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

arXiv: https://arxiv.org/abs/2110.03440 · HTML: https://mlsanity.com/paper/2110.03440/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)

## Built on by (most cited first, 3 of 3)

- [Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems](https://mlsanity.com/paper/2210.08011/) (2022)
- [Comparison of Clustering Algorithms for Statistical Features of Vibration Data Sets](https://mlsanity.com/paper/2305.06753/) (2023)
- [One-Class Domain Adaptation via Meta-Learning](https://mlsanity.com/paper/2501.13052/) (2025)

## Abstract (arXiv)

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.