往复式压缩机振动信号特征分析及故障诊断方法研究
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摘要
往复式压缩机是石油化工生产的关键设备,保证它的安全稳定运行是石化行业设备维护工作的重中之重。因此,开展往复式压缩机状态监测与故障诊断的研究工作有着重要的现实意义和经济价值。本文以往复式压缩机故障诊断横向课题、国家自然科学基金项目(No.50805014)及教育部科学技术研究重点项目(No.109047)为依托,以超高压聚乙烯压缩机为研究对象,从往复式压缩机振动信号特征入手,针对往复式压缩机的状态监测与故障诊断技术开展了一系列的研究工作。论文的主要工作内容如下：
1.介绍了往复式压缩机的工作原理,分析了各主要零部件的故障形式及故障机理,研究了往复式压缩机各主要激励源所引起的响应信号特征,建立了各主要部件壳体表面振动信号的信号模型；总结出往复式压缩机振动信号所具有的两大特征：冲击性和周期性,并初步讨论了如何应用这两大特征进行往复式压缩机状态监测与故障诊断工作。
2.分析了往复式压缩机振动信号的频域能量分布特征,提出了基于频带能量变化的响应信号特征频带判定方法；给出了一种基于梳状滤波及包络分析的频带信号特征提取方法,并将其应用到往复式压缩机的状态监测与故障诊断中。
3.对基于EMD的降噪方法进行了研究,并根据往复式压缩机振动信号特点提出了新的降噪准则；应用包络分析方法得到降噪后信号的冲击特征,构造包络曲线的峰值特征指标作为表征压缩机工作状态的特征参数；提出结合分类敏感度评估及自然选择离散粒子群算法的最优特征子集选择方法,并应用最优特征子集构造优化支持向量机的输入向量,从而实现往复式压缩机气阀工作状态的智能识别与诊断。
4.研究了循环自相关函数的特性,将其看作一种特殊的时频分析方法,提出了一种通过EMD降噪、重构抑制循环自相关函数中交叉干扰项的方法；提出了循环信息熵的概念,并构造了两类循环信息熵,通过基于循环熵距的最小距离分类器实现了往复式压缩机气阀工作状态的快速识别。
5.提出了局域波时频相干算法,并将其应用于往复式压缩机缸体故障诊断中。利用双通道同步采集的缸体和气阀信号,通过循环互相关方法消除二者之间的延迟；然后应用局域波时频相干方法将缸体信号中的气阀干扰成分剔除出去,从而实现往复式压缩机缸体及其内部部件工作状态的准确监测与诊断。
6.设计了往复式压缩机在线监测系统总体结构,确定了主要监测参数；介绍了硬件系统构成及软件系统各组成模块的功能；通过诊断实例对系统的有效性进行了验证。
For all petrochemical enterprises, it is the most considerable work to maintain the stable operation of reciprocating compressors which play a very important role in petrochemical production. Therefore, condition monitoring and fault diagnosis for reciprocating compressors are of great practical significance and economic value. Based on the engineering research project for ethylene hypercompressor, National Natural Science Foundation of China (Grant No.50805014) and the Key Project of Chinese Ministry of Education (Grant No.109047), some effective works for fault diagnosis of reciprocating compressor are carried out by using feature analysis of vibration signals. The main works of this dissertation are listed as follows:
1. The working mechanism of reciprocating compressors is illustrated in detail. Failure modes and mechanism of the key components are summarized. Under the analysis of the characteristics of the response signals produced by certain excitation sources, vibration signal models of the key components are established. Impact response and periodicity are indicated as two key features of reciprocating compressor signal, and the application of these key features in reciprocating compressor fault diagnosis is preliminarily discussed.
2. Frequency-domain energy distribution characteristics of reciprocating compressor vibration signals are discussed. Based on frequency-band energy variation, a criterion on the decision of response signal characteristic bands is proposed. The features of these characteristic bands can be extracted by comb filtering and envelope analysis. Application in reciprocating compressor condition monitoring verifies the validity of these methods.
3. EMD denoise criterions based on the characteristics of reciprocating compressor vibration signals are proposed. The envelope peak factors of the denoised signal are obtained by using improved Hilbert transform method. A new optimal method based on improved NS-DPSO is applied to select the optimal feature subset, which is employed to construct eigenvectors to identify different working conditions of reciprocating compressor by using optimal multi-class SVMs. The engineering application in working condition classification of reciprocating compressor valves verifies the effectiveness of these methods.
4. The properties of cyclic auto-correlation function are studied. Under the discussion of second order cyclostationary method applied in condition monitoring and fault diagnosis of reciprocating compressors, cyclic auto-correlation function is indicated to be one sort of special time-frequency distribution method. EMD denoise and reconstruction are used to reduce the cross-terms of cyclic auto-correlation function. The conception of cyclic information entropy is proposed and a minimum distance classifier based on cyclic entropy distance is used to achieve the fast classification of reciprocating compressor working conditions.
5. The algorithm of Local Wave time-frequency coherence is proposed, which is applied in the fault diagnosis of reciprocating compressor cylinder. Firstly, double channel signals of cylinder and valve are synchronously sampled. Secondly, cyclic cross-correlation function is used to eliminate the time delay between the two signals. Thirdly, Local Wave time-frequency coherence method is utilized to remove the interference components, which produced by valve vibration, from cylinder signal. Finally, more concise and precise working condition information for cylinder and inner components can be obtained.
6. An on-line condition monitoring and fault diagnosis system for reciprocating compressors is developed. Overall architecture of the system is designed and primary monitoring parameters are determined. Hardware structure and the functions of software modules are introduced in detail. At last, a diagnosis example is used to testify the effectiveness of the system.
1.介绍了往复式压缩机的工作原理,分析了各主要零部件的故障形式及故障机理,研究了往复式压缩机各主要激励源所引起的响应信号特征,建立了各主要部件壳体表面振动信号的信号模型；总结出往复式压缩机振动信号所具有的两大特征：冲击性和周期性,并初步讨论了如何应用这两大特征进行往复式压缩机状态监测与故障诊断工作。
2.分析了往复式压缩机振动信号的频域能量分布特征,提出了基于频带能量变化的响应信号特征频带判定方法；给出了一种基于梳状滤波及包络分析的频带信号特征提取方法,并将其应用到往复式压缩机的状态监测与故障诊断中。
3.对基于EMD的降噪方法进行了研究,并根据往复式压缩机振动信号特点提出了新的降噪准则；应用包络分析方法得到降噪后信号的冲击特征,构造包络曲线的峰值特征指标作为表征压缩机工作状态的特征参数；提出结合分类敏感度评估及自然选择离散粒子群算法的最优特征子集选择方法,并应用最优特征子集构造优化支持向量机的输入向量,从而实现往复式压缩机气阀工作状态的智能识别与诊断。
4.研究了循环自相关函数的特性,将其看作一种特殊的时频分析方法,提出了一种通过EMD降噪、重构抑制循环自相关函数中交叉干扰项的方法；提出了循环信息熵的概念,并构造了两类循环信息熵,通过基于循环熵距的最小距离分类器实现了往复式压缩机气阀工作状态的快速识别。
5.提出了局域波时频相干算法,并将其应用于往复式压缩机缸体故障诊断中。利用双通道同步采集的缸体和气阀信号,通过循环互相关方法消除二者之间的延迟；然后应用局域波时频相干方法将缸体信号中的气阀干扰成分剔除出去,从而实现往复式压缩机缸体及其内部部件工作状态的准确监测与诊断。
6.设计了往复式压缩机在线监测系统总体结构,确定了主要监测参数；介绍了硬件系统构成及软件系统各组成模块的功能；通过诊断实例对系统的有效性进行了验证。
For all petrochemical enterprises, it is the most considerable work to maintain the stable operation of reciprocating compressors which play a very important role in petrochemical production. Therefore, condition monitoring and fault diagnosis for reciprocating compressors are of great practical significance and economic value. Based on the engineering research project for ethylene hypercompressor, National Natural Science Foundation of China (Grant No.50805014) and the Key Project of Chinese Ministry of Education (Grant No.109047), some effective works for fault diagnosis of reciprocating compressor are carried out by using feature analysis of vibration signals. The main works of this dissertation are listed as follows:
1. The working mechanism of reciprocating compressors is illustrated in detail. Failure modes and mechanism of the key components are summarized. Under the analysis of the characteristics of the response signals produced by certain excitation sources, vibration signal models of the key components are established. Impact response and periodicity are indicated as two key features of reciprocating compressor signal, and the application of these key features in reciprocating compressor fault diagnosis is preliminarily discussed.
2. Frequency-domain energy distribution characteristics of reciprocating compressor vibration signals are discussed. Based on frequency-band energy variation, a criterion on the decision of response signal characteristic bands is proposed. The features of these characteristic bands can be extracted by comb filtering and envelope analysis. Application in reciprocating compressor condition monitoring verifies the validity of these methods.
3. EMD denoise criterions based on the characteristics of reciprocating compressor vibration signals are proposed. The envelope peak factors of the denoised signal are obtained by using improved Hilbert transform method. A new optimal method based on improved NS-DPSO is applied to select the optimal feature subset, which is employed to construct eigenvectors to identify different working conditions of reciprocating compressor by using optimal multi-class SVMs. The engineering application in working condition classification of reciprocating compressor valves verifies the effectiveness of these methods.
4. The properties of cyclic auto-correlation function are studied. Under the discussion of second order cyclostationary method applied in condition monitoring and fault diagnosis of reciprocating compressors, cyclic auto-correlation function is indicated to be one sort of special time-frequency distribution method. EMD denoise and reconstruction are used to reduce the cross-terms of cyclic auto-correlation function. The conception of cyclic information entropy is proposed and a minimum distance classifier based on cyclic entropy distance is used to achieve the fast classification of reciprocating compressor working conditions.
5. The algorithm of Local Wave time-frequency coherence is proposed, which is applied in the fault diagnosis of reciprocating compressor cylinder. Firstly, double channel signals of cylinder and valve are synchronously sampled. Secondly, cyclic cross-correlation function is used to eliminate the time delay between the two signals. Thirdly, Local Wave time-frequency coherence method is utilized to remove the interference components, which produced by valve vibration, from cylinder signal. Finally, more concise and precise working condition information for cylinder and inner components can be obtained.
6. An on-line condition monitoring and fault diagnosis system for reciprocating compressors is developed. Overall architecture of the system is designed and primary monitoring parameters are determined. Hardware structure and the functions of software modules are introduced in detail. At last, a diagnosis example is used to testify the effectiveness of the system.
引文
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