基于非线性复杂测度的往复压缩机故障诊断

Fault Diagnosis Based on Nonlinear Complexity Measure for Reciprocating Compressor

往复压缩机以多源非线性冲击振动信号为主,应用传统方法难以从振动信号中提取故障特征,为此提出一种基于非线性复杂测度的往复压缩机故障诊断方法。以气阀正常、阀片有缺口、阀片断裂及弹簧损坏4种状态下往复压缩机气阀振动信号为分析数据,在小波阈值降噪处理的基础上,采用均值符号化方法计算信号的归一化Lempel-Ziv复杂度(Lempel-Zivcomplexity,LZC)指标,分别给出各状态相应的LZC特征区间,利用BP人工神经网络对各状态信号的有效值特征、功率谱能量特征及LZC特征分别进行训练和测试,结果表明LZC更能准确区分不同状态的往复压缩机气阀故障,为往复压缩机故障诊断和维修决策提供了一种有效方法。

The vibration of reciprocating compressor mainly contains multi-source nonlinear pulse signal,it is difficult to extract fault characteristics from the signal with traditional methods.A novel fault diagnosis approach based on nonlinear complexity measure for reciprocating compressor is proposed.The gas valve signals of reciprocating compressor in four different states including normal valve sheets,gap valve sheets,fractured valve sheets and bad spring are used as the experimental data.The signals are denoised with threshold-based wavelet so as to reduce the noise interference.The normalized Lempel-Ziv complexity（LZC） indexes are calculated by using mean symbolization method.The LZC characteristics interval for each state is estimated,and the characteristics of effective value,power spectrum energy and LZC for reciprocating compressor are trained and detected by artificial neural network.The results show that the LZC method can extract the different faults states of reciprocating compressor accurately,which supplies a measure of fault diagnosis and maintenance strategy for reciprocating compressor.