Online breakage detection of multitooth tools using classifier ensembles for imbalanced data

Cutting tool breakage detection is an important task, due to its economic impact on mass production lines in the automobile industry. This task presents a central limitation: real data-sets are extremely imbalanced because breakage occurs in very few cases compared with normal operation of the cutti...

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Vydáno v:International journal of systems science Ročník 45; číslo 12; s. 2590 - 2602
Hlavní autoři: Bustillo, Andrés, Rodríguez, Juan J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Taylor & Francis 02.12.2014
Témata:
Balancing
Breakage
breakage detection
classifier ensembles
Classifiers
Data mining
Graphs
imbalanced data
industrial applications
Logistics
multitooth tools
Regression
Tasks
ISSN:0020-7721, 1464-5319
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Shrnutí:Cutting tool breakage detection is an important task, due to its economic impact on mass production lines in the automobile industry. This task presents a central limitation: real data-sets are extremely imbalanced because breakage occurs in very few cases compared with normal operation of the cutting process. In this paper, we present an analysis of different data-mining techniques applied to the detection of insert breakage in multitooth tools. The analysis applies only one experimental variable: the electrical power consumption of the tool drive. This restriction profiles real industrial conditions more accurately than other physical variables, such as acoustic or vibration signals, which are not so easily measured. Many efforts have been made to design a method that is able to identify breakages with a high degree of reliability within a short period of time. The solution is based on classifier ensembles for imbalanced data-sets. Classifier ensembles are combinations of classifiers, which in many situations are more accurate than individual classifiers. Six different base classifiers are tested: Decision Trees, Rules, Naïve Bayes, Nearest Neighbour, Multilayer Perceptrons and Logistic Regression. Three different balancing strategies are tested with each of the classifier ensembles and compared to their performance with the original data-set: Synthetic Minority Over-Sampling Technique (SMOTE), undersampling and a combination of SMOTE and undersampling. To identify the most suitable data-mining solution, Receiver Operating Characteristics (ROC) graph and Recall-precision graph are generated and discussed. The performance of logistic regression ensembles on the balanced data-set using the combination of SMOTE and undersampling turned out to be the most suitable technique. Finally a comparison using industrial performance measures is presented, which concludes that this technique is also more suited to this industrial problem than the other techniques presented in the bibliography.
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ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2013.775378