核应用的混合故障预测:解决旋转电厂模型的不确定性

Jennifer Blair, B. Stephen, Blair Brown, Alistair Forbes, S. Mcarthur
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引用次数: 1

摘要

核电厂运营商必须了解与部署预测工具相关的不确定性,以证明将其纳入运营决策过程并满足监管要求是合理的。运行的不确定性可能导致潜在的预测模型在资产上表现不佳,这些资产受到年限、制造公差、运行条件和运行环境影响的不断变化的影响,这些影响可能通过状态监测(CM)系统捕获,而CM系统本身可能会退化。数据获取管道中的不确定性来源可能影响用于估计资产剩余使用寿命(RUL)的CM数据的健康状况。这些不确定性可以掩盖或错误地描述开发中的错误,例如,直到故障发展到无法管理的状态时才实现故障识别。这给运营商的维护决策留下了很少的灵活性,通常会破坏模型的可信度。一种量化和解释操作不确定性的方法是校准混合模型,采用物理、知识或数据驱动的方法来提高模型的准确性和鲁棒性。混合模型允许已知的物理关系来抵消对潜在不可信数据的完全依赖,同时减少了对大量代表性历史数据的需求,以可靠地识别受监测资产的潜在行为趋势。然后对模型进行校准,通过考虑物理或知识模型与CM数据之间的差异,确保模型更新并代表实际监控资产。在本文中,一个开源的轴承知识告知机器学习(ML)模型和CM数据集被用于说明性轴承预测应用。在模型的数据采集和处理管道开发的关键阶段做出的决策所产生的不确定性被评估并通过结果影响RUL预测性能来证明。结果表明,设计决策可能导致多个有效的管道设计,这些管道设计产生不同的预测RUL轨迹,增加了模型输出的不确定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Fault Prognostics for Nuclear Applications: Addressing Rotating Plant Model Uncertainty
Nuclear plant operators are required to understand the uncertainties associated with the deployment of prognostics toolsin order to justify their inclusion in operational decision making processes and satisfy regulatory requirements. Operationaluncertainty can cause underlying prognostics models to underperform on assets that are subject to evolving impactsof age, manufacturing tolerances, operating conditions, and operating environment effects, of which may be capturedthrough a condition monitoring (CM) system that itself may be degraded. Sources of uncertainty in the data acquisitionpipeline can impact the health of CM data used to estimate the remaining useful life (RUL) of assets. These uncertaintiescan disguise or misrepresent developing faults, where (for example) the fault identification is not achieved until it hasprogressed to an unmanageable state. This leaves little flexibility for the operator’s maintenance decisions and generallyundermines model confidence. One method to quantify and account for operational uncertainty is calibrated hybrid models, employing physics, knowledgeor data driven methods to improve model accuracy and robustness. Hybrid models allow known physical relations tooffset full reliance on potentially untrustworthy data, whilst reducing the need for an abundance of representative historicaldata to reliably identify the monitored asset’s underlying behavioural trends. Calibration of the model then ensuresthe model is updated and representative of the real monitored asset by accounting for differences between the physics orknowledge model and CM data. In this paper, an open-source bearing knowledge informed machine learning (ML) model and CM datasets are utilizedin an illustrative bearing prognostic application. The uncertainty incurred by the decisions made at key stages in thedevelopment of the model’s data acquisition and processing pipeline are assessed and demonstrated by the resultant impacton RUL prediction performance. It was shown that design decisions could result in multiple valid pipeline designswhich generated different predicted RUL trajectories, increasing the uncertainty in the model output.
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