Attempt to Predict Failure Case Classification in a Failure Database by using Neural Network Models

TL;DR

This paper explores automating failure case classification in a failure database using neural network models like MLP, CNN, and RNN, aiming to improve accuracy and efficiency in failure type categorization.

Contribution

It evaluates and compares neural network models for failure classification, identifying the most effective model in terms of accuracy and processing time.

Findings

MLP achieved the highest accuracy among models.

CNN provided a practical processing time.

Neural networks can effectively automate failure classification.

Abstract

With the recent progress of information technology, the use of networked information systems has rapidly expanded. Electronic commerce and electronic payments between banks and companies, and online shopping and social networking services used by the general public are examples of such systems. Therefore, in order to maintain and improve the dependability of these systems, we are constructing a failure database from past failure cases. When importing new failure cases to the database, it is necessary to classify these cases according to failure type. The problems are the accuracy and efficiency of the classification. Especially when working with multiple individuals, unification of classification is required. Therefore, we are attempting to automate classification using machine learning. As evaluation models, we selected the multilayer perceptron (MLP), the convolutional neural network (CNN), and the recurrent neural network (RNN), which are models that use neural networks. As a result, the optimal model in terms of accuracy is first the MLP followed by the CNN, and the processing time of the classification is practical.