摘要
天然气压缩机在石油工业中应用广泛,已成为油气生产的关键设备。由于生产现场工况变化复杂,要求压缩机重负荷连续运转,因而力求操作过程安全、可靠。为提高压气站的整体使用效率,使压缩机组正常有效运行,建立压气站设备的故障诊断系统具有重要意义。压缩机发生故障时主要表现为振动异常,其输出与正常系统输出相比,相同频率内信号的能量会有较大的差别,某些或者某种频率成分能量的改变即代表了一种故障信号。根据小波包变换对信号奇异性敏感的特点,对压缩机振动采样信号进行多尺度多层次分解,分析各个频带内信号的小波包模系数,判断压缩机是否发生故障以及故障对应的特征频带,进而确定压缩机故障类型及部位,并给出了诊断实例。结果表明,此方法切实可行,为油田进行压缩机实时故障检测提供了有效途径。
In order to raise the whole applicative efficiency of compressor stations and enable compressor sets to effectively run,it is significant to establish vibration monitoring system for compressor equipment malfunction.The main indication of compressor malfunction is quivering.Compared with the normal system output,the energy involved in the same frequency of the malfunction output is very different and the energy change in one or some frequencies represents a kind of malfunction signal.Based on the sensitivity of wavelet transfer to signal singularity,the sampled signals of compressor vibration are decomposed by multi-scale and multi-hiberarchy method and the wavelet module coefficient of the signal in each frequency band is analyzed to judge whether the compressor malfunction happened as well as its corresponding characteristic frequency band,furthermore to conform the compressor malfunction type and position.A malfunction diagnosis example is illustrated in this paper.
出处
《石油工程建设》
2008年第2期5-6,46,共3页
Petroleum Engineering Construction
关键词
压缩机
振动监测
故障诊断
compressor
vibration monitoring
malfunction diagnosis
