Steel Plate Fault Detection using the Fitness Dependent Optimizer and Neural Networks
arxiv(2024)
摘要
Detecting faults in steel plates is crucial for ensuring the safety and
reliability of the structures and industrial equipment. Early detection of
faults can prevent further damage and costly repairs. This chapter aims at
diagnosing and predicting the likelihood of steel plates developing faults
using experimental text data. Various machine learning methods such as
GWO-based and FDO-based MLP and CMLP are tested to classify steel plates as
either faulty or non-faulty. The experiments produced promising results for all
models, with similar accuracy and performance. However, the FDO-based MLP and
CMLP models consistently achieved the best results, with 100
tested datasets. The other models' outcomes varied from one experiment to
another. The findings indicate that models that employed the FDO as a learning
algorithm had the potential to achieve higher accuracy with a little longer
runtime compared to other algorithms. In conclusion, early detection of faults
in steel plates is critical for maintaining safety and reliability, and machine
learning techniques can help predict and diagnose these faults accurately.
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