Montes Sánchez, Juan ManuelYoshifumi NishioVicente Díaz, SaturninoJiménez Fernández, Ángel Francisco2025-03-102025-03-102024-11-150018-95291558-1721https://hdl.handle.net/11441/169852Peristaltic pumps are widely used in many industrial applications, especially in medical devices. Their reliability depends on proper maintenance, which includes the total replacement of tubes regularly due to the aging of the materials. The proper use of predictive maintenance techniques could potentially improve the efficiency of maintenance interventions and prevent failures by having a way to determine when the tube has passed its replacement time. We recorded a dataset using six different sensors (three accelerometers, one gyroscope, one magnetometer, and one microphone) using several cassettes (three new units and three units with expired life span). The recording was done at the highest possible frequency (100–6667 Hz, different for each sensor) and then downsampled several times to obtain frequencies as low as 12 Hz. This dataset is now publicly available. We trained 939 different models, which were the result of combining all different sensors as inputs but the microphone, and four basic architectures of recurrent neural network: One or two layers of either gated recurrent unit or long short-term memory with different number of nodes per layer (from 2 to 64). Among all trained models, we selected the ten best performing networks in terms of both accuracy and complexity. All of them reached an F1 score of 0.99 or 1 with holdout cross-validation. Those models were deployed on four different edge AI devices. For all combinations of model and edge AI devices we obtained metrics of memory size (from 0.3% to 160.6% RAM, and from 0.9% to 21.3% flash), inference time (from 0.39 to 1463.91 ms), and average consumption (from 0.15 to 5.30 mA). Nine out of ten models were proven viable for deployment. We concluded that the four models based on magnetometer data were significantly better in terms of consumption and inference time. To the best of our knowledge, the use of magnetometer data is a very uncommon approach to failure detection in predictivemaintenance applications, and this is probably the first time it has been used for peristaltic pump aging detection, so our results are very promising for future applications. Also, since most trained models use little resources, we have proved that our approach is perfectly compatible with running other communication and control algorithms on the same device, which is ideal for easy integration and scalability in industrial systems. Some limitations for real deployment include facing environmental factors (noise) and long-term monitoring, so we also proposed a protocol that should reduce the impact of those factors by taking measurements in a controlled way.application/pdf15 p.engAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/AccelerometerEdge artificial intelligence (AI)Gated recurrent unit (GRU)Industrial Internet of Things (IIoT)Long short-term memory (LSTM)MagnetometerPeristaltic pumpsPredictive maintenance (PdM)Recurrent neural network (RNN)info:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1109/tr.2024.3488963