Data-driven fault diagnosis of industrial robots with a cloud computing framework

초록

In modern manufacturing industry, industrial robots have been widely used. Health status of the robots should be always monitored to prevent a sudden shutdown of manufacturing lines. Supervising the signals measured from the industrial robot and diagnosing the status of machines in real time are essential tasks for us to manage the manufacturing lines. In this work, we developed a system for data-driven fault diagnosis of industrial robots, which includes a data acquisition and mining process, a machining learning process, and a cloud computing framework. The signals gathered from attached sensors on the robot are stored in a database within the framework. Structured data are extracted from the stored raw signals. The most important features are selected from the structured data for preventing the overfitting problems in the machine learning process. The fault diagnosis models are trained based on several machine learning algorithms and selected features. Finally, the fault diagnosis results are monitored by operators using mobile devices in real time. All monitoring and diagnosing processes including signal processing, feature extraction, feature selection, and diagnosis operate in the server of our cloud computing framework. Copyright © Proceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020. All rights reserved.

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