A text mining-based approach for comprehensive understanding of Chinese railway operational equipment failure reports
Abstract
Railway
operational
equipment
is
crucial
for
ensuring
the
safe,
smooth,
and
efficient
operation
of
trains.
Comprehensive
analysis
mining
historical
railway
failure
(ROEF)
reports
are
significant
importance
improving
safety.
Currently,
challenges
in
comprehensively
analyzing
ROEF
arise
due
to
limitations
text
technologies.
To
address
this
concern,
study
leverages
advanced
techniques
thoroughly
analyze
these
reports.
Firstly,
real
report
data
provided
by
a
Chinese
bureau
used
as
source.
The
preprocessed
an
corpus
constructed
according
related
standard.
Secondly,
based
on
corpus,
introduced
build
innovative
named
entity
recognition
(NER)
model.
This
model
combines
bidirectional
encoder
representations
from
transformers
(BERT),
long
short-term
memory
(BiLSTM)
networks,
conditional
random
fields
(CRF),
with
additional
attention
layer
deeply
extract
features.
network
architecture
classify
specific
entities
unstructured
Finally,
knowledge
graph
(KG)
using
Neo4j
database
store
visualize
extracted
ROEF-related
relationships.
results
indicate
that
constructing
topological
relationships
network,
enables
visualization
potential
factors,
laying
foundation
prediction
safety,
while
also
filling
current
gap
Research Square (Research Square), Год журнала: 2024, Номер unknown
Опубликована: Авг. 12, 2024
Язык: Английский