基于模糊聚类算法的早期故障检测

N. Boudaoud, M. Masson
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引用次数: 2

摘要

本文介绍了一种基于模糊模式识别方法的自适应诊断系统。所提出的系统被设计为在线运行,并处理以下特征:类的在线适应,在新状态下检测缓慢或突然的变化和稳定,在线创建新类。为了满足这些要求,采用模糊聚类的方法对类进行了顺序构造。这样的聚类过程只需要通过一次数据,随着新的观察结果的收集,模糊原型被创建或调整。原型是通过使用带有距离拒绝选项的k近邻规则创建的。这种标记规则很好地适用于稳态和突变。但是,此规则不适用于早期故障。为了解决这个限制,我们定义了临时原型的概念。为了确定该原型是代表稳态还是瞬态,我们引入了基于原型激活率的渐进假设检验。给出了稳健性研究的结果。最后,通过仿真实例对诊断系统的运行进行了演示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of incipient fault using fuzzy agglomerative clustering algorithm
This paper depicts an adaptive diagnostic system based on a fuzzy pattern recognition approach. The proposed system is designed to operate on-line and to deal with the following characteristics: on-line adaptation of classes, detection of slow or abrupt changes and stabilization in a new state, on-line creation of new classes. To meet these requirements, classes are constructed sequentially with a fuzzy agglomerative clustering procedure. Such a clustering procedure requires only one pass through the data, the fuzzy prototypes are created or adapted as new observations are gathered. A prototype is labelled as it is created by using a k-nearest neighbours rule with a distance reject option. This labelling rule is well adapted for stationary states and abrupt changes. However, this rule does not operate in case of incipient faults. To deal with this limitation, we define the concept of temporary prototype. To decide if this prototype is representative of a stationary state or a transient one we introduce a progressive hypotheses test based on the activation rate of the prototype. The results of a robustness study are presented. Finally, the diagnosis system operation is demonstrated on a simulated example.
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