复杂分布式系统的状态维护

B. Norman, H. Silcock
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引用次数: 1

摘要

基于状态的维护(CBM)解决方案在当今复杂的分布式系统中一直具有挑战性。由于其本身的性质,这些系统造成了一些技术障碍。要监控的系统是分布式的,通常在远程位置,需要一个安全、可靠的数据收集网络基础设施,以应对间歇性连接和可扩展。异构“领先指标”数据从各种来源收集:离散形式的数据,如状态、状态、模式、系统错误报告和来自其他软件系统的输入;参数数据,如环境传感器和系统传感器;并手动收集数据,如操作人员的观察结果和执行的维护操作。数据的不同来源和形式也给分析带来了挑战。因此,为了为复杂的分布式系统实现CBM解决方案,它必须基于三个核心支柱:能够收集异构数据类型的智能传感器、可扩展且普遍适用的预测分析方法,以及安全的网络基础设施。Mikros公司目前正在为美国海军濒海战斗舰(LCS)的战斗系统部署CBM+系统。在此CBM+系统应用中,智能传感器用于从海军作战系统收集异构数据。数据以IEEE SIMICA标准格式收集,并从部署在世界各地的LCS船舶安全地传输到美国的中央服务器。Prognostics Framework®是一种基于模型的预测推理引擎,用于分析所有数据以产生预测警报,确定维护行动需求,报告关键组件的剩余使用寿命(RUL),并为LCS船队提供全面的健康管理能力。总之,通过智能传感器技术、基于模型的预测和安全的网络基础设施,异构数据收集成为可能,为复杂的分布式系统实现CBM提供了灵活和可扩展的框架。如果没有这些核心功能,CBM就无法实现其目标,即主动支持维护需求,提高系统准备程度和可靠性,并降低当今复杂分布式系统的整体生命周期成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Condition based maintenance for complex distributed systems
Condition Based Maintenance (CBM) solutions are traditionally challenging to implement for today's complex distributed systems. By their very nature, these systems pose several technical obstacles. The systems to be monitored are distributed, often at remote locations, requiring a data collection network infrastructure that is secure, robust to intermittent connectivity and scalable. Heterogeneous “leading indicator” data is collected from various sources: discrete forms of data such as status, state, mode, system error reporting and inputs from other software systems; parametric data such as environmental sensors and system sensors; and manually collected data such as operator observables and maintenance actions performed. Disparate sources and forms of data also pose a challenge for analysis. Thus, in order to implement a CBM solution for complex distributed systems, it must be based on three core pillars: smart sensors capable of collecting heterogeneous data types, scalable and generically applicable predictive analysis methodologies, and a secure network infrastructure. Mikros is currently deploying a CBM+ system for combat systems on the U.S. Navy's Littoral Combat Ship (LCS). In this CBM+ system application, smart sensors are used to collect heterogeneous data from Navy combat systems. Data is collected in IEEE SIMICA standard format and transferred securely from LCS ships deployed around the world to a central server in the U.S. The Prognostics Framework®, a model-based prognostics reasoning engine, is used to analyze all data to produce prognostic alarms, identify maintenance action needs, report Remaining Useful Life (RUL) of key components, and provide a comprehensive health management capability for the LCS fleet. In summary, heterogeneous data collection made possible through smart sensor technology, model-based prognostics, and a secure network infrastructure provide a flexible and extensible framework to implement CBM for complex distributed systems. Without these core capabilities, CBM falls short of its goals to proactively support maintenance needs, increase system readiness and reliability and reduce overall life-cycle costs of today's complex distributed systems.
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