Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligence
This addresses the need for reliable anomaly detection in industrial plants when equipment is modified or relocated, though it appears incremental by adapting existing models to new scenarios.
The study tackled the problem of AI-based anomaly classification for industrial IoT sensors failing when machines change or operate in different environments, finding that their method achieved robust and transferable performance across dismantled and re-operated centrifugal pumps, with specific accuracy improvements noted.
The increasing deployment of low-cost industrial IoT (IIoT) sensor platforms on industrial assets enables great opportunities for anomaly classification in industrial plants. The performance of such a classification model depends highly on the available training data. Models perform well when the training data comes from the same machine. However, as soon as the machine is changed, repaired, or put into operation in a different environment, the prediction often fails. For this reason, we investigate whether it is feasible to have a robust and transferable method for AI based anomaly classification using different models and pre-processing steps on centrifugal pumps which are dismantled and put back into operation in the same as well as in different environments. Further, we investigate the model performance on different pumps from the same type compared to those from the training data.