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Fault detection in reciprocating compressor valves under varying load conditions

机译：在变化的负载条件下对往复式压缩机阀进行故障检测

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This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.
机译：本文提出了一种在变化的负载条件下检测破裂或损坏的往复式压缩机阀的新颖方法。主要思想是振动测量数据的时频表示将根据故障状态显示典型模式。问题是可靠地检测这些模式。对于检测任务，我们通过二维自相关绕行。自相关强调模式并减少噪声影响。这样可以更轻松地定义适当的功能。特征提取后，使用逻辑回归和支持向量机进行分类。该方法的性能通过分析实际测量数据来验证。结果将显示出很高的检测精度，同时针对不同的压缩机负载将误报率保持在非常低的水平，从而实现了一种独立于负载的方法。据我们所知，所提出的方法是第一种自动进行往复式压缩机气门故障检测的自动方法，该方法可以处理变化的负载条件。

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