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Neural Network to Failure Classification in Robotic Systems

2016; UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ; Volume: 4; Issue: 1 Linguagem: Inglês

10.3895/bjic.v4n1.4663

ISSN

2318-4531

Autores

José Jair Alves Mendes, Marcelo Bissi Pires, Mário Elias Marinho Vieira, Sérgio Okida, Sérgio Luiz Stevan,

Tópico(s)

Fault Detection and Control Systems

Resumo

A robotic system is a reconfigurable element, and inits programming, an algorithm can be implemented in order todetect and classify failures. This is an important step to ensurethat errors in actions do not cause damage or bring risks.Considering this, a Neural Network Multi Layer Perceptron(MLP) was used, in order to classify a set of failures in robotactuators, present in a database. This purpose is to analyze ifrobotic failures could be classified by MLP. The raw data aredivided in a temporal progression manner and torque in x, y andz axes. In total, five MLP neural networks were implemented foreach type of failure classification, using two different topologies.The number of neurons in the hidden layer is in accord with thecriteria of Kolmogorov and Weka, being the latter the besttopology for such application. In comparison to an algorithm(SKIL) using the same set of data, the MLP obtained the bestperformance in any topology of classification, with hit rates in80 to 90%.

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