Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628 CE Delft, The Netherlands.
Design Engineering Research Group (GRID), Universidad EAFIT, Carrera 49 N° 7 Sur-50, Medellín 050001, Colombia.
Sensors (Basel). 2020 Apr 24;20(8):2429. doi: 10.3390/s20082429.
Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments.
网络物理系统 (CPSs) 具有复杂的控制机制，有助于实现最佳的系统运行和服务。这些机制意味着要并行考虑多个信号输入，以便及时响应不断变化的工作条件。尽管控制机制带来了优势，但它们在故障预防方面带来了新的挑战。控制施加的补偿作用会产生故障掩蔽效应，从而阻碍故障诊断。同样，CPSs 必须处理的多个信息输入会影响系统对故障的及时响应。本文提出了一种故障预测方法，该方法结合了基于时间序列的预测方法和基于统计的分类技术，以便在系统级别上研究系统的退化和失效形成。该方法利用基于系统运行模式 (SOM) 概念的新方法，为健康管理提供了新的视角，允许通过 SOM 的频率和持续时间来监测系统行为。通过在一个网络物理温室测试平台中系统地注入故障，对该方法进行了验证。获得的结果表明，通过分析 SOM 的频率和持续时间的变化，可以监测退化和故障形成过程。这些指标使得有可能根据实验中发生的各种故障来估计失效时间。
