多系统多传感器异常检测新框架，提升工业设备预测性维护精度
M$^2$AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholding
- 用深度模型学正常行为，残差做异常信号
- 全球评分+伽马校准，平均性能领先21%
- 适合大规模跨系统工业场景的异常监测
随着工业与运营系统中传感器数据的广泛部署，我们经常面临来自多个系统的异构时间序列。异常检测对实现预测性维护至关重要。然而，现有方法大多针对单变量或单系统多变量数据，难以应对复杂场景。为此，本文提出 M²AD 框架，用于多系统多变量时间序列的无监督异常检测。M²AD 利用深度模型捕捉正常状态下的预期行为，以残差作为潜在异常指标，并通过高斯混合模型和伽马校准将残差聚合为全局异常得分。理论证明该框架能有效处理传感器与系统间的异质性与依赖关系。实证表明，M²AD 在多项评估中平均性能优于现有方法 21%，并在亚马逊履约中心 130 个资产的大规模真实案例中验证了有效性。代码与结果已开源。
原文摘要 · Abstract (English)
With the widespread availability of sensor data across industrial and operational systems, we frequently encounter heterogeneous time series from multiple systems. Anomaly detection is crucial for such systems to facilitate predictive maintenance. However, most existing anomaly detection methods are designed for either univariate or single-system multivariate data, making them insufficient for these complex scenarios. To address this, we introduce M$^2$AD, a framework for unsupervised anomaly detection in multivariate time series data from multiple systems. M$^2$AD employs deep models to capture expected behavior under normal conditions, using the residuals as indicators of potential anomalies. These residuals are then aggregated into a global anomaly score through a Gaussian Mixture Model and Gamma calibration. We theoretically demonstrate that this framework can effectively address heterogeneity and dependencies across sensors and systems. Empirically, M$^2$AD outperforms existing methods in extensive evaluations by 21% on average, and its effectiveness is demonstrated on a large-scale real-world case study on 130 assets in Amazon Fulfillment Centers. Our code and results are available at https://github.com/sarahmish/M2AD.
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