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- W4296250508 abstract "Artificial intelligence, automated sensors, and robots have improved the production efficiency in smart factories. Real-time monitoring and quick responses are important to prevent time losses and increased costs caused by abnormal situations in an automated manufacturing process with several actuators. In this study, we explicitly analyzed only sequential 2D image data from a webcam to detect faults in an automatic manufacturing process without any additional sensors. A model combining a convolutional neural network and long-short term memory was used to reflect the characteristics of sequential processes. When a current process image was applied to the model, it predicted the corresponding future image captured by the camera 1 s later. For training, only image datasets collected during normal manufacturing process operations were used. The prediction error of the training datasets was used for threshold calculation to detect faults. For validation and verification, we collected image datasets for two fault types; the proposed model demonstrated the highest detection accuracy with a dynamic threshold." @default.
- W4296250508 created "2022-09-19" @default.
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- W4296250508 date "2022-01-01" @default.
- W4296250508 modified "2023-10-16" @default.
- W4296250508 title "Fault Detection in Automatic Manufacturing Processes via 2D Image Analysis Using a Combined CNN–LSTM Model" @default.
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- W4296250508 doi "https://doi.org/10.1007/978-3-031-16407-1_2" @default.
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