Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6858494 | Information Sciences | 2014 | 13 Pages |
Abstract
This paper presents an evaluation of various methodologies used to determine relative significances of input variables in data-driven models. Significance analysis applied to manufacturing process parameters can be a useful tool in fault diagnosis for various types of manufacturing processes. It can also be applied to building models that are used in process control. The relative significances of input variables can be determined by various data mining methods, including relatively simple statistical procedures as well as more advanced machine learning systems. Several methodologies suitable for carrying out classification tasks which are characteristic of fault diagnosis were evaluated and compared from the viewpoint of their accuracy, robustness of results and applicability. Two types of testing data were used: synthetic data with assumed dependencies and real data obtained from the foundry industry. The simple statistical method based on contingency tables revealed the best overall performance, whereas advanced machine learning models, such as ANNs and SVMs, appeared to be of less value.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Marcin Perzyk, Andrzej Kochanski, Jacek Kozlowski, Artur Soroczynski, Robert Biernacki,