Predictive Maintenance Edge Artificial Intelligence Application Study Using Recurrent Neural Networks for Early Aging Detection in Peristaltic Pumps

作者：J. M. Montes-Sanchez, Yoko Uwate, Yoshifumi Nishio, Saturnino Vicente-Díaz, Á. Jiménez-Fernández · 发表于：IEEE Transactions on Reliability · 年份：2025 · DOI：10.1109/tr.2024.3488963 · 被引用次数：11 · 研究领域：Computer Science

Peristaltic 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 ...