Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks

“…4. It should be added that a substantial increase in the accuracy of the models using class output variables, such as CTs and NBC, cannot be achieved by increasing the number of classes (detailed testing results can be found in reference [25]).…”

Section: Significances Of the Input Variablesmentioning

confidence: 99%

Data mining in manufacturing: Significance analysis of process parameters

Perzyk

Biernacki²,

Kozłowski³

2008

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Determination of the most significant manufacturing process parameters using collected past data can be very helpful in solving important industrial problems, such as the detection of root causes of deteriorating product quality, the selection of the most efficient parameters to control the process, and the prediction of breakdowns of machines, equipment, etc. A methodology of determination of relative significances of process variables and possible interactions between them, based on interrogations of generalized regression models, is proposed and tested. The performance of several types of data mining tool, such as artificial neural networks, support vector machines, regression trees, classification trees, and a naïve Bayesian classifier, is compared. Also, some simple non-parametric statistical methods, based on an analysis of variance (ANOVA) and contingency tables, are evaluated for comparison purposes. The tests were performed using simulated data sets, with assumed hidden relationships, as well as on real data collected in the foundry industry. It was found that the performance of significance and interaction factors obtained from regression models, and, in particular, neural networks, is satisfactory, while the other methods appeared to be less accurate and/or less reliable.

show abstract

“…In the context of the diagnosis of industrial systems, bayesian networks have been already used and give convenient results compared to other classification tools like support vector machines, neural networks or k-nearest neighborhoods [16], [17], [18], [19], [20]. As the performances of the CSNBN have been previously demonstrated [17], [18], we choose this classifier in this article.…”

Section: Bayesian Network For Fault Diagnosismentioning

confidence: 99%

Distance rejection in a bayesian network for fault diagnosis of industrial systems

Verron

Tiplica

Kobi

2008

2008 16th Mediterranean Conference on Control and Automation

View full text Add to dashboard Cite

The purpose of this article is to present a method for industrial process diagnosis with bayesian network. The interest of the proposed method is to combine a discriminant analysis and a distance rejection in a bayesian network in order to detect new types of fault. The performances of this method are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults and to obtain sufficient results in rejection of new types of fault.

show abstract

Modeling of manufacturing processes by learning systems: The naïve Bayesian classifier versus artificial neural networks

Cited by 29 publications

References 4 publications

Knowledge in Imperfect Data

Knowledge in Imperfect Data

Data mining in manufacturing: Significance analysis of process parameters

Distance rejection in a bayesian network for fault diagnosis of industrial systems

Contact Info

Product

Resources

About