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Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients

机译：聚类分析用于核轮机停机瞬态的无监督故障诊断

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摘要

Empirical methods for fault diagnosis usually entail a process of supervised training based on a set of examples of signal evolutions "labeled" with the corresponding, known classes of fault. However, in practice, the signals collected during plant operation may be, very often, "unlabeled", i.e., the information on the corresponding type of occurred fault is not available. To cope with this practical situation, in this paper we develop a methodology for the identification of transient signals showing similar characteristics, under the conjecture that operational/faulty transient conditions of the same type lead to similar behavior in the measured signals evolution. The methodology is founded on a feature extraction procedure, which feeds a spectral clustering technique, embedding the unsupervised fuzzy C-means (FCM) algorithm, which evaluates the functional similarity among the different operational/faulty transients. A procedure for validating the plausibility of the obtained clusters is also propounded based on physical considerations. The methodology is applied to a real industrial case, on the basis of 148 shut-down transients of a Nuclear Power Plant (NPP) steam turbine.
机译：故障诊断的经验方法通常需要基于一组带有相应已知故障类别的“标记”信号演化示例的监督训练过程。但是，实际上，在工厂运行期间收集的信号可能经常是“未标记的”，即有关相应类型的已发生故障的信息不可用。为了应对这种实际情况，在推测相同类型的操作/故障瞬态条件导致被测信号演变过程中具有相似行为的前提下，本文提出了一种识别具有相似特性的瞬态信号的方法。该方法基于特征提取过程，该过程提供了一种频谱聚类技术，并嵌入了无监督的模糊C均值（FCM）算法，该算法可评估不同操作/故障瞬变之间的功能相似性。基于物理考虑，还提出了验证获得的簇的合理性的过程。该方法基于核电厂（NPP）汽轮机的148次停机瞬态而应用于实际工业案例。

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