论文标题
一种基于深度学习和定时自动机的混合生产系统的新型异常检测算法
A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata
论文作者
论文摘要
在混合系统中执行异常检测是一项具有挑战性的任务，因为它需要分析离散和连续信号的时序行为和相互依赖性。通常，它需要建模系统行为，这通常是由人类工程师手动完成的。利用机器学习从观察结果创建行为模型具有优势，例如开发成本较低，对系统的特定知识的要求较少。该论文介绍了DAD：DeepAnoMalyTection，这是一种自动模型学习和混合生产系统中异常检测的新方法。它结合了深度学习和定时自动机，用于从观察结果创建行为模型。深信网络从实价输入中提取二进制特征的能力用于转换连续到离散的信号。这些信号与原始离散信号相比以相同的方式处理。通过比较实际和预测的系统行为来进行异常检测。该算法已应用于少数数据集，包括来自实际系统的两个数据集，并显示出令人鼓舞的结果。
Performing anomaly detection in hybrid systems is a challenging task since it requires analysis of timing behavior and mutual dependencies of both discrete and continuous signals. Typically, it requires modeling system behavior, which is often accomplished manually by human engineers. Using machine learning for creating a behavioral model from observations has advantages, such as lower development costs and fewer requirements for specific knowledge about the system. The paper presents DAD:DeepAnomalyDetection, a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The ability of deep belief nets to extract binary features from real-valued inputs is used for transformation of continuous to discrete signals. These signals, together with the original discrete signals are than handled in an identical way. Anomaly detection is performed by the comparison of actual and predicted system behavior. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
