用Transformer预测工业设备故障，提前一小时准确率达70.84%
Industrial Machines Health Prognosis using a Transformer-based Framework
- 分两步：先用改进的分位数回归找异常，再用融合Transformer分类
- 在真实饮料厂场景中，提前1小时预测故障准确率70.84%
- 可将良品率从78.38%提升至89.62%，适合制造业实时维护场景
本文提出一种基于Transformer的分位数回归神经网络（TQRNN），用于制造场景下的实时设备故障预测。该方法采用两阶段设计：(i) 改进的分位数回归神经网络，在保持低时间复杂度的同时识别异常离群点；(ii) 融合Transformer网络，实现长达一小时时间窗内的高精度分类。模型在真实饮料制造工业环境中部署，结果表明，其在1小时预警时间内故障预测准确率达到70.84%。此外，使用TQRNN可显著提升高质量生产，使产品良品率从78.38%提升至89.62%。该研究证明了预测性维护在降低非计划停机、减少维修成本、优化生产效率和保障运行稳定方面的重要作用，具有显著的成本节约潜力，并增强可持续性与竞争力。
原文摘要 · Abstract (English)
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.
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