CN111931936A - Equipment fault diagnosis method based on collaborative case reasoning and semantic model reasoning - Google Patents

Equipment fault diagnosis method based on collaborative case reasoning and semantic model reasoning Download PDF

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CN111931936A
CN111931936A CN202010553423.1A CN202010553423A CN111931936A CN 111931936 A CN111931936 A CN 111931936A CN 202010553423 A CN202010553423 A CN 202010553423A CN 111931936 A CN111931936 A CN 111931936A
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fault diagnosis
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刘立
刘子文
韩光洁
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Changzhou Campus of Hohai University
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Abstract

The invention discloses an equipment fault diagnosis method based on collaborative case reasoning and semantic model reasoning, which comprises the following steps: s1, collecting cases and constructing a case library; s2, establishing a fault diagnosis ontology model by a fuzzy ontology development methodology process by combining knowledge extracted by fuzzy logic and FMEA analysis; and S3, generating a corresponding SWRL rule by combining with expert experience on the basis of the knowledge obtained in the ontology model, and performing conflict detection on the generated SWRL rule to form a fault diagnosis rule base. S4 carries out fault detection according to the fault diagnosis ontology model, the rule base and the case base constructed above. On the basis of combining the CBR and the RBR, knowledge extracted by fuzzy logic and FMEA analysis is fused into the construction of the body model, so that the integrity of the body model is improved, and the definition of uncertain knowledge is more reasonable; meanwhile, the shallow knowledge and the deep knowledge are utilized to construct the diagnosis rule, and the integrity and the accuracy of the rule base are improved, so that the reasoning performance of a diagnosis framework is improved.

Description

Equipment fault diagnosis method based on collaborative case reasoning and semantic model reasoning
Technical Field
The invention relates to an equipment fault diagnosis method based on collaborative case reasoning and semantic model reasoning, and belongs to the technical field of fault diagnosis and information in the industrial Internet of things.
Background
The semantic model-based fault diagnosis method has a very wide application range in the background of the industrial Internet of things. The ontology model can integrate, share and reuse knowledge, overcomes the problem of data heterogeneity, can generate diagnosis rules according to the model, and can be combined with case-based reasoning (CBR), thereby improving the diagnosis efficiency. Depending on the mode of operation, the devices may be divided into different classes (e.g., devices, systems, components, parts) and a failure at one class may affect the mode of operation at another class. For such information, failure mode and impact analysis (FMEA) is an effective extraction method. Because FMEA has the problem that knowledge is difficult to obtain and expression modes are inconsistent, the integration of the extracted knowledge into the ontology model is an effective solution, but some problems still exist:
1) the fault diagnosis accuracy rate based on the ontology model depends on the completeness of the model and the reasonability of the generation rule, and needs the support of expert experience analysis (shallow knowledge) and diagnosis object structure and data (deep knowledge);
2) the rigid rule generated according to the ontology model may cause the problem that the reasoning result is inaccurate in the reasoning process.
Disclosure of Invention
In order to improve the integrity of a fault diagnosis ontology model, perfect diagnosis rules, improve problems caused by rigid rules and improve the accuracy of reasoning results and diagnosis, the invention provides the equipment fault diagnosis method for collaborative case reasoning and semantic model reasoning, which integrates knowledge extracted by fuzzy logic and FMEA (failure mode analysis) and constructs a fault diagnosis semantic model in an ontology form, and combines CBR (cubic boron reactor) into the model to improve the diagnosis efficiency.
The invention mainly adopts the technical scheme that:
a device fault diagnosis method for collaborative case reasoning and semantic model reasoning comprises the following steps:
s1, collecting cases and constructing a case library;
s2, establishing a fault diagnosis ontology model by a fuzzy ontology development methodology process by combining knowledge extracted by fuzzy logic and FMEA analysis;
s3, generating a corresponding SWRL rule by combining with expert experience on the basis of the knowledge obtained in the ontology model, and performing conflict detection on the generated SWRL rule to form a fault diagnosis rule base;
s4 carries out fault detection according to the fault diagnosis body model, rule base and case base, the detection process is divided into a CBR module and an RBR module, wherein the CBR module comprises the body model and the case base, fault diagnosis is carried out by reusing experience of historical cases, and the RBR module comprises the body model and the rule base and is used for carrying out fault diagnosis through SWRL rule reasoning when the CBR module fails, namely when case matching similarity is lower than a threshold value.
Preferably, in the fault diagnosis ontology model construction in step S2, a fuzzy logic and FMEA analysis method are combined, and an ontology is developed by using a fuzzy ontology development methodology flow, and the specific steps are as follows:
s2-1: obtaining the relation between the equipment and the components at the same level or different levels in the diagnostic object by using an FMEA (failure mode and effects analysis) method;
s2-2: and for the part with uncertainty, defining the fuzzy requirement degree of the fuzzy ontology in the development methodology process of the fuzzy ontology, and integrating the knowledge into the fault diagnosis ontology model to improve the relationship integrity degree of the classes and the attributes of the fault diagnosis ontology model.
Preferably, in step S4, the specific process of fault detection is as follows:
s4-1: selecting parameter characteristics in the new case;
s4-2: firstly, executing a CBR module, based on a fault diagnosis body model, carrying out case retrieval in a case base according to the parameter characteristics selected in the step S4-1, if the parameter characteristics are successfully matched with the cases in the case base, adopting the cases, using the selected parameter characteristics and a fault maintenance method in the matching cases as a new case, evaluating the similarity between the new case and the matching cases, if the similarity is lower than a threshold value, storing the new case in the case base for case updating, if the similarity is higher than the threshold value, not updating, finishing the CBR module after the evaluation is finished, and finishing fault diagnosis; if the matching of the parameter characteristics and the cases in the case base fails, ending the CBR module and entering the step S4-3;
s4-3, executing an RBR module, judging the fault reason, fault position and maintenance measure according to the SWRL rule in the rule base based on the fault diagnosis body model, completing fault diagnosis, and adding the case into the case base for updating; if a new fault type occurs, the fault type is primarily diagnosed through the SWRL rule in the rule base, and if the diagnosis result is valid, the case is added into the case base for updating.
Preferably, the rule base is added to the rule base for updating after performing rule conflict detection according to rules newly generated by expert experience or actual case analysis.
Has the advantages that: the invention provides an equipment fault diagnosis method for collaborative case reasoning and semantic model reasoning, which improves the integrity of a body model by fusing knowledge extracted by fuzzy logic and FMEA analysis into the construction of the body model on the basis of the combination of CBR and RBR, and is more reasonable for the definition of uncertain knowledge; meanwhile, the shallow knowledge and the deep knowledge are utilized to construct the diagnosis rule, and the integrity and the accuracy of the rule base are improved, so that the reasoning performance of a diagnosis framework is improved.
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FIG. 1 is a flow chart of fault diagnosis;
FIG. 2 is a process for constructing an ontology model and a rule base for an RBR module;
FIG. 3 is a workflow of the CBR module.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 1, a method for diagnosing equipment failure by coordinating case reasoning and semantic model reasoning includes the following steps:
s1, collecting cases and constructing a case library;
s2, establishing a fault diagnosis ontology model by a fuzzy ontology development methodology process by combining knowledge extracted by fuzzy logic and FMEA analysis;
and S3, generating a corresponding SWRL rule by combining with expert experience on the basis of the knowledge obtained in the ontology model, and performing conflict detection on the generated SWRL rule to form a fault diagnosis rule base. (the construction of the rule base belongs to the prior art, and the rules are manually edited according to the structure and the relationship after the model construction is finished, so that the detailed description is not added).
S4 carries out fault detection according to the fault diagnosis body model, rule base and case base, the detection process is divided into a CBR module and an RBR module, wherein the CBR module comprises the body model and the case base, fault diagnosis is carried out by reusing experience of historical cases, and the RBR module comprises the body model and the rule base and is used for carrying out fault diagnosis through SWRL rule reasoning when the CBR module fails, namely when case matching similarity is lower than a threshold value.
Preferably, as shown in fig. 2, in the fault diagnosis ontology model building in step S2, a fuzzy logic and FMEA analysis method are combined, and a fuzzy ontology development methodology flow is adopted to develop an ontology, which includes the following specific steps:
s2-1: obtaining the relation between the equipment and the components at the same level or different levels in the diagnostic object by using an FMEA (failure mode and effects analysis) method;
s2-2: and for the part with uncertainty, defining the fuzzy requirement degree of the fuzzy ontology in the development methodology process of the fuzzy ontology, and integrating the knowledge into the fault diagnosis ontology model to improve the relationship integrity degree of the classes and the attributes of the fault diagnosis ontology model. The knowledge, namely the definition of the uncertain part in the Fuzzy Ontology Development Methodology (FODM) flow, is integrated into the definition of fuzzy related classes and parameters in a fault diagnosis ontology model.
Preferably, in step S4, as shown in fig. 3, the specific process of fault detection is as follows:
s4-1: selecting parameter characteristics in the new case;
s4-2: firstly, executing a CBR module, based on a fault diagnosis body model, carrying out case retrieval in a case base according to the parameter characteristics selected in the step S4-1, if the parameter characteristics are successfully matched with the cases in the case base, adopting the cases, using the selected parameter characteristics and a fault maintenance method in the matching cases as a new case, evaluating the similarity between the new case and the matching cases, if the similarity is lower than a threshold value, storing the new case in the case base for case updating, if the similarity is higher than the threshold value, not updating, finishing the CBR module after the evaluation is finished, and finishing fault diagnosis; if the matching of the parameter characteristics and the cases in the case base fails, ending the CBR module and entering the step S4-3;
s4-3: executing an RBR module, judging the fault reason, fault position and maintenance measure according to the SWRL rule in the rule base based on the fault diagnosis body model, completing fault diagnosis, and adding the case into the case base for updating; if a new fault type occurs, the fault type is primarily diagnosed through the SWRL rule in the rule base, and if the diagnosis result is valid, the case is added into the case base for updating.
Preferably, the rule base is added to the rule base for updating after performing rule conflict detection according to rules newly generated by expert experience or actual case analysis.
According to the invention, CBR and RBR modules are called to carry out fault diagnosis, and relevant information of faults is determined by obtaining feedback through input parameters such as fault modes and relevant data values.
1. Construction of an ontology model
For the construction of the ontology model, a flow of Fuzzy Ontology Development Methodology (FODM) is adopted. Firstly, extracting the relation among equipment, a system and components and related parameter values according to an FMEA (failure mode analysis) method, and taking the relation and the related parameter values as a construction basis of classes, object attributes and data attributes in a body model, wherein the construction basis mainly comprises a fault mode number, a fault mode, equipment components, fault influence, fault reasons and maintenance measures. Defining the obtained information in the ontology as related classes + + and attributes, extracting FMEA knowledge, and constructing an ontology model taking ambiguity into consideration by the following steps:
1) the purpose and scope of the ontology are determined, basic questions are presented and answers are made explicitly.
2) Requirement for determining ambiguity: and further identifying the domain or range of the ontology, extracting the definite degree of knowledge according to FMEA, determining whether to introduce ambiguity into the ontology design, and identifying different types of ambiguities according to the definition of the fuzzy ontology.
3) Determining the fuzzy related information: further investigation is performed on the part requiring the fuzzy definition, information really requiring the fuzzy definition (ambiguity is not required if there is accurate information) is identified, and a knowledge base in the expected field (i.e. the equipment actually subjected to fault diagnosis) is divided into the accurate information and the fuzzy information.
4) Consider reusing an existing ontology: existing ontologies relating to a domain or scope are examined and their reusability determined.
5) Reusing fuzzy ontology elements: checking the reusable ontology, determining whether the selected ontology elements are fuzzy, if only the clear ontology elements are reusable, entering step 7, and if only the fuzzy ontology elements are reusable, entering step 6; if both are present, steps 6 and 7 are activated simultaneously.
6) Correct fuzzy ontology elements: the ambiguity specification and modeling provided by the existing fuzzy ontology element may not be matched, and the fuzzification of the ontology element needs to be perfected to adapt to the requirements of a target ontology, so that correct approximation of information with fuzzy meaning in the expected field or application is ensured.
7) Defining fuzzy ontology elements: different fuzzy ontology elements are defined according to professional knowledge or historical statistical data to provide a correct approximation of the nature of fuzzy and inaccurate information in the domain.
8) Defining a clear ontology element: a combination of top-down and bottom-up methods is used to develop class hierarchies, define relationships (object properties) to link different concepts (classes), and develop data properties, axioms, and clear ontology elements of instances.
9) Normalization: the ontology elements designed in step 8 are formalized into a machine-readable format.
10) And (3) verification: and verifying the designed ontology according to correctness, consistency, completeness, rationality, understandability and simplicity.
In addition, the case base is built in the form of an ontology, relevant classes and attributes are created and are merged into an ontology model of fault diagnosis to support the operation of the CBR module.
CBR Module
In the event of a failure, the parameters obtained from the data acquisition device will change. Case retrieval refers to searching a case base according to parameter characteristics (such as equipment type, fault phenomenon and operation parameters). The case base is a set of fault diagnosis cases constructed by filing corresponding data for historical cases according to the categories and attributes of the fault diagnosis ontology. The onto-model can be retrieved using semantically constructed cases, case matching being achieved by matching classes defined by the features, actual data values of parameters corresponding to feature indices between the onto-model and the cases. The similarity value is used for judging the similarity between the fault to be diagnosed and the cases in the case base, if the similarity is larger than a set threshold value, the result can be considered to be adopted, and after case matching is successfully completed, the actual fault diagnosis result can be stored in the case base.
RBR Module
When the CBR module does not successfully match the case, the RBR module is triggered. And forming a rule base of the fault diagnosis model according to the knowledge acquired from the ontology model and the SWRL rule generated by expert experience. According to the fault parameters such as fault modes and detected elements such as related data attribute values, related rules in the rule base are triggered to carry out reasoning so as to achieve the purpose of fault diagnosis, the reasoning result shows possible fault reasons, and corresponding maintenance measures are given. And for the fault with unknown type, carrying out preliminary reasoning according to the class and attribute relation in the body and the SWRL rule. If the inferred result meets the fact, the result can be stored in the case base. In addition, the rule base can be continuously improved, and the newly generated rules according to expert experience or actual case analysis can be added into the rule base after rule conflict detection is carried out.
The expert experience referred to in the present invention is knowledge on the structure of the equipment obtained by analyzing the equipment, or judgment of a fault, and belongs to the technical term.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (4)

1. A device fault diagnosis method based on collaborative case reasoning and semantic model reasoning is characterized by comprising the following steps:
s1, collecting cases and constructing a case library;
s2, establishing a fault diagnosis ontology model by a fuzzy ontology development methodology process by combining knowledge extracted by fuzzy logic and FMEA analysis;
s3, generating a corresponding SWRL rule by combining with expert experience on the basis of the knowledge obtained in the ontology model, and performing conflict detection on the generated SWRL rule to form a fault diagnosis rule base;
s4 carries out fault detection according to the fault diagnosis body model, rule base and case base, the detection process is divided into a CBR module and an RBR module, wherein the CBR module comprises the body model and the case base, fault diagnosis is carried out by reusing experience of historical cases, and the RBR module comprises the body model and the rule base and is used for carrying out fault diagnosis through SWRL rule reasoning when the CBR module fails, namely when case matching similarity is lower than a threshold value.
2. The method for diagnosing equipment faults by coordinating case reasoning and semantic model reasoning with claim 1, wherein: in the fault diagnosis ontology model construction of the step S2, a fuzzy logic and FMEA analysis method are combined, and an ontology is developed by adopting a fuzzy ontology development methodology flow, which includes the following specific steps:
s2-1: obtaining the relation between the equipment and the components at the same level or different levels in the diagnostic object by using an FMEA (failure mode and effects analysis) method;
s2-2: and for the part with uncertainty, defining the fuzzy requirement degree of the fuzzy ontology in the development methodology process of the fuzzy ontology, and integrating the knowledge into the fault diagnosis ontology model to improve the relationship integrity degree of the classes and the attributes of the fault diagnosis ontology model.
3. The method for diagnosing equipment faults by coordinating case reasoning and semantic model reasoning with claim 1, wherein: in step S4, the specific process of fault detection is as follows:
s4-1: selecting parameter characteristics in the new case;
s4-2: firstly, executing a CBR module, based on a fault diagnosis body model, carrying out case retrieval in a case base according to the parameter characteristics selected in the step S4-1, if the parameter characteristics are successfully matched with the cases in the case base, adopting the cases, using the selected parameter characteristics and a fault maintenance method in the matching cases as a new case, evaluating the similarity between the new case and the matching cases, if the similarity is lower than a threshold value, storing the new case in the case base for case updating, if the similarity is higher than the threshold value, not updating, finishing the CBR module after the evaluation is finished, and finishing fault diagnosis; if the matching of the parameter characteristics and the cases in the case base fails, ending the CBR module and entering the step S4-3;
s4-3: executing an RBR module, judging the fault reason, fault position and maintenance measure according to the SWRL rule in the rule base based on the fault diagnosis body model, completing fault diagnosis, and adding the case into the case base for updating; if a new fault type occurs, the fault type is primarily diagnosed through the SWRL rule in the rule base, and if the diagnosis result is valid, the case is added into the case base for updating.
4. The method for diagnosing equipment faults by coordinating case reasoning and semantic model reasoning with claim 1, wherein: and the rule base is added into the rule base for updating after rule conflict detection is carried out according to the newly generated rule based on expert experience or actual case analysis.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112396197A (en) * 2020-12-18 2021-02-23 三门核电有限公司 Equipment fault diagnosis system and method based on information fusion technology
CN113011726A (en) * 2021-03-08 2021-06-22 浙江大学 Intelligent analysis and processing system for quality data in tractor manufacturing process
CN113128878A (en) * 2021-04-23 2021-07-16 中国人民解放军陆军工程大学 Equipment guarantee simulation rule construction method and terminal equipment
CN114254099A (en) * 2021-11-03 2022-03-29 北京思特奇信息技术股份有限公司 Automatic processing recommendation method and system for fault work order and electronic equipment
CN115329774A (en) * 2022-10-14 2022-11-11 中国建筑科学研究院有限公司 Intelligent building fault diagnosis rule generation method and device based on semantic matching

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112396197A (en) * 2020-12-18 2021-02-23 三门核电有限公司 Equipment fault diagnosis system and method based on information fusion technology
CN113011726A (en) * 2021-03-08 2021-06-22 浙江大学 Intelligent analysis and processing system for quality data in tractor manufacturing process
CN113011726B (en) * 2021-03-08 2024-02-02 浙江大学 Intelligent analysis processing system for quality data in tractor manufacturing process
CN113128878A (en) * 2021-04-23 2021-07-16 中国人民解放军陆军工程大学 Equipment guarantee simulation rule construction method and terminal equipment
CN114254099A (en) * 2021-11-03 2022-03-29 北京思特奇信息技术股份有限公司 Automatic processing recommendation method and system for fault work order and electronic equipment
CN115329774A (en) * 2022-10-14 2022-11-11 中国建筑科学研究院有限公司 Intelligent building fault diagnosis rule generation method and device based on semantic matching

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Application publication date: 20201113