Knowledge graph-based mechanical fault diagnosis knowledge base construction method

Abstract

The invention discloses a knowledge graph-based mechanical fault diagnosis knowledge base construction method, and belongs to the field of mechanical fault diagnosis. A mechanical fault diagnosis knowledge base reflects fault generation essences and domain expert experiences; and through a knowledge processing module, the fault generation essences and the domain expert experiences are stored in the knowledge base, thereby providing support for mechanical fault diagnosis. A conventional knowledge graph is represented in a network form; nodes represent entities; connection lines represent relationships; and for the representation form, a special graph algorithm needs to be designed for storing and utilizing a database, so that the disadvantage of time and labor waste exists. According to a representation learning technology represented by deep learning, a triple object is mapped to a vector space and represented as a dense low-dimensional vector, and efficient calculation and reasoning are realized through vector conversion. The knowledge graph-based mechanical fault diagnosis knowledge base construction method is established; mechanical fault diagnosis knowledge is represented as atriple, and the tripe is represented as the vector by utilizing a TransD model, so that the problems of inaccurate case representation, difficult maintenance and modification, low reasoning and calculation efficiency and the like of a conventional knowledge base can be optimized; and the method has important significance for the field of fault diagnosis.

Description

technical field [0001] The invention belongs to the field of mechanical fault diagnosis and relates to the construction of a knowledge base in the field of mechanical fault diagnosis, including a construction method and a knowledge representation method of the mechanical fault diagnosis knowledge base. Background technique [0002] Mechanical fault diagnosis is a rapidly developing subject in petroleum, chemical and manufacturing industries, and it is also a key research topic for the intelligent transformation of my country's manufacturing industry. Many staff and researchers have made a lot of explorations in this research. At present, the condition monitoring system has been widely used, but these data are complicated and have no organic connection with each other, and most of them are unstructured data, which exist in various fault diagnosis reports and fault case databases. How to integrate and screen these data , Extract useful information from it, and use knowledge gr...

AI Technical Summary

Problems solved by technology

The present invention proposes a method for constructing a mechanical fault diagnosis knowledge base based on knowledge graph correlation theory. The traditional knowledge graph is expressed in the form of a network, nodes represent entities, and connections represent relationships. This representation requires the design of a special graph algorithm for storage and Using the database, there are time-consuming and labor-intensive points. The representation learning technology represented by deep learning maps the triplet object to the vector space, expresses it as a dense low-dimensional vector, and realizes efficient calculation and reasoning through vector transformation.

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