![](bga.png)
ical fault field and the data collected from the CWRU
rolling bearing data center, we extract the relation-
ship between entities and entities using a seven-step
process, store the obtained entities and inter-entity re-
lationships in the Neo4j graph database to complete
the construction of the bearing fault event knowledge
graph, and implement the four types of visual query
functions based on the constructed event kdb. Then,
we construct a deep convolutional neural network
with a wide convolutional kernel on the first layer
in order to diagnose the operational status of rolling
bearings. The experimentally established WDCNN
model has an accuracy rate of greater than 99.8% on
the training dataset and greater than 97.3% on the test
dataset. Finally, we predict bearing faults by com-
bining an event knowledge graph with a deep con-
volutional neural network. By acquiring newly col-
lected data during rolling bearing operation, setting
fault thresholds, and applying WDCNN to predict and
analyze them, new nodes and their relationships are
added to the event knowledge graph.
In rolling bearing fault diagnosis, the introduction
of the event knowledge graph and convolutional neu-
ral network provides support for fault diagnosis and
prediction, enhances the accuracy of fault diagnosis
and prediction, and reduces the need for expertise and
expert system domain knowledge.
ACKNOWLEDGEMENTS
This work is supported by the National Natural Sci-
ence Foundation of China (62002207, 62072290,
62073201), the Shandong Provincial Natural Sci-
ence Foundation (ZR2022YQ05, ZR2020MA102)
and Shandong Provincial Key Laboratory for Novel
Distributed Computer Software Technology.
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