基于过程信息融合的旋转机械信息（火用）故障诊断研究
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摘要
本文以国家自然科学基金资助项目:“旋转机械振动故障信息火用诊断方法研究”为课题研究背景,以旋转机械转子系统为研究对象,基于对国内外旋转机械振动故障诊断理论与技术研究现状的分析,针对目前振动故障诊断中依靠随机提取的状态信息进行诊断存在的不足,本文提出了基于多状态的过程信息来进行诊断的新思路,在此基础上,本文首次建立了一个全新的基于过程信息融合的旋转机械信息火用故障诊断体系,该诊断体系对振动信号的故障判别、故障定位以及故障烈度诊断进行了系统的研究。
首先,根据论文研究内容的需求,设计了转子故障模拟试验方案,对每种故障均进行了多次升速试验,获得了大量的故障试验数据,为论文的研究提供了强有力地数据支持。同时通过与已有文献总结的故障征兆进行对比,不仅对所模拟故障信号的正确性进行了验证,还发现了一些故障在升速过程中新的故障征兆及过程变化规律。
第二,针对当前旋转机械故障诊断中依靠随机抽取的状态信息进行诊断存在的不足,提出了基于多状态的过程信息来进行诊断的新思路。从信息熵的思路出发,选取最能反映故障振动信号过程变化规律的单轴振测点和单瓦振测点来构造双测点多转速下的信息熵矩阵。并根据已有的试验数据,分别计算得到了8种典型故障基于时域奇异谱熵、频域功率谱熵以及时-频域小波空间特征谱熵的信息熵样本矩阵。
第三,在旋转机械故障诊断领域首次提出并定义一个全新的基于过程信息融合的概念——信息火用,并建立了旋转机械振动的信息火用故障诊断方法来定量刻画过程变化规律。分别构造了8种典型故障的时域、频域以及时-频域的信息火用样本矩阵,并通过实例计算演示了信息火用故障诊断方法。在实例计算中,针对该诊断方法存在的不足,通过定义信息火用空间贴近度的概念,提出了基于信息火用空间贴近度的信息火用故障诊断方法。
第四,从另一个角度出发,将不同过程中对应状态间的过程变化规律也定义为一种新的信息火用,使得信息火用概念得以延伸,更具普遍性。由于升速过程中振动信号的三维幅值谱最能反映振动信号的过程变化规律,在此基础上定义了频域时空特征谱的概念,并将其作为特征值,由此建立了基于频域时空特征谱的信息火用故障诊断方法,并构造了8种典型故障基于频域时空特征谱的信息火用样本矩阵。计算结果表明,该方法具有很强的故障分类能力,是一种有效的基于过程信息融合的旋转机械振动故障诊断的新方法。
最后,首次建立了一个全新的基于过程信息融合的旋转机械信息火用故障诊断体系,该诊断体系对振动信号的故障判别、故障定位以及故障烈度诊断进行了系统的研究,其诊断流程为:首先通过基于频域时空特征谱的双测点信息火用故障诊断方法对待检振动信号进行故障判别;接着通过基于频域时空特征谱的多测点信息火用故障定位方法对待检振动信号进行故障定位;最后通过待检振动信号升速过程中的原始波形数据来进行故障烈度诊断,同时诊断待检振动信号是否为正常态信号。并给出了诊断流程图。实例计算表明,该诊断体系是一种功能全面、诊断准确率高、适应性强的旋转机械振动故障诊断的新模式。
This work was supported by the National Natural Science Foundation of China (NSFC), Project No. 50775083. This paper pointed out the shortcoming of the state-based fault diagnosis methods and proposed a new diagnosis idea based on information fusion of process. On this basis, a new fault diagnosis system of information exergy of rotating machinery based on information fusion of process was first established. In this system, the systemic research on fault identification, fault orientation and fault intensity of vibration signals were carried out.
Firstly, according to the needs of research, the fault simulation test project of rotor system was designed, and the vibration signals of eight kinds of typical faults during speed-up were collected from the rotor test platform. These data provided strong support for research in this paper. Through comparing with existing faults symptom in literature, not only the correctness of the simulated fault signals was verified, but also some new faults symptom and change rules of some faults during speed-up were found.
Secondly, the shortcoming of the state-based fault diagnosis methods was pointed out and a new diagnosis idea based on information fusion of process was proposed. A dual-channel multi-speed information entropy matrix was constructed by a single shaft vibration signal and a single bearing vibration signal which can best reflect the process change rule of each fault vibration signal. Then the information entropy sample matrix of eight kinds of typical faults in time domain, frequency domain and time-frequency domain were calculated respectively.
Thirdly, a new concept of information exergy based on information fusion of process was first proposed and defined. The process change rule was quantitatively characterized through establishing the fault diagnosis method of information exergy of rotating machinery vibration. Then the information exergy sample matrix of eight kinds of typical faults in time domain, frequency domain and time-frequency domain were calculated respectively. In the calculation example, for the shortcoming of the diagnosis method, the fault diagnosis method of information exergy based on space close degree of information exergy was established.
Fourthly, from another perspective, the process change rule between two corresponding states in the difference process was also defined as a new information exergy. Because the amplitude spectrum of fault signals during speed-up can best reflect the process change rule, a new concept of space-time feature spectrum in frequency domain was defined and then it was used as eigenvalue. On this basis, the fault diagnosis method of information exergy based on space-time feature spectrum in frequency domain was established and the information exergy sample matrix of eight kinds of typical faults based on space-time feature spectrum in frequency domain was constructed respectively. The results show that the method had strong ability of fault classification, and is an effective new diagnosis method of rotating machinery based on information fusion of process.
Finally, a new fault diagnosis system of information exergy of rotating machinery based on information fusion of process was first established. In this system, the systemic research on fault identification, fault orientation and fault intensity of vibration signals were carried out. Its diagnosis approach was: First, the fault type of vibration signal was identified by fault diagnosis method of information exergy of dual-sensor based on space-time feature spectrum in frequency domain. Then the fault location of vibration signal was confirmed by fault orientation method of information exergy of multi-sensor based on space-time feature spectrum in frequency domain. Finally, the fault intensity of vibration signal was confirmed by the original waveform data during speed-up. Analysis showed that the diagnosis system was a powerful, high diagnostic accuracy, strong adaptability new model of fault diagnosis of rotating machinery.
首先,根据论文研究内容的需求,设计了转子故障模拟试验方案,对每种故障均进行了多次升速试验,获得了大量的故障试验数据,为论文的研究提供了强有力地数据支持。同时通过与已有文献总结的故障征兆进行对比,不仅对所模拟故障信号的正确性进行了验证,还发现了一些故障在升速过程中新的故障征兆及过程变化规律。
第二,针对当前旋转机械故障诊断中依靠随机抽取的状态信息进行诊断存在的不足,提出了基于多状态的过程信息来进行诊断的新思路。从信息熵的思路出发,选取最能反映故障振动信号过程变化规律的单轴振测点和单瓦振测点来构造双测点多转速下的信息熵矩阵。并根据已有的试验数据,分别计算得到了8种典型故障基于时域奇异谱熵、频域功率谱熵以及时-频域小波空间特征谱熵的信息熵样本矩阵。
第三,在旋转机械故障诊断领域首次提出并定义一个全新的基于过程信息融合的概念——信息火用,并建立了旋转机械振动的信息火用故障诊断方法来定量刻画过程变化规律。分别构造了8种典型故障的时域、频域以及时-频域的信息火用样本矩阵,并通过实例计算演示了信息火用故障诊断方法。在实例计算中,针对该诊断方法存在的不足,通过定义信息火用空间贴近度的概念,提出了基于信息火用空间贴近度的信息火用故障诊断方法。
第四,从另一个角度出发,将不同过程中对应状态间的过程变化规律也定义为一种新的信息火用,使得信息火用概念得以延伸,更具普遍性。由于升速过程中振动信号的三维幅值谱最能反映振动信号的过程变化规律,在此基础上定义了频域时空特征谱的概念,并将其作为特征值,由此建立了基于频域时空特征谱的信息火用故障诊断方法,并构造了8种典型故障基于频域时空特征谱的信息火用样本矩阵。计算结果表明,该方法具有很强的故障分类能力,是一种有效的基于过程信息融合的旋转机械振动故障诊断的新方法。
最后,首次建立了一个全新的基于过程信息融合的旋转机械信息火用故障诊断体系,该诊断体系对振动信号的故障判别、故障定位以及故障烈度诊断进行了系统的研究,其诊断流程为:首先通过基于频域时空特征谱的双测点信息火用故障诊断方法对待检振动信号进行故障判别;接着通过基于频域时空特征谱的多测点信息火用故障定位方法对待检振动信号进行故障定位;最后通过待检振动信号升速过程中的原始波形数据来进行故障烈度诊断,同时诊断待检振动信号是否为正常态信号。并给出了诊断流程图。实例计算表明,该诊断体系是一种功能全面、诊断准确率高、适应性强的旋转机械振动故障诊断的新模式。
This work was supported by the National Natural Science Foundation of China (NSFC), Project No. 50775083. This paper pointed out the shortcoming of the state-based fault diagnosis methods and proposed a new diagnosis idea based on information fusion of process. On this basis, a new fault diagnosis system of information exergy of rotating machinery based on information fusion of process was first established. In this system, the systemic research on fault identification, fault orientation and fault intensity of vibration signals were carried out.
Firstly, according to the needs of research, the fault simulation test project of rotor system was designed, and the vibration signals of eight kinds of typical faults during speed-up were collected from the rotor test platform. These data provided strong support for research in this paper. Through comparing with existing faults symptom in literature, not only the correctness of the simulated fault signals was verified, but also some new faults symptom and change rules of some faults during speed-up were found.
Secondly, the shortcoming of the state-based fault diagnosis methods was pointed out and a new diagnosis idea based on information fusion of process was proposed. A dual-channel multi-speed information entropy matrix was constructed by a single shaft vibration signal and a single bearing vibration signal which can best reflect the process change rule of each fault vibration signal. Then the information entropy sample matrix of eight kinds of typical faults in time domain, frequency domain and time-frequency domain were calculated respectively.
Thirdly, a new concept of information exergy based on information fusion of process was first proposed and defined. The process change rule was quantitatively characterized through establishing the fault diagnosis method of information exergy of rotating machinery vibration. Then the information exergy sample matrix of eight kinds of typical faults in time domain, frequency domain and time-frequency domain were calculated respectively. In the calculation example, for the shortcoming of the diagnosis method, the fault diagnosis method of information exergy based on space close degree of information exergy was established.
Fourthly, from another perspective, the process change rule between two corresponding states in the difference process was also defined as a new information exergy. Because the amplitude spectrum of fault signals during speed-up can best reflect the process change rule, a new concept of space-time feature spectrum in frequency domain was defined and then it was used as eigenvalue. On this basis, the fault diagnosis method of information exergy based on space-time feature spectrum in frequency domain was established and the information exergy sample matrix of eight kinds of typical faults based on space-time feature spectrum in frequency domain was constructed respectively. The results show that the method had strong ability of fault classification, and is an effective new diagnosis method of rotating machinery based on information fusion of process.
Finally, a new fault diagnosis system of information exergy of rotating machinery based on information fusion of process was first established. In this system, the systemic research on fault identification, fault orientation and fault intensity of vibration signals were carried out. Its diagnosis approach was: First, the fault type of vibration signal was identified by fault diagnosis method of information exergy of dual-sensor based on space-time feature spectrum in frequency domain. Then the fault location of vibration signal was confirmed by fault orientation method of information exergy of multi-sensor based on space-time feature spectrum in frequency domain. Finally, the fault intensity of vibration signal was confirmed by the original waveform data during speed-up. Analysis showed that the diagnosis system was a powerful, high diagnostic accuracy, strong adaptability new model of fault diagnosis of rotating machinery.
引文
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