发表机构
Georgia Tech; Ford Motor Company（佐治亚理工学院; 福特汽车公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高维时间序列监测，提出基于终线测试数据的无监督两阶段框架，结合非线性降维与控制图，在福特数据上显著提升准确率、召回率和F1分数。
AI 中文摘要
传感技术在从能源到汽车制造等各行业迅速发展。这些系统生成的高维（HD）数据具有复杂的非线性模式和强时间依赖性。传统的统计监测方法往往难以捕捉此类非线性结构。同样，终线测试中使用的许多分析方法依赖于预定义阈值和启发式规则，这限制了它们在HD时间数据中检测信息丰富异常特征的能力。相比之下，现代深度学习和生成式AI模型虽然提供了强大的预测能力，但在数据收集成本高且监测系统必须保持可解释性、低延迟、计算高效且可供非技术从业者使用的应用中往往不适用。为克服这些局限，我们提出了一种面向HD数据的先进多变量监测框架。该框架分两个阶段运行。第一阶段，对数据进行预处理，移除不完整和非信息性样本，并对时间序列数据进行时间对齐。第二阶段，执行非线性降维，然后通过基于控制图的阶段I监测程序进行异常检测。该框架可用于无监督和有监督设置，具体取决于训练期间是否有真实标签可用。此外，其灵活和模块化结构允许从业者将其组件适应不同领域和操作要求。我们在福特汽车公司汽车制造环境的真实生产数据上评估了所提出的框架。所提方法在准确率、召回率和F1分数上均优于公司现有模型，将这些指标从0.50、0.30和0.429分别提升至0.625、1.00和0.769。
英文摘要
Sensing technologies have advanced rapidly across industries ranging from energy to automotive manufacturing. These systems generate high-dimensional (HD) data characterized by complex nonlinear patterns and strong temporal dependencies. Traditional statistical monitoring methods are often limited in their ability to capture such nonlinear structure. Likewise, many analytical approaches used in End-of-Line testing rely on predefined thresholds and heuristic rules, which restrict their ability to detect informative anomaly signatures in HD temporal data. In contrast, while modern deep learning and generative AI models offer strong predictive capabilities, they are often unsuitable in applications where data are costly to collect and where the monitoring system must remain interpretable, low-latency, computationally efficient, and usable by non-technical practitioners. To overcome these limitations, we propose an advanced multivariate monitoring framework for HD data. The framework operates in two stages. In the first stage, the data are preprocessed to remove incomplete and non-informative samples and to temporally align time series data. In the second stage, nonlinear dimensionality reduction is performed, followed by anomaly detection through a control chart based phase I monitoring procedure. The framework can be used in both unsupervised and supervised settings, depending on the availability of ground truth labels during training. Moreover, its flexible and modular structure allows practitioners to adapt its components to different domains and operational requirements. We evaluate the proposed framework on real production data from an automotive manufacturing environment at Ford Motor Company. The proposed method achieves higher accuracy, recall, and F1 score than the company's existing model, improving these metrics from 0.50, 0.30, and 0.429 to 0.625, 1.00, and 0.769, respectively.
