基于多路传感器信息融合的旋转机械故障诊断方法研究
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摘要
旋转机械是应用最为广泛的机械设备之一,对于转速较高的旋转机械,其零、部件一旦发生故障,整个生产过程会无法正常进行,甚至发生灾难性事故,因此对旋转机械故障诊断的研究具有一定的学术意义和社会经济意义。旋转机械故障诊断包括信号的采集、特征的提取、故障状态诊断和故障状态分析四个环节。本论文首先对多路传感器获取的信号进行降噪,然后对获得的混合信号进行分离,提取特征信息,最后应用信息融合技术进行旋转机械的故障诊断和分析。
本论文对旋转机械故障诊断主要进行了以下方面的研究：
1)确定了适合旋转机械故障诊断相适应的解决方案和不确定性推理方法。由于旋转机械存在着本身特性、检测手段、工作环境和信号采集过程的不确定性,知识的不确定性和结论的不确定性,所以在旋转机械故障诊断过程中,存在着许多不可预测因素,根据旋转机械不确定性的处理原则,确定了适合旋转机械故障诊断相适应的解决方案和不确定性推理方法。
2)通过对旋转机械非平稳振动信号的降噪方法的研究,提出了一种新的小波阈值滤波降噪算法。该部分详细研究了新阈值降噪的特性,将其结果与软阈值和硬阈值降噪方法进行了对比,验证了新的小波阈值滤波降噪算法具有信噪比高,均方根误差小的良好降噪效果。
3)通过对混合信号分离方法的研究,提出了基于EMD和FastICA阈值分离算法。对多路传感器获得的旋转机械振动的混合信号难以提取其特征信息的问题,用基于EMD和FastICA阈值分离的算法,解决了仅用FastICA算法分离混合信号时,存在观测信号的数目少于源信号的数目而导致信号分离效果极差的问题。基于EMD和FastICA分离算法得到分离信号后再进行阈值降噪,有效的提取了旋转机械振动信号的故障特征。EMD分解法将信号分解为若干个imf分量,再对每个imf分量进行Hilbert变换得到瞬时频率和瞬时幅值,得到信号的Hilbert谱,表示了信号完整的时间-频率分布。利用EMD分解的imf分量对信号数目进行调整,提高了FastICA分离性能,有效地保证了旋转机械设备故障特征信息的提取。
4)对BP神经网络学习效率低,收敛速度较慢,并且容易在局部极小值处收敛的问题,提出了基于FastICA遗传神经网络算法。该算法首先应用FastICA算法对带噪的旋转机械混合信号进行估计,得到了源信号完全分离的多个独立分量估计,其次用遗传算法优化BP神经网络的权值和阈值,得到优化的BP神经网络,最后将源信号经过FastICA算法得到的多个独立分量估计的归一化能量作为遗传神经网络的输入,应用到遗传神经网络的训练和预测中,进行旋转机械故障的模式识别。该方法保证了神经网络训练过程的全局收敛性,提高了故障识别能力和精度。
5)针对多传感器信息在故障诊断中可能存在伪证据,造成证据间剧烈冲突使经典证据理论合成规则失效的问题,提出了一种基于伪证据识别的D-S组合规则。首先,利用D-S合成规则中一致证据的聚焦性,进行伪证据识别,并将被识别的伪证据抽取；其次,将伪证据与矛盾证据重新构造新的证据作为替代证据；最后,将替代证据取代被识别伪证据,进行D-S证据合成,削弱了伪证据的不良影响。利用该组合规则对发动机故障进行了诊断,并将诊断结果与其它D-S合成规则诊断结果进行比较,验证了该规则的有效性和优越性。
Rotating machinery is one of the most widely used mechanical equipments. If high speed parts of rotating machinery are malfunction, the whole production fails to work properly, and even the disaster accident may take place. Therefore, the research on fault diagnosis for rotating machinery has, not only theoretical significance and academic value, but also society and economy value. Fault diagnosis for rotating machinery consists of signal collection, feature extraction, diagnosis and analysis of fault state. First, multi-sensor signals are de-noised. Second, individual source signal is separated from the mixed signals to extract characteristic information. Third, the rotating machinery faults are diagnosed based on information fusion technology.
The innovated achievements of this dissertation about fault diagnosis for rotating machinery are summed up as follows:
1) Uncertainty of fault diagnosis for rotating machinery is identified. There are some uncertainties in testing method, work environment, process of data acquisition and characteristic of rotating machinery, some uncertainty of knowledge and conclusion, and lots of unpredictable factors in rotating machinery fault diagnosis. Therefore, uncertainty of fault diagnosis for rotating machinery is determined according to uncertainty principles.
2) Targeting at non-stationary vibration signals de-noising of rotating machinery, a new kind of wavelet threshold de-noising method is proposed. The characteristic of the suggested method is analyzed in depth. Through comparing the results of signals de-noising with soft and hard threshold value method, it shows that the proposed method has high signal-noise ratio and smaller RMS error.
3) A new method based on EMD, FastICA and threshold algorithm is proposed through researching separated individual source signal from the mixed signals to extract characteristic information. The proposed method solves the problem that extracting features from the mixed vibration signals of rotating machinery obtained by multi-sensor is difficult. Furthermore, when the number of observed signals is less than the number of source signals, the effect of sources separation by FastICA algorithm is poor. The method based on EMD and FastICA algorithm can separated signals, do wavelet threshold de-noising and get fault characteristic information of machinery vibratory signal. EMD is used to decompose the vibratory signals with noises to obtain a number of IMF and then instantaneous frequency and amplitude of the signal can be obtained from each IMF after Hilbert transforming and the Hilbert spectrum is obtained which shows the complete distribution of time and frequency. In this way, enough signals are obtained, then the separated source signals are de-noised, and the features of rotating machinery vibration signals are extracted effectively.
4) To overcome intrinsic shortcomings of BP neural network, including low learning efficiency, slow convergence rate and easy trapping in local minimum, a new genetic neural network based on FastICA is proposed. First, the mixed signals of rotating machinery by FastICA algorithm are estimated, many individual estimations are obtained from the source signals. Second, the genetic algorithm is used to optimize the weights and thresholds of BP neural network. Third, the normalized power of independent estimation from source signals by FastICA algorithm is obtained, they are put into the genetic neural network to be trained and predicted, and pattern recognition of the rotating machinery fault is shown. The proposed method ensures the global astringency of neural network training and improves the ability and accuracy of fault identification.
5) The high evidence conflict is caused by the false evidence in multi-sensor systems, and it can make the D-S combination rule out of action. To reduce the negative impact of the false evidence, a D-S combination rule base on false evidence identification is proposed and it is used as follows:first, the false evidence is identified according to consistent evidence focus of the D-S combination rule and extracted of false evidence. Second, the false evidence and conflicting evidence are restructured as an substitute evidence. Third, the substitute evidence is instead of false evidence to be composed by D-S combination rule and weaken the influence of the false evidence. Comparing the diagnosis result of the new method with results of other methods in the engine fault diagnosis, the diagnosis result of the new method turns out to be more accurate. The new false evidence identification method is proved to be credible and excellent.
本论文对旋转机械故障诊断主要进行了以下方面的研究：
1)确定了适合旋转机械故障诊断相适应的解决方案和不确定性推理方法。由于旋转机械存在着本身特性、检测手段、工作环境和信号采集过程的不确定性,知识的不确定性和结论的不确定性,所以在旋转机械故障诊断过程中,存在着许多不可预测因素,根据旋转机械不确定性的处理原则,确定了适合旋转机械故障诊断相适应的解决方案和不确定性推理方法。
2)通过对旋转机械非平稳振动信号的降噪方法的研究,提出了一种新的小波阈值滤波降噪算法。该部分详细研究了新阈值降噪的特性,将其结果与软阈值和硬阈值降噪方法进行了对比,验证了新的小波阈值滤波降噪算法具有信噪比高,均方根误差小的良好降噪效果。
3)通过对混合信号分离方法的研究,提出了基于EMD和FastICA阈值分离算法。对多路传感器获得的旋转机械振动的混合信号难以提取其特征信息的问题,用基于EMD和FastICA阈值分离的算法,解决了仅用FastICA算法分离混合信号时,存在观测信号的数目少于源信号的数目而导致信号分离效果极差的问题。基于EMD和FastICA分离算法得到分离信号后再进行阈值降噪,有效的提取了旋转机械振动信号的故障特征。EMD分解法将信号分解为若干个imf分量,再对每个imf分量进行Hilbert变换得到瞬时频率和瞬时幅值,得到信号的Hilbert谱,表示了信号完整的时间-频率分布。利用EMD分解的imf分量对信号数目进行调整,提高了FastICA分离性能,有效地保证了旋转机械设备故障特征信息的提取。
4)对BP神经网络学习效率低,收敛速度较慢,并且容易在局部极小值处收敛的问题,提出了基于FastICA遗传神经网络算法。该算法首先应用FastICA算法对带噪的旋转机械混合信号进行估计,得到了源信号完全分离的多个独立分量估计,其次用遗传算法优化BP神经网络的权值和阈值,得到优化的BP神经网络,最后将源信号经过FastICA算法得到的多个独立分量估计的归一化能量作为遗传神经网络的输入,应用到遗传神经网络的训练和预测中,进行旋转机械故障的模式识别。该方法保证了神经网络训练过程的全局收敛性,提高了故障识别能力和精度。
5)针对多传感器信息在故障诊断中可能存在伪证据,造成证据间剧烈冲突使经典证据理论合成规则失效的问题,提出了一种基于伪证据识别的D-S组合规则。首先,利用D-S合成规则中一致证据的聚焦性,进行伪证据识别,并将被识别的伪证据抽取；其次,将伪证据与矛盾证据重新构造新的证据作为替代证据；最后,将替代证据取代被识别伪证据,进行D-S证据合成,削弱了伪证据的不良影响。利用该组合规则对发动机故障进行了诊断,并将诊断结果与其它D-S合成规则诊断结果进行比较,验证了该规则的有效性和优越性。
Rotating machinery is one of the most widely used mechanical equipments. If high speed parts of rotating machinery are malfunction, the whole production fails to work properly, and even the disaster accident may take place. Therefore, the research on fault diagnosis for rotating machinery has, not only theoretical significance and academic value, but also society and economy value. Fault diagnosis for rotating machinery consists of signal collection, feature extraction, diagnosis and analysis of fault state. First, multi-sensor signals are de-noised. Second, individual source signal is separated from the mixed signals to extract characteristic information. Third, the rotating machinery faults are diagnosed based on information fusion technology.
The innovated achievements of this dissertation about fault diagnosis for rotating machinery are summed up as follows:
1) Uncertainty of fault diagnosis for rotating machinery is identified. There are some uncertainties in testing method, work environment, process of data acquisition and characteristic of rotating machinery, some uncertainty of knowledge and conclusion, and lots of unpredictable factors in rotating machinery fault diagnosis. Therefore, uncertainty of fault diagnosis for rotating machinery is determined according to uncertainty principles.
2) Targeting at non-stationary vibration signals de-noising of rotating machinery, a new kind of wavelet threshold de-noising method is proposed. The characteristic of the suggested method is analyzed in depth. Through comparing the results of signals de-noising with soft and hard threshold value method, it shows that the proposed method has high signal-noise ratio and smaller RMS error.
3) A new method based on EMD, FastICA and threshold algorithm is proposed through researching separated individual source signal from the mixed signals to extract characteristic information. The proposed method solves the problem that extracting features from the mixed vibration signals of rotating machinery obtained by multi-sensor is difficult. Furthermore, when the number of observed signals is less than the number of source signals, the effect of sources separation by FastICA algorithm is poor. The method based on EMD and FastICA algorithm can separated signals, do wavelet threshold de-noising and get fault characteristic information of machinery vibratory signal. EMD is used to decompose the vibratory signals with noises to obtain a number of IMF and then instantaneous frequency and amplitude of the signal can be obtained from each IMF after Hilbert transforming and the Hilbert spectrum is obtained which shows the complete distribution of time and frequency. In this way, enough signals are obtained, then the separated source signals are de-noised, and the features of rotating machinery vibration signals are extracted effectively.
4) To overcome intrinsic shortcomings of BP neural network, including low learning efficiency, slow convergence rate and easy trapping in local minimum, a new genetic neural network based on FastICA is proposed. First, the mixed signals of rotating machinery by FastICA algorithm are estimated, many individual estimations are obtained from the source signals. Second, the genetic algorithm is used to optimize the weights and thresholds of BP neural network. Third, the normalized power of independent estimation from source signals by FastICA algorithm is obtained, they are put into the genetic neural network to be trained and predicted, and pattern recognition of the rotating machinery fault is shown. The proposed method ensures the global astringency of neural network training and improves the ability and accuracy of fault identification.
5) The high evidence conflict is caused by the false evidence in multi-sensor systems, and it can make the D-S combination rule out of action. To reduce the negative impact of the false evidence, a D-S combination rule base on false evidence identification is proposed and it is used as follows:first, the false evidence is identified according to consistent evidence focus of the D-S combination rule and extracted of false evidence. Second, the false evidence and conflicting evidence are restructured as an substitute evidence. Third, the substitute evidence is instead of false evidence to be composed by D-S combination rule and weaken the influence of the false evidence. Comparing the diagnosis result of the new method with results of other methods in the engine fault diagnosis, the diagnosis result of the new method turns out to be more accurate. The new false evidence identification method is proved to be credible and excellent.
引文
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