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Vision-based Position Deviation Fault Injection Method for Building a Collaborative Robot Motion Fault Dataset
Donghee Yun, Dongyeon Yoo, Jungwon Lee
http://doi.org/10.5626/JOK.2023.50.9.795
The data-based fault detection method, which collects data from internal and external sensors in real-time and predicts fault, is being applied to collaborative robots, which are key facilities in smart factories. The data-based fault detection method requires a large amount of data for learning, and in particular, a large amount of data labeled as a fault state is essential. However, it is difficult to obtain large amounts of actual fault data in industrial settings. Therefore, in this study, the output of the collaborative robot fault state based on a vision sensor was analyzed and compared with the output of the normal state, and a fault injection method was proposed based on the deviation between the analyzed output signals. Collaborative robot data collected in the actual fault state could be replaced with data collected in the proposed fault injection state. The comparison of the performance of the model trained with fault injection data and trained with actual fault data confirmed that there was almost no difference, with an average of 0.97 and 0.98 accuracy, thus verifying the effectiveness of the proposed fault injection method.
Study of State Machine Diagram Robustness Testing using Casual Relation of Events
Seon Yeol Lee, Heung Seok Chae
Studies of fault injection into state machine diagram have been studied for generating robustness test cases. Conventional studies have, however, tended to inject too many faults into diagrams because they only have considered structural aspects of diagrams. In this paper, we propose a method that aims to reduce the number of injected fault without a decrease in effectivenss of robustness test. A proposed method is demonstrated using a microwave oven sate machine diagram and evaluated using a hash table state machine diagram. The result of the evaluation shows that the number of injected faults is decreased by 43% and the number of test cases is decreased by 63% without a decrease in effectiveness of hash table robustness test.
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