A p−V Diagram Based Fault Identification for Compressor Valve by Means of Linear Discrimination Analysis
A p−V Diagram Based Fault Identification for Compressor Valve by Means of Linear Discrimination Analysis
复制标题
基于p-V图的线性判别分析压缩机阀门故障识别
DOI：
10.3390/machines10010053
复制
发表时间：
2022
期刊：
影响因子：
2.6
通讯作者：
Xueyuan Peng
中科院分区：
文献类型：
--
作者：
Xueying Li;Peng Ren;Zhe Zhang;Xiaohan Jia;Xueyuan Peng
压力-容积图（p-V图）是分析往复式压缩机气缸内热力过程以及包括阀门在内的核心部件故障的既定方法。吸入/排出阀的故障是非计划停机的最常见原因，未被发现的故障可能导致灾难性事故。虽然研究人员已经通过各种估计技术和案例研究对故障分类进行了研究，但很少有人深入研究实现故障级别确定的障碍和途径。阀门故障的初始阶段的特征是轻微泄漏;如果在此期间发现这一点，可以防止更严重的事故。提出了一种基于p-V图特征的往复压缩机气阀故障诊断和严重程度估计方法。由压力比、过程角系数、面积系数和过程指数系数组成的四维特征变量从p-V图中提取。采用主成分分析（PCA）和线性判别分析（LDA）建立诊断模型，PCA实现特征放大和投影，LDA实现特征降维和故障预测。通过对往复式压缩机气阀泄漏不同严重程度的诊断，验证了该方法的有效性，并将其应用于两种实际故障的诊断：往复式压缩机阀板断裂导致的轻度泄漏和加氢站液压驱动活塞式压缩机气阀变形导致的严重泄漏。
The pressure-volume diagram (p−V diagram) is an established method for analyzing the thermodynamic process in the cylinder of a reciprocating compressor as well as the fault of its core components including valves. The failure of suction/discharge valves is the most common cause of unscheduled shutdowns, and undetected failure may lead to catastrophic accidents. Although researchers have investigated fault classification by various estimation techniques and case studies, few have looked deeper into the barriers and pathways to realize the level determination of faults. The initial stage of valve failure is characterized in the form of mild leakage; if this is identified at this period, more serious accidents can be prevented. This study proposes a fault diagnosis and severity estimation method of the reciprocating compressor valve by virtue of features extracted from the p−V diagram. Four-dimensional characteristic variables consisting of the pressure ratio, process angle coefficient, area coefficient, and process index coefficient are extracted from the p−V diagram. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were applied to establish the diagnostic model, where PCA realizes feature amplification and projection, then LDA implements feature dimensionality reduction and failure prediction. The method was validated by the diagnosis of various levels of severity of valve leakage in a reciprocating compressor, and further, applied in the diagnosis of two actual faults: Mild leakage caused by the cracked valve plate in a reciprocating compressor, and serious leakage caused by the deformed valve in a hydraulically driven piston compressor for a hydrogen refueling station (HRS).