Towards Robust and Transferable IIoT Sensor based Anomaly Classification using Artificial Intelligence

TL;DR

This paper explores the development of a robust and transferable AI-based anomaly classification method for IIoT sensors on industrial pumps, aiming to maintain accuracy across different machines and environments.

Contribution

It investigates the feasibility of transferability in AI anomaly classification models across different pumps and operational conditions, using various models and preprocessing techniques.

Findings

Models perform well on same-machine data

Transferability decreases with different machines

Preprocessing improves cross-environment accuracy

Abstract

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.