Abstract
In this work, an adaptive architecture has been implemented for rotating machinery fault diagnoses of an industrial machine, where the input data used in this work has been taken from alternative current motors. The data sets for training and testing were recorded with a vibrometer. A dynamic neural architecture is previously trained with the training data set. Furthermore, an online monitoring system is implemented using the testing data set to detect abnormal behaviour of the monitored signal which can lead to a failure of the industrial machine. For the evaluation of the architecture, tests are performed using the obtained signal from different vibration tests.
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This project is supported by Jan Evangelista Purkyně University. Title of the project - Predictive maintenance of an industrial machine using neural networks.
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Rodríguez-Jorge, R., Sánchez-Pérez, L., Bíla, J., Škvor, J. (2021). Adaptive Architecture for Fault Diagnosis of Rotating Machinery. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-030-75078-7_5
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