Steel Plate Fault Detection using the Fitness Dependent Optimizer and Neural Networks

TL;DR

This paper demonstrates that machine learning models, especially those using the Fitness Dependent Optimizer, can accurately detect faults in steel plates with up to 100% accuracy, enhancing safety and maintenance.

Contribution

The study introduces the application of the Fitness Dependent Optimizer in neural networks for fault detection in steel plates, achieving superior accuracy over other methods.

Findings

FDO-based models achieved 100% accuracy.

FDO models had slightly longer runtimes but better accuracy.

All tested models showed promising results.

Abstract

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% accuracy in all 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.