# Industrial Machines Health Prognosis using a Transformer-based Framework

arXiv: https://arxiv.org/abs/2411.14443 · HTML: https://mlsanity.com/paper/2411.14443/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Long-Range Transformers for Dynamic Spatiotemporal Forecasting](https://mlsanity.com/paper/2109.12218/) (2021)
- [Transformers predicting the future. Applying attention in next-frame and time series forecasting](https://mlsanity.com/paper/2108.08224/) (2021)
- [Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks](https://mlsanity.com/paper/2204.09942/) (2022)

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

- [Quantile-Led Feature Extraction for Multi-Horizon Predictive Maintenance in Industrial Manufacturing Systems](https://mlsanity.com/paper/2609.07533/) (2026)

## Abstract (arXiv)

This article introduces Transformer Quantile Regression Neural Networks (TQRNNs), a novel data-driven solution for real-time machine failure prediction in manufacturing contexts. Our objective is to develop an advanced predictive maintenance model capable of accurately identifying machine system breakdowns. To do so, TQRNNs employ a two-step approach: (i) a modified quantile regression neural network to segment anomaly outliers while maintaining low time complexity, and (ii) a concatenated transformer network aimed at facilitating accurate classification even within a large timeframe of up to one hour. We have implemented our proposed pipeline in a real-world beverage manufacturing industry setting. Our findings demonstrate the model's effectiveness, achieving an accuracy rate of 70.84% with a 1-hour lead time for predicting machine breakdowns. Additionally, our analysis shows that using TQRNNs can increase high-quality production, improving product yield from 78.38% to 89.62%. We believe that predictive maintenance assumes a pivotal role in modern manufacturing, minimizing unplanned downtime, reducing repair costs, optimizing production efficiency, and ensuring operational stability. Its potential to generate substantial cost savings while enhancing sustainability and competitiveness underscores its importance in contemporary manufacturing practices.