A Self-Learning Diagnosis Algorithm Based on Data Clustering

D. Tretyakov
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引用次数: 1

Abstract

The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.
基于数据聚类的自学习诊断算法
本文描述了一种构建自学习诊断算法的方法。自学习算法创建考虑对象的模型。模型是在一定时间周期内周期性形成的。模型包括一组函数,可以描述整个对象,也可以描述对象的一部分,也可以描述对象的特定功能。从而获取故障定位信息。在对象的操作过程中,算法收集从传感器接收的数据。然后,该算法生成与稳态运行相关的样本。这些样本的聚类用于函数定义。集群中心的函数值存储在计算机的存储器中。为了说明所考虑的方法,描述了它在涡轮机械诊断中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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