Industrial components and systems typically operate in an evolving environment characterized by modifications of the working conditions. Methods for diagnosing faults in components and systems must, therefore, be capable of adapting to the changings in the environment of operation. In this work, we propose a novel fault diagnostic method based on the compacted object sample extraction algorithm for fault diagnostics in an evolving environment from where unlabeled data are collected. The developed diagnostic method is shown able to correctly classify data taken from synthetic and real-world case studies.

中文翻译：

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使用未标记数据的不断发展的环境中的故障​​诊断方法

工业组件和系统通常在不断变化的环境中运行，其特征是工作条件的改变。因此，诊断组件和系统故障的方法必须能够适应操作环境的变化。在这项工作中，我们提出了一种基于压缩对象样本提取算法的新颖故障诊断方法，用于在不断发展的环境中进行故障诊断，在该环境中收集未标记的数据。显示出开发的诊断方法能够正确分类从综合案例和实际案例中获得的数据。