School of Computer Science and Technology, Tianjin University, Tianjin 300350, China.
Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing 100144, China.
Sensors (Basel). 2018 Jun 5;18(6):1844. doi: 10.3390/s18061844.
Predictive industrial maintenance promotes proactive scheduling of maintenance to minimize unexpected device anomalies/faults. Almost all current predictive industrial maintenance techniques construct a model based on prior knowledge or data at build-time. However, anomalies/faults will propagate among sensors and devices along correlations hidden among sensors. These correlations can facilitate maintenance. This paper makes an attempt on predicting the anomaly/fault propagation to perform predictive industrial maintenance by considering the correlations among faults. The main challenge is that an anomaly/fault may propagate in multiple ways owing to various correlations. This is called as the uncertainty of anomaly/fault propagation. This present paper proposes a correlation-based event routing approach for predictive industrial maintenance by improving our previous works. Our previous works mapped physical sensors into a soft-ware-defined abstraction, called proactive data service. In the service model, anomalies/faults are encapsulated into events. We also proposed a service hyperlink model to encapsulate the correlations among anomalies/faults. This paper maps the anomalies/faults propagation into event routing and proposes a heuristic algorithm based on service hyperlinks to route events among services. The experiment results show that, our approach can reach 100% precision and 88.89% recall at most.
预测性工业维护通过主动安排维护来最小化设备异常/故障的发生。几乎所有当前的预测性工业维护技术都是在构建时基于先验知识或数据构建模型的。然而，异常/故障会沿着传感器之间隐藏的相关性在传感器和设备之间传播。这些相关性可以促进维护。本文通过考虑故障之间的相关性，尝试预测异常/故障的传播，以进行预测性工业维护。主要的挑战是，由于各种相关性，异常/故障可能会以多种方式传播。这被称为异常/故障传播的不确定性。本文通过改进我们之前的工作，提出了一种基于相关性的事件路由方法，用于预测性工业维护。我们之前的工作将物理传感器映射到称为主动数据服务的软件定义的抽象中。在服务模型中，异常/故障被封装成事件。我们还提出了一种服务超链接模型来封装异常/故障之间的相关性。本文将异常/故障的传播映射到事件路由中，并提出了一种基于服务超链接的启发式算法来在服务之间路由事件。实验结果表明，我们的方法在大多数情况下可以达到 100%的精度和 88.89%的召回率。
