用嵌入模型提升工业5.0多变量时序异常检测效果
Multivariate Time Series Anomaly Detection in Industry 5.0
- 将时序数据转为向量嵌入，捕捉复杂时间依赖关系
- 在真实工厂数据上，噪声环境下仍保持高检测准确率
- 适合需要实时监控设备状态的智能制造场景
Industry 5.0 环境对有效异常检测方法提出迫切需求，以识别设备故障、流程低效或潜在安全风险。制造产线日益传感器化，使过程更可观察，但也带来持续分析海量多变量时间序列数据的挑战。这些挑战包括数据质量问题，如存在噪声、无标签甚至错误标注。一种有前景的方法是结合嵌入模型与其它机器学习算法，以提升异常检测的整体性能。将时间序列表示为向量具有诸多优势，如更高灵活性和更强的复杂时间依赖性捕捉能力。我们在一个真实工业场景中测试了该方案，使用来自 Bonfiglioli 工厂的数据。结果表明，与传统基于重构的自编码器相比，后者在偶发噪声下常表现不佳，而我们的嵌入式框架在多种噪声条件下均保持高性能。
原文摘要 · Abstract (English)
Industry5.0 environments present a critical need for effective anomaly detection methods that can indicate equipment malfunctions, process inefficiencies, or potential safety hazards. The ever-increasing sensorization of manufacturing lines makes processes more observable, but also poses the challenge of continuously analyzing vast amounts of multivariate time series data. These challenges include data quality since data may contain noise, be unlabeled or even mislabeled. A promising approach consists of combining an embedding model with other Machine Learning algorithms to enhance the overall performance in detecting anomalies. Moreover, representing time series as vectors brings many advantages like higher flexibility and improved ability to capture complex temporal dependencies. We tested our solution in a real industrial use case, using data collected from a Bonfiglioli plant. The results demonstrate that, unlike traditional reconstruction-based autoencoders, which often struggle in the presence of sporadic noise, our embedding-based framework maintains high performance across various noise conditions.
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