Abstract
This paper compares two fault identification implementations based on a neural network and a model based approach. Our worked example is the detection of gas bubbles in the pump head of a centrifugal blood pump. We focus on algorithms applicable on minimal sensor data with a reasonable implementation effort. The approaches were restricted to the desired blood flow and the measured rotational speed of the pump. We evaluated both implementations with data from an ECMO system.
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The authors gratefully acknowledge the contribution of the Bundesministerium für Bildung und Forschung BMBF (Grant 31LO134B).
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Kühn, J. et al. (2019). Fault Identification in a Blood Pump Using Neural Networks. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/2. Springer, Singapore. https://doi.org/10.1007/978-981-10-9038-7_6
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DOI: https://doi.org/10.1007/978-981-10-9038-7_6
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