# NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines

arXiv: https://arxiv.org/abs/2505.06333 · HTML: https://mlsanity.com/paper/2505.06333/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 8 of 8)

- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [A Comprehensive Survey on Rare Event Prediction](https://mlsanity.com/paper/2309.11356/) (2023)
- [Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform](https://mlsanity.com/paper/2401.15544/) (2024)
- [Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data](https://mlsanity.com/paper/2106.04920/) (2021)
- [Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0](https://mlsanity.com/paper/2307.06975/) (2023)
- [AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines](https://mlsanity.com/paper/2408.02181/) (2024)
- [Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing](https://mlsanity.com/paper/2407.01644/) (2024)

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

- [CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing](https://mlsanity.com/paper/2510.12033/) (2025)
- [Knowledge-Integrated Representation Learning for Crypto Anomaly Detection under Extreme Label Scarcity; Relational Domain-Logic Integration with Retrieval-Grounded Context and Path-Level Explanations](https://mlsanity.com/paper/2601.12839/) (2026)
- [Where Should Knowledge Enter? A Layered Framework for Knowledge Infusion in Multimodal Iterative Generative Model](https://mlsanity.com/paper/2606.06356/) (2026)

## Abstract (arXiv)

In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intricate relationships required for precise anomaly prediction in complex predictive environments with abundant data and multiple modalities. This paper proposes a neurosymbolic AI and fusion-based approach for multimodal anomaly prediction in assembly pipelines. We introduce a time series and image-based fusion model that leverages decision-level fusion techniques. Our research builds upon three primary novel approaches in multimodal learning: time series and image-based decision-level fusion modeling, transfer learning for fusion, and knowledge-infused learning. We evaluate the novel method using our derived and publicly available multimodal dataset and conduct comprehensive ablation studies to assess the impact of our preprocessing techniques and fusion model compared to traditional baselines. The results demonstrate that a neurosymbolic AI-based fusion approach that uses transfer learning can effectively harness the complementary strengths of time series and image data, offering a robust and interpretable approach for anomaly prediction in assembly pipelines with enhanced performance. noindent The datasets, codes to reproduce the results, supplementary materials, and demo are available at https://github.com/ChathurangiShyalika/NSF-MAP.