein the storage medium stores a computer program, and the computer program is called by one or more processors to implement the above-mentioned TransH model-based associative fault diagnosis reasoning method.

[0008] Compared with the prior art, the TransH model-based associative fault diagnosis reasoning method and system provided by the present invention decomposes the fault mode of each fault phenomenon into multiple fault elements, and queries multiple fault causes for each fault element, so as to diagnose comprehensive fault causes for the fault phenomenon and provide a comprehensive reference for maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a flow chart of the associated fault diagnosis reasoning method based on the TransH model provided by the present invention. DETAILED DESCRIPTION

[0010] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0011] Figure 1 is a flow chart of the associated fault diagnosis reasoning method based on the TransH model provided by the present invention, such as Figure 1 As shown, the associated fault diagnosis reasoning method based on the TransH model provided by the present invention includes the following steps: Step 1: Sort out each fault phenomenon of the system based on expert experience Failure modes are divided into multiple failure elements. , sort out the fault elements Multiple causes of failure , the fault phenomenon , Fault element and cause of failure As an entity, the fault phenomenon and fault element relationship, fault element and cause of failure The relationship of is used as an edge to construct an initial fault knowledge graph, where M, N and J are positive integers greater than or equal to 2; Step 2: Use the TransH model to vectorize the relationships and edges in the initial fault knowledge graph to obtain a fault vector knowledge graph; Step 3: Obtain the fault phenomenon to be detected and query the vector value of the fault phenomenon from the mapping table ; Step 4: According to the vector value of the fault phenomenon to be detected , calculate the similarity between the fault phenomenon to be detected and the entities in the fault vector knowledge graph, and the entity set directly connected to the entities in the fault vector knowledge graph with the highest similarity to the fault phenomenon to be detected through a relationship is regarded as the one-hop entity set connected to the fault phenomenon to be detected through a relationship, recorded as , wherein calculating the similarity between the fault phenomenon to be detected and the entities in the fault vector knowledge graph specifically includes the following steps: dividing the entities in the fault vector knowledge graph into P regions according to the related fault phenomena, fault elements and fault causes, and calculating the similarity between the fault phenomenon to be detected and the entities in the P regions in parallel, respectively obtaining the entities in the P regions that are most similar to the fault phenomenon, and obtaining P better similarities; sorting the P better similarities, obtaining the entity and region with the maximum similarity, K and P are positive integers greater than or equal to 2.

[0012] The associated fault diagnosis reasoning method based on the TransH model provided by the present invention also includes the following steps: Step 5: Query the fault vector knowledge graph for the one-hop entity set Each entity in The entities directly connected by the edge are regarded as the two-hop entity set of the fault phenomenon to be detected. , In the formula, Representation and Entity Subtract entities from entities that are directly connected by edges The obtained entity set is the entity set obtained by subtracting the backflow entities from the entities in the two-hop entity set and the entities in the one-hop entity set that are directly connected through edges.

[0013] By analogy, query the fault vector knowledge graph with r-1 hop entity set E r Each entity in the is directly connected to the entity through the edge minus the return flow entity as the r-hop entity set of the fault phenomenon to be detected. After multiple reasonings, the comprehensive fault cause for the fault phenomenon can be diagnosed through the fault vectorization knowledge graph.

[0014] The associated fault diagnosis reasoning method based on the TransH model provided by the present invention also includes the following steps: Step 6: Derive the corresponding relationship between entities, relations and vectors in the fault vector knowledge graph to obtain a mapping table

[0015] In the fault reasoning method of the TransH model provided by the present invention, the entities and edges in the initial fault knowledge graph are vectorized by using the TransH model to obtain the fault vector knowledge graph, which specifically includes: S1-1 uses the TransH model to perform initial quantization on the entities and edges in the initial fault knowledge graph to obtain the initial fault vector knowledge graph, which specifically includes: S1-1-1: The two entities and relations in the initial fault knowledge graph constitute a fault triple; S1-1-2: Extract all entities in the initial fault knowledge graph to form entity codes, extract all relations in the initial fault knowledge graph to form relation codes, and the size of the entity set is , the size of the relationship set is ; S1-1-3: Randomly initialize the entity embedding vector to obtain the entity embedding matrix : ; Randomly initialize the embedding vector of the relationship and obtain the relationship embedding matrix R: ; Initialize the hyperplane normal vector of each relationship and normalize it to obtain the normalized normal vector matrix W: , where d is the embedding dimension; S1-1-4: From entity embedding matrix Take out the head entity vector h and the tail entity vector , from the relation embedding matrix Extract the relationship vector , from the normal vector matrix take out Corresponding normal vector , calculate the projection of the head entity vector h and the tail entity vector t on the hyperplane according to the following formula: , , In the formula, T represents transpose; S1-1-5: Calculate the forward loss function according to the following formula : , In the formula, represents the two-norm, Represents the projection vector of vector h on the hyperplane; Represents the projection vector of vector t on the hyperplane; S1-1-6: Construct negative samples by randomly replacing the fault triple vector Generate a pseudo triplet vector from the head or tail entity vector in , , ; S1-1-7: Calculate the negative loss function according to the following formula : , In the formula, is the projection of the relationship vector r on the hyperplane; Representation vector Projection onto the hyperplane; Representation vector Projection onto the hyperplane; S1-1-8: Traverse the vector of fault triples in the initial fault knowledge graph False triple vectors in the collection and initial fault knowledge graph Set, use the following formula to find the first loss : , In the formula, represents the positive part; D is the set of real triples, is a set of false triples, is a hyperparameter; S1-1-9: Vector of fault triples in the initial fault knowledge graph and the false triple vector Perform the perturbation and calculate the second loss by the following formula: In the formula, is the disturbance amount; S1-1-10: Use the gradient descent method to update the values ​​of the head entity vector h, the tail entity vector t, and the relationship vector r, and determine the total loss Is it the smallest? If the total loss To achieve the minimum, use the vector of test fault triples Verification is performed and the entities and relationships in the initial fault knowledge graph are assigned fixed vectors to obtain the optimized entity embedding matrix. , relation embedding matrix and the normal matrix , and then obtain the initial fault vector knowledge graph; if the total loss L does not reach the minimum, the updated values ​​of the head entity vector h, the tail entity vector t and the relationship vector r are replaced by the updated values, and then return to step S1-1-4.

[0016] The present invention quantifies all entities and relationships in the initial fault knowledge graph through the above technical solution, and assigns a fixed vector to each entity and relationship in the initial fault knowledge graph, thereby laying a foundation for diagnosing comprehensive fault causes for fault phenomena.

[0017] In the present invention, using the TransH model to vectorize entities and edges in the initial fault knowledge graph to obtain a fault vector knowledge graph also includes: S1-2: Get any entity in the initial fault vector knowledge graph , whose vector is , the entity is calculated by the following formula Vector and other entities in the initial fault vector knowledge graph Vector The bi-norm of the distance of the projection onto the hyperplane: , In the formula, ; For vector Projection vector on the hyperplane; For vector Projection vector on the hyperplane; S1-3: Acquisition All entities that are less than the set value , , and will be and All entities smaller than the set value Connect them and calculate the relationship vector between them; S1-4: Repeat steps S1-2 and S1-3 to traverse all entities in the initial fault vector knowledge graph to obtain a first fault vector knowledge graph.

[0018] The present invention relates each fault phenomenon to multiple fault elements, and each fault element to multiple fault causes, so that comprehensive fault causes for the fault phenomenon can be diagnosed.

[0019] In the present invention, vectorizing entities and edges in the initial fault knowledge graph using the TransH model to obtain a fault vector knowledge graph also includes the following steps: S1-5: Get new fault symptoms , calculate the new fault phenomenon All entities in the knowledge graph with the first fault vector Similarity , , if the similarity If it is greater than or equal to the threshold, the fault phenomenon as entities and connected to them by edges and Connected entities are connected, and the entities The vector is adjusted according to the similarity as the fault phenomenon A vector that connects the entities The edge vectors of the entities connected to it are adjusted according to the similarity as a connection failure phenomenon and entities The vector of the edge of the connected entities; if the similarity Less than a threshold, a new fault mode for the new fault phenomenon is sorted out based on expert experience, the new fault mode is decomposed into multiple new fault elements, multiple new fault causes are sorted out for the new fault elements, and then a new initial fault knowledge graph is constructed, that is, the new fault phenomenon, the new fault element and the new fault cause are taken as entities, and the relationship between the new fault phenomenon and the new fault element, and the relationship between the new fault element and the new fault cause are taken as edges to construct a new initial fault knowledge graph; then the TransH model is used to vectorize the entities and edges of the new initial fault knowledge graph to obtain a second fault vector knowledge graph, and the first fault vector knowledge graph and the second fault vector knowledge graph are fused to obtain a fault vector knowledge graph.

[0020] In the present invention, fusing the first fault vector knowledge graph and the second fault vector knowledge graph to obtain the fault vector knowledge graph includes the following steps: S1-5-1: Get any entity in the first fault vector knowledge graph , whose vector is : Get any entity in the second fault vector knowledge graph , whose vector is ; The entity is calculated by Vectors and entities The bi-norm of the distance between the projection of a vector on the hyperplane: , In the formula, For vector Projection vector on the hyperplane; For vector Projection vector on the hyperplane; S1-5-2: Obtained from the second fault vector knowledge graph All entities less than the set value, get the entity set , , is the entity set size in the second fault knowledge graph, and and Connect all entities in and calculate the relationship vector between them; S1-5-3: Repeat steps S1-5-1 and S1-5-2, and traverse all entities in the first fault vector knowledge graph to obtain a fault vector knowledge graph.

[0021] The present invention can fuse multiple fault vector knowledge graphs greater than or equal to 2 through the above technical solution without having to re-quantize the spliced ​​fault knowledge graphs.

[0022] Optionally, in the associated fault diagnosis reasoning method based on the TransH model provided by the present invention, according to the vector value of the fault phenomenon to be detected , calculating the similarity between the fault phenomenon to be detected and the entity in the fault vector knowledge graph specifically includes: S2-1: The vector value of the fault phenomenon to be detected As the head entity is projected onto the hyperplane, we get , all entities in the fault vector knowledge graph are taken as tail entities, and the vector of the tail entity is projected onto the hyperplane as ; S2-2: Obtain the entity with the highest similarity to the fault phenomenon to be detected in the fault vector knowledge graph through the following formula: .

[0023] To achieve the objective of the invention, the present invention also provides a system, which includes a storage medium and one or more processors, wherein the storage medium stores a computer program, and the computer program is called by one or more processors to implement the above-mentioned TransH model-based associative fault diagnosis reasoning method.

[0024] To achieve the above-mentioned object of the invention, the present invention also provides a computer program product, which uses computer language to compile the above-mentioned TransH model-based associative fault diagnosis reasoning method into a computer program called and executed by one or more processors.

[0025] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can well understand and use the present invention. The technical solution formed by the partial combination of the steps disclosed in the present invention also belongs to the scope of the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A correlation fault diagnosis reasoning method based on TransH model, characterized in that: The steps include: Step 1: Based on expert experience, sort out the fault mode for each fault phenomenon of the system, divide each fault mode into multiple fault elements, sort out the fault cause for each fault element, and build an initial fault knowledge graph based on the fault phenomenon, fault element and fault cause; Step 2: Use the TransH model to vectorize the entities and edges in the initial fault knowledge graph to obtain the fault vector knowledge graph; Step 3: Obtain the fault phenomenon to be detected, and query the vector value of the fault phenomenon from the mapping table between entities and vectors; Step 4: Divide the fault phenomena, fault elements and fault causes related to the entities in the fault vector knowledge graph into multiple regions, calculate the similarity between the fault phenomenon to be detected and the entities in multiple regions in parallel, obtain the entities most similar to the fault phenomenon in multiple regions respectively, and obtain multiple better similarities; sort the multiple better similarities, obtain the entity with the maximum similarity and its region, and all entities directly connected to the entity with the maximum similarity through edges are regarded as the one-hop entity set connected to the fault phenomenon to be detected through edges.

2. The associated fault diagnosis reasoning method based on the TransH model according to claim 1 is characterized in that: The following steps are also included: Step 5: Query the fault vector knowledge graph for all entities directly connected to each entity in the one-hop entity set through edges as the two-hop entity set of the fault phenomenon to be detected. The two-hop entity set is the entity set obtained by subtracting the return flow entities from the entities directly connected to the entities in the one-hop entity set through edges.

3. The associated fault diagnosis reasoning method based on the TransH model according to claim 1 is characterized in that: The TransH model is used to vectorize the entities and edges in the initial fault knowledge graph to obtain the fault vector knowledge graph, which specifically includes: S1-1 uses the TransH model to initially quantify the relationships and edges in the initial fault knowledge graph to obtain the initial fault vector knowledge graph; S1-2: Get any entity in the initial fault vector knowledge graph , whose vector is , the entity is calculated by the following formula Vector and other entities in the initial fault vector knowledge graph Vector The bi-norm of the distance of the projection on the hyperplane, : ， In the formula, represents the two-norm, For vector Projection vector on the hyperplane; For vector Projection vector on the hyperplane; S1-3: Acquisition All entities that are less than the set value , , and will be and All entities smaller than the set value Connect them and calculate the relationship vector between them. is the size of the entity set in the initial vector knowledge graph; S1-4: Repeat steps S1-2 and S1-3 to traverse all entities in the initial fault vector knowledge graph to obtain a first fault vector knowledge graph.

4. The associated fault diagnosis reasoning method based on the TransH model according to claim 3 is characterized in that: Using the TransH model to vectorize the entities and edges in the initial fault knowledge graph to obtain the fault vector knowledge graph also includes the following steps: S1-5: Get new fault symptoms , calculate the new fault phenomenon Any entity in the knowledge graph with the first fault vector Similarity , ; If the similarity If it is greater than or equal to the threshold, the fault phenomenon as entities and connected to them by edges and Connected entities are connected, and the entities The vector is adjusted according to the similarity as the fault phenomenon A vector that connects the entities The edge vectors of the entities connected to it are adjusted according to the similarity as a connection failure phenomenon and entities The vector of the edge of the connected entities; if the similarity When the fault rate is less than the threshold, a new fault mode for the new fault phenomenon is sorted out based on expert experience, and the new fault mode is decomposed into multiple new fault elements. Multiple new fault causes are sorted out for the new fault elements, and then a new initial fault knowledge graph is constructed. Then, the TransH model is used to vectorize the entities and edges of the new initial fault knowledge graph to obtain the second fault vector knowledge graph.

5. The associated fault diagnosis reasoning method based on the TransH model according to claim 4 is characterized in that: Using the TransH model to vectorize entities and edges in the initial fault knowledge graph to obtain a fault vector knowledge graph also includes: fusing the first fault vector knowledge graph and the second fault vector knowledge graph to obtain a fault vector knowledge graph.

6. The associated fault diagnosis reasoning method based on the TransH model according to claim 5 is characterized in that: The steps of fusing the first fault vector knowledge graph and the second fault vector knowledge graph to obtain the fault vector knowledge graph include: S1-5-1: Get any entity in the first fault vector knowledge graph , whose vector is ; Get any entity in the second fault vector knowledge graph , whose vector is ; The entity is calculated by Vectors and entities The bi-norm of the distance between the projection of a vector on the hyperplane: ， In the formula, For vector Projection vector on the hyperplane; For vector Projection vector on the hyperplane; S1-5-2: Obtained from the second fault vector knowledge graph All entities less than the set value, get the entity set , , is the entity set size in the second fault knowledge graph, and and Connect all entities in and calculate the relationship vector between them; S1-5-3: Repeat steps S1-5-1 and S1-5-2, and traverse all entities in the first fault vector knowledge graph to obtain a fault vector knowledge graph.

7. The associated fault diagnosis reasoning method based on the TransH model according to claim 6 is characterized in that: The following steps are also included: Step 6: Derive the correspondence between entities, relations, and vectors in the fault vector knowledge graph to obtain a mapping table.

8. A system, characterized in that: It comprises a storage medium and one or more processors, wherein the storage medium stores a computer program, and the computer program is called by one or more processors to implement the associated fault diagnosis reasoning method based on the TransH model as described in any one of claims 1-7.

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