This paper presents a learning system with a K-nearest neighbour classifier to classify the wear condition of a multi-piston positive displacement pump. The first part reviews current built diagnostic methods and describes typical failures of multi-piston positive displacement pumps and their causes. Next is a description of a diagnostic experiment conducted to acquire a matrix of vibration signals from selected locations in the pump body. The measured signals were subjected to time-frequency analysis. The signal features calculated in the time and frequency domain were grouped in a table according to the wear condition of the pump. The next step was to create classification models of a pump wear condition and assess their accuracy. The selected model, which best met the set criteria for accuracy assessment, was verified with new measurement data. The article ends with a summary.

中文翻译：

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K-最近邻分类器在正排量泵磨损工况分类中的应用

本文提出了一个具有K-最近邻分类器对多活塞容积泵的磨损情况进行分类。第一部分回顾了当前建立的诊断方法并描述了多活塞容积泵的典型故障及其原因。接下来是对从泵体中选定位置获取振动信号矩阵的诊断实验的描述。对测量信号进行时频分析。在时域和频域中计算的信号特征根据泵的磨损情况分组在表中。下一步是创建泵磨损情况的分类模型并评估其准确性。所选模型最符合精度评估的既定标准，并使用新的测量数据进行了验证。文章以总结结束。