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

Abstract

The invention discloses an equipment fault diagnosis method based on collaborative case-based reasoning and semantic model-based reasoning, which comprises the following steps of S1, collecting cases,and constructing a case library; S2, combining fuzzy logic and knowledge extracted by an FMEA analysis method, and constructing a fault diagnosis ontology model by a fuzzy ontology development methodology process; and S3, on the basis of knowledge obtained in the ontology model, generating a corresponding SWRL rule in combination with expert experience, and performing conflict detection on the generated SWRL rule to form a fault diagnosis rule base; S4, performing fault detection according to the constructed fault diagnosis ontology model, rule base and case base. According to the method, onthe basis of combination of CBR and RBR, knowledge extracted by fuzzy logic and an FMEA analysis method is fused into construction of the ontology model, so that the integrity of the ontology model isimproved, and definition of uncertain knowledge is more reasonable; meanwhile, diagnosis rules are constructed by utilizing shallow knowledge and deep knowledge, and the integrity and accuracy of a rule base are improved, so that the reasonability of a diagnosis framework is improved.

Description

technical field [0001] The invention relates to a device fault diagnosis method for collaborative case reasoning and semantic model reasoning, which belongs to the field of fault diagnosis and information technology in the Industrial Internet of Things. Background technique [0002] Semantic model-based fault diagnosis methods have a wide range of applications in the context of the Industrial Internet of Things. Among them, the ontology model can integrate, share and reuse knowledge, overcome the problem of data heterogeneity, and can generate diagnostic rules according to the model, and can be combined with case-based reasoning (CBR) to improve diagnostic efficiency. According to the working mode, the equipment can be divided into different levels (such as equipment, system, component, part), and the failure of a certain level may affect the working mode of other levels. For such information, failure mode and effects analysis (FMEA) is an effective extraction method. Sinc...

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Problems solved by technology

[0005] In order to improve the integrity of the fault diagnosis ontology model, improve the diagnosis rules, improve the problems caused by rigid rules, and improve the accuracy of reasoning results and diagnosis, the present invention provides a device fault diagnosis method that cooperates with case reasoning and semantic model reasoning. Based on the knowledge extracted from fuzzy logic and FMEA analysis, a fault diagnosis semantic model is constructed in the form of ontology, and CBR is combined into the model to improve diagnosis efficiency

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