Intelligent equipment fault diagnosis method based on semantic reasoning

Abstract

The invention discloses an intelligent equipment fault diagnosis method based on semantic reasoning. According to the method, on the basis that a cloud distributed system is used for storing and rapidly processing mass manufacturing data generated by the intelligent equipment, firstly, a Profee graphical modeling tool is used for building an intelligent equipment semantic model, and the semantic model is loaded and edited through a Jena toolkit; reading the real-time data of the equipment through the JDBC, and associating the real-time data of the equipment in the database with the model; self-defining a logical reasoning rule in Jena, and performing logical reasoning by utilizing a reasoning machine to realize fault diagnosis of the intelligent equipment; finally, the fault diagnosis result is intelligently visualized, and a proper solution is recommended. By adopting the method, the fault diagnosis efficiency and level of the intelligent equipment in the intelligent factory can be remarkably improved, the maintenance time of the equipment is shortened, and the production benefit is maximized.

Description

technical field [0001] The invention relates to the field of fault diagnosis of intelligent equipment in an intelligent factory, in particular to a method for fault diagnosis of intelligent equipment based on semantic reasoning. Background technique [0002] With the rapid development of related technologies such as the Internet of Things, cloud computing, and advanced manufacturing, the field of intelligent manufacturing is also developing rapidly. At present, establishing a smart factory based on a cyber-physical fusion system is the main way to realize smart manufacturing. More and more manufacturing equipment in smart factories is intelligent, such as robotic arms that automatically identify product types and quantities, and precisely positioned automatic cruise vehicles (AGV). Such equipment, devices or machines with computing and processing capabilities are called smart equipment. With the emergence and increase of smart devices, the manufacturing line is increasingl...

AI Technical Summary

Problems solved by technology

Although the fault diagnosis method based on machine learning can effectively obtain potential knowledge from massive data, it is very dependent on the accuracy of the diagnosis model and the integrity of the data sample.

Moreover, most of these methods are black-box models, which have poor interpretability and are not conducive to accurate fault location.

Images