核工业中用于预测和健康管理的资产故障特征的发展

V. Agarwal, N. Lybeck, R. Bickford, Richard Rusaw
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引用次数: 11

摘要

核电工业的主动在线监测正在探索使用电力研究所的全舰队预测和健康管理(FW-PHM)套件软件。FW-PHM Suite是一组基于web的诊断和预后工具和数据库,可作为集成的健康监测体系结构。FW-PHM套件有四个主要模块:(1)诊断顾问,(2)资产故障特征数据库,(3)剩余使用寿命顾问,(4)剩余使用寿命数据库。本文主要研究了核电厂升压发电机和应急柴油发电机健康状态评估的资产故障特征。资产故障特征描述了基于技术检查的显著特征,可用于检测特定的故障类型。在最基本的级别上,故障签名由资产类型、故障类型和指示指定故障的一组或多个故障特征(症状)组成。资产故障签名数据库是通过基于密集技术研究的结果以及技术专家的知识和经验的内容开发练习来填充资产故障签名的。已开发的故障签名捕获这些知识,并以标准化的方法实现这些知识,从而简化诊断和预测过程。这将支持核电站主动在线监测技术的自动化,以诊断早期故障,执行主动维护,并估计资产的剩余使用寿命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of asset fault signatures for Prognostic and Health Management in the nuclear industry
Proactive online monitoring in the nuclear industry is being explored using the Electric Power Research Institute's Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. The FW-PHM Suite is a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. The FW-PHM Suite has four main modules: (1) Diagnostic Advisor, (2) Asset Fault Signature Database, (3) Remaining Useful Life Advisor, and (4) Remaining Useful Life Database. This paper focuses on development of asset fault signatures to assess the health status of generator step-up generators and emergency diesel generators in nuclear power plants. Asset fault signatures describe distinctive features based on technical examinations that can be used to detect a specific fault type. At the most basic level, fault signatures are comprised of an asset type, a fault type, and a set of one or more fault features (symptoms) that are indicative of the specified fault. The Asset Fault Signature Database is populated with asset fault signatures via a content development exercise that is based on the results of intensive technical research and on the knowledge and experience of technical experts. The developed fault signatures capture this knowledge and implement it in a standardized approach, thereby streamlining the diagnostic and prognostic process. This will support the automation of proactive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.
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