# Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes

arXiv: https://arxiv.org/abs/2101.06993 · HTML: https://mlsanity.com/paper/2101.06993/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 7 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 1 of 1)

- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)

## Built on by (most cited first, 1 of 1)

- [Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach](https://mlsanity.com/paper/2604.09988/) (2026)

## Abstract (arXiv)

Artificial neural network has achieved the state-of-art performance in fault detection on the Tennessee Eastman process, but it often requires enormous memory to fund its massive parameters. In order to implement online real-time fault detection, three deep compression techniques (pruning, clustering, and quantization) are applied to reduce the computational burden. We have extensively studied 7 different combinations of compression techniques, all methods achieve high model compression rates over 64% while maintain high fault detection accuracy. The best result is applying all three techniques, which reduces the model sizes by 91.5% and remains a high accuracy over 94%. This result leads to a smaller storage requirement in production environments, and makes the deployment smoother in real world.