Author:
Rammal Abbas,Ezukwoke Kenneth,Hoayek Anis,Batton-Hubert Mireille
Abstract
AbstractFailure analysis has become an important part of guaranteeing good quality in the electronic component manufacturing process. The conclusions of a failure analysis can be used to identify a component’s flaws and to better understand the mechanisms and causes of failure, allowing for the implementation of remedial steps to improve the product’s quality and reliability. A failure reporting, analysis, and corrective action system is a method for organizations to report, classify, and evaluate failures, as well as plan corrective actions. These text feature datasets must first be preprocessed by Natural Language Processing techniques and converted to numeric by vectorization methods before starting the process of information extraction and building predictive models to predict failure conclusions of a given failure description. However, not all-textual information is useful for building predictive models suitable for failure analysis. Feature selection has been approached by several variable selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune and others are not applicable to textual data. This article aims to develop a predictive model able to predict the failure conclusions using the discriminating features of the failure descriptions. For this, we propose to combine a Genetic Algorithm with supervised learning methods for an optimal prediction of the conclusions of failure in terms of the discriminant features of failure descriptions. Since we have an unbalanced dataset, we propose to apply an F1 score as a fitness function of supervised classification methods such as Decision Tree Classifier and Support Vector Machine. The suggested algorithms are called GA-DT and GA-SVM. Experiments on failure analysis textual datasets demonstrate the effectiveness of the proposed GA-DT method in creating a better predictive model of failure conclusion compared to using the information of the entire textual features or limited features selected by a genetic algorithm based on a SVM. Quantitative performances such as BLEU score and cosine similarity are used to compare the prediction performance of the different approaches.
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. Farhat, H. Chapter 9—failure analysis. In Farhat, H., editor, Operation, Maintenance, and Repair of Land-Based Gas Turbines (Elsevier, 2021).
2. Farshad, M. Chapter 2-failure investigation of plastic pipes. In Farshad, M., editor, Plastic Pipe Systems, pp. 28–25 (Oxford, 2006).
3. Blokdyk, G. Failure Reporting Analysis And Corrective Action System A Complete Guide (American Society for Quality Control, West Wisconsin, 2020).
4. Adel, M. et al. Early damage detection of fatigue failure for rc deck slabs under wheel load moving test using image analysis with artificial intelligence. Eng. Struct. 246, 1130–1150 (2021).
5. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. & Dean, J. Distributed representations of words and phrases and their compositionality. In An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing (eds Burges, C. J. et al.) (Curran Associates Inc, USA, 2013).
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献