Bayesian Network Classifier with Efficient Statistical Time-Series Features for the Classification of Robot Execution Failures

International Journal of Computer Science and Engineering
© 2016 by SSRG - IJCSE Journal
Volume 3 Issue 11
Year of Publication : 2016
Authors : José Alonso-Tovar, Baidya Nath Saha, Jesús Romero-Hdz, David Ortega
: 10.14445/23488387/IJCSE-V3I11P114

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Citation:
MLA Style:

José Alonso-Tovar, Baidya Nath Saha, Jesús Romero-Hdz, David Ortega, "Bayesian Network Classifier with Efficient Statistical Time-Series Features for the Classification of Robot Execution Failures" SSRG International Journal of Computer Science and Engineering 3.11 (2016): 70-79.

APA Style:

José Alonso-Tovar, Baidya Nath Saha, Jesús Romero-Hdz, David Ortega,(2016). Bayesian Network Classifier with Efficient Statistical Time-Series Features for the Classification of Robot Execution Failures. SSRG International Journal of Computer Science and Engineering 3(11), 70-79.

Abstract:

Accurate classification of robot execution failure during the manufacturing assembly operations guides to automate robot to perform the predefined tasks. In this paper we exploit the statistical transformations of time-series data for the classification of robot execution failure in the context of peg-hole insertion task. The statistical transformation of time-series data aims to reduce the dimension and unearth the discriminative features for the classification task and and hence improves the performance, such as predictive accuracy and Learning time. We collected force-torque sensor data for different execution failures during peg-hole insertion task using the industrial high speed and powerful MOTOMAN-MH6 six axis robot. We conducted an extensive supervised classification analysis with different classifiers with raw force torque sensor data as well as statistical features computed from the force torque sensor data. Experimental results demonstrated that Bayesian network classifier with efficient time-series features can more accurately classify different robot execution failures than other classifiers. We validated the experimental results on UCI benchmark dataset.

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Key Words:

Time series classification, Robot execution failures, Data transformation.