# FAIR: Facilitating Artificial Intelligence Resilience in Manufacturing Industrial Internet

arXiv: https://arxiv.org/abs/2503.01086 · HTML: https://mlsanity.com/paper/2503.01086/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [Meta-Transformer: A Unified Framework for Multimodal Learning](https://mlsanity.com/paper/2307.10802/) (2023)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?](https://mlsanity.com/paper/1801.03744/) (2018)
- [LANISTR: Multimodal Learning from Structured and Unstructured Data](https://mlsanity.com/paper/2305.16556/) (2023)
- [Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing](https://mlsanity.com/paper/2310.06306/) (2023)
- [Infrastructure Resilience Curves: Performance Measures and Summary Metrics](https://mlsanity.com/paper/2102.01009/) (2021)
- [A Survey on Resilience in the IoT: Taxonomy, Classification and Discussion of Resilience Mechanisms](https://mlsanity.com/paper/2109.02328/) (2021)
- [An Online Approach to Cyberattack Detection and Localization in Smart Grid](https://mlsanity.com/paper/2102.11401/) (2021)

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

- [Robust Analysis for Resilient AI System](https://mlsanity.com/paper/2509.06172/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) systems have been increasingly adopted in the Manufacturing Industrial Internet (MII). Investigating and enabling the AI resilience is very important to alleviate profound impact of AI system failures in manufacturing and Industrial Internet of Things (IIoT) operations, leading to critical decision making. However, there is a wide knowledge gap in defining the resilience of AI systems and analyzing potential root causes and corresponding mitigation strategies. In this work, we propose a novel framework for investigating the resilience of AI performance over time under hazard factors in data quality, AI pipelines, and the cyber-physical layer. The proposed method can facilitate effective diagnosis and mitigation strategies to recover AI performance based on a multimodal multi-head self latent attention model. The merits of the proposed method are elaborated using an MII testbed of connected Aerosol Jet Printing (AJP) machines, fog nodes, and Cloud with inference tasks via AI pipelines.