Online prediction of threading task failure using Convolutional Neural Networks
| Autor: | Guilherme Renato Caldo Moreira, Gustavo Jose Giardini Lahr, José Otávio Savazzi, Thiago Boaventura, Glauco Augusto de Paula Caurin |
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| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry 020208 electrical & electronic engineering 02 engineering and technology Thread (computing) Machine learning computer.software_genre Convolutional neural network Fault detection and isolation law.invention Instruction set Industrial robot 020901 industrial engineering & automation law 0202 electrical engineering electronic engineering information engineering Task analysis Pneumatic gripper Robot Artificial intelligence business computer |
| Zdroj: | IROS |
| Popis: | Fasteners assembly automation in different industries require flexible systems capable of dealing with faulty situations. Fault detection and isolation (FDI) techniques are used to detect failure and deal with them, avoiding losses on parts, tools or robots. However, FDI usually deals with the faults after or at the moment they occur. Thus, we propose a method that predicts potential failures online, based on the forces and torques signatures captured during the task. We demonstrate the approach experimentally using an industrial robot, equipped with a force-torque sensor and a pneumatic gripper, used to align and thread nuts into bolts. All effort information is fed into a supervised machine learning algorithm, based on a Convolutional Neural Network (CNN) classifier. The network was able to predict and classify the threading task outcomes in 3 groups: mounted, not mounted or jammed. Our approach was able to reduce in 10.9% the threading task execution time when compared to a reference without FDI, but had problem to predict jammed cases. The same experiment was also performed with other two additional learning algorithms, and the results were systematically compared. |
| Databáze: | OpenAIRE |
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