离心通风机故障诊断方法及失速预警研究
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摘要
风机属于通用机械范畴,在国民经济各部门都有广泛应用。作为发电厂烟风系统的动力源,风机运行状况是影响电厂的安全、经济运行的重要因素之一。因此,开展风机故障诊断研究,保障风机安全可靠的运行,有着重大的意义。本文以G4-73No8D风机为研究对象,进行风机故障诊断及旋转失速预测研究。主要研究内容及成果如下：
(1)离心风机故障模拟实验研究及故障样本库的建立。基于G4-73No8D离心通风机实验台,实现了风机典型机械振动故障和旋转失速故障模拟,并建立了高速数据采集系统进行数据采集,建立了故障样本库。机械振动故障状态包括不同严重程度、不同发生部位情况下的不平衡、不对中、松动、碰摩等故障状态；失速故障包括不同导流器开度工况下,风机从正常运行状态到强失速状态的渐进过程。
(2)基于小波包及信号复杂度分析的风机智能故障诊断模型研究。实现了振动信号样本熵特征、符号动力学信息熵特征、小波包能量特征、小波包奇异值特征的提取,并对每种特征各自的特点进行了分析,发现不同故障状态下的特征均具有一定的可区分性,为智能故障诊断提供了基础。改进了智能诊断方法,针对神经网络易陷入局部极小、收敛速度慢,SVM模型参数不易选择的问题,提出用增加动量项和自适应调节学习率优化的BP神经网络和粒子群算法改进SVM作为故障分类器,建立了智能故障诊断模型。结果显示,基于复杂度分析和小波包分析的智能诊断模型能够对风机故障进行准确诊断,且计算效率较高。
(3)基于SDP分析和图像匹配相结合的风机故障诊断方法研究。实现了风机各类运行状态下振动信号的SDP变换,得到了不同运行状态振动信号的SDP图,反映了振动信号的特征,体现出风机不同运行状态之间的区别；建立了故障模板图,实现了未知故障SDP图与故障模板图之间的图像匹配,确定了未知故障的种类；对基于单模板、多模板和聚类模板的图像匹配效果进行分析,结果显示基于聚类故障模板图像匹配效果最佳,在保证了匹配准确率的基础上,不增加额外计算量。
(4)基于相空间变换和支持向量回归机的风机旋转失速预测模型研究。以风机从正常状态到强失速状态的渐进信号训练风机失速预测模型,以相空间变换的方法提取信号特征,挖掘出隐藏在一维时间序列中的本质规律,为回归研究提供更充实的信息；以SVR作为回归模型,实现了旋转失速的实时预测,并基于小波分析的方法检测出失速起始点。基于多步预测技术,建立了旋转失速预测模型,实现了提前五步预测失速起始点,满足了失速预警的时间需求。
As an important part of the mechanical equipment, fans have been widely used in lots of departments of the national economy. In power plant, it is the power source of the smoke system, and its running states are directly related to the security and economic operation of the power plant. Therefore, it is of great significance to research the fault diagnosis and ensure the safe and reliable operation of the fan. In this paper, G4-73No8D centrifugal fan is chosen as the study object. The fault diagnosis and the prediction of the rotating stall of the fan are studied based on the experimental research. The main research contents and results are as follows:
(1) The simulation research the build of sample library of fan faults. The simulation of typical failure of the mechanical vibration and the rotating stall of the fan are realized based on the G4-73No8D centrifugal fan test bench. A high speed data acquisition system is established for data acquisition, and the fault sample library is built. The types of the mechanical vibration faults include the imbalance, misalignment, bearing's looseness and rub-impact. The operating states include faults of different severity, occurred in different parts. The simulation of the rotating stall failure is the gradual process of the fan working from the normal operation to the strong stall station under the different working condition of the diverter opening angle.
(2)The intelligent fault diagnosis model of the fan based on wavelet packet and the signal complexity is researched. The sample entropy features, the symbolic dynamics entropy features, the wavelet packet energy features and the wavelet packet singular value features of the vibration signal are extracted, and different features of each feature vector are analyzed. The characteristic is found that different fault conditions have certain distinction, which provides the basis for intelligent fault diagnosis. Researching on the intelligent diagnosis methods, we know that the neural network is easy to fall into the local minimum and the convergence speed is low, the parameters of the SVM model are not easy to choose. Therefore, the BP neural network, improved by increasing the momentum term and the adaptive adjustment vector optimization, and the SVM, improved by the particle swarm optimization, are used to be fault classifier, classify the fault features and then the intelligent fault diagnosis model established. Result shows that the intelligent fault diagnosis model based on the complexity analysis and the wavelet packet analysis can make an accurate diagnosis for the fan faults and the computational efficiency is high.
(3) The fault diagnosis model combined SDP analysis and image matching is established. Symmetrized Dot Pattern (SDP) transforms the vibration signal into SDP graphics within polar coordinates through the corresponding calculation formula; it can fully describe the characteristics of the signal. When all types of the vibration signals of the fan are transformed with the SDP, we can get SDP figure of vibration signals in different operating states. Establish a known fault SDP template map and match the unknown fault SDP chart with fault template image. The matching results are just the fault classification results. The study shows that selecting a suitable template has a great impact on matching results. The matching accuracy rate of failure is low by using a single template, and the multi-fault template increases the number of additional computation and extends the computing time. Clustering fault template by the means of the clustering analysis can ensure the accuracy of matching and will not increase the additional computation.
(4)Establish the rotating stall prediction model of the fan based on the phase space transformation and the support of vector regression. The stall prediction model of the fan is trained by the progressive signal from the normal state to the stall state so as to realize the real-time prediction of rotating stall. Extracting the signal features by the means of phase space transformation and finding the nature of the law hidden in the one-dimensional time sequences provide more substantial information for regression research. Improved SVR regression model is used for real-time prediction of rotating stall and wavelet transform method is used for detecting the starting point of stall. Multi-step prediction is studied for a longer prediction time. The result shows that rotating stall prediction model used in this paper can predict the starting point of stall five steps ahead and meet the time requirements of stall warning.
(1)离心风机故障模拟实验研究及故障样本库的建立。基于G4-73No8D离心通风机实验台,实现了风机典型机械振动故障和旋转失速故障模拟,并建立了高速数据采集系统进行数据采集,建立了故障样本库。机械振动故障状态包括不同严重程度、不同发生部位情况下的不平衡、不对中、松动、碰摩等故障状态；失速故障包括不同导流器开度工况下,风机从正常运行状态到强失速状态的渐进过程。
(2)基于小波包及信号复杂度分析的风机智能故障诊断模型研究。实现了振动信号样本熵特征、符号动力学信息熵特征、小波包能量特征、小波包奇异值特征的提取,并对每种特征各自的特点进行了分析,发现不同故障状态下的特征均具有一定的可区分性,为智能故障诊断提供了基础。改进了智能诊断方法,针对神经网络易陷入局部极小、收敛速度慢,SVM模型参数不易选择的问题,提出用增加动量项和自适应调节学习率优化的BP神经网络和粒子群算法改进SVM作为故障分类器,建立了智能故障诊断模型。结果显示,基于复杂度分析和小波包分析的智能诊断模型能够对风机故障进行准确诊断,且计算效率较高。
(3)基于SDP分析和图像匹配相结合的风机故障诊断方法研究。实现了风机各类运行状态下振动信号的SDP变换,得到了不同运行状态振动信号的SDP图,反映了振动信号的特征,体现出风机不同运行状态之间的区别；建立了故障模板图,实现了未知故障SDP图与故障模板图之间的图像匹配,确定了未知故障的种类；对基于单模板、多模板和聚类模板的图像匹配效果进行分析,结果显示基于聚类故障模板图像匹配效果最佳,在保证了匹配准确率的基础上,不增加额外计算量。
(4)基于相空间变换和支持向量回归机的风机旋转失速预测模型研究。以风机从正常状态到强失速状态的渐进信号训练风机失速预测模型,以相空间变换的方法提取信号特征,挖掘出隐藏在一维时间序列中的本质规律,为回归研究提供更充实的信息；以SVR作为回归模型,实现了旋转失速的实时预测,并基于小波分析的方法检测出失速起始点。基于多步预测技术,建立了旋转失速预测模型,实现了提前五步预测失速起始点,满足了失速预警的时间需求。
As an important part of the mechanical equipment, fans have been widely used in lots of departments of the national economy. In power plant, it is the power source of the smoke system, and its running states are directly related to the security and economic operation of the power plant. Therefore, it is of great significance to research the fault diagnosis and ensure the safe and reliable operation of the fan. In this paper, G4-73No8D centrifugal fan is chosen as the study object. The fault diagnosis and the prediction of the rotating stall of the fan are studied based on the experimental research. The main research contents and results are as follows:
(1) The simulation research the build of sample library of fan faults. The simulation of typical failure of the mechanical vibration and the rotating stall of the fan are realized based on the G4-73No8D centrifugal fan test bench. A high speed data acquisition system is established for data acquisition, and the fault sample library is built. The types of the mechanical vibration faults include the imbalance, misalignment, bearing's looseness and rub-impact. The operating states include faults of different severity, occurred in different parts. The simulation of the rotating stall failure is the gradual process of the fan working from the normal operation to the strong stall station under the different working condition of the diverter opening angle.
(2)The intelligent fault diagnosis model of the fan based on wavelet packet and the signal complexity is researched. The sample entropy features, the symbolic dynamics entropy features, the wavelet packet energy features and the wavelet packet singular value features of the vibration signal are extracted, and different features of each feature vector are analyzed. The characteristic is found that different fault conditions have certain distinction, which provides the basis for intelligent fault diagnosis. Researching on the intelligent diagnosis methods, we know that the neural network is easy to fall into the local minimum and the convergence speed is low, the parameters of the SVM model are not easy to choose. Therefore, the BP neural network, improved by increasing the momentum term and the adaptive adjustment vector optimization, and the SVM, improved by the particle swarm optimization, are used to be fault classifier, classify the fault features and then the intelligent fault diagnosis model established. Result shows that the intelligent fault diagnosis model based on the complexity analysis and the wavelet packet analysis can make an accurate diagnosis for the fan faults and the computational efficiency is high.
(3) The fault diagnosis model combined SDP analysis and image matching is established. Symmetrized Dot Pattern (SDP) transforms the vibration signal into SDP graphics within polar coordinates through the corresponding calculation formula; it can fully describe the characteristics of the signal. When all types of the vibration signals of the fan are transformed with the SDP, we can get SDP figure of vibration signals in different operating states. Establish a known fault SDP template map and match the unknown fault SDP chart with fault template image. The matching results are just the fault classification results. The study shows that selecting a suitable template has a great impact on matching results. The matching accuracy rate of failure is low by using a single template, and the multi-fault template increases the number of additional computation and extends the computing time. Clustering fault template by the means of the clustering analysis can ensure the accuracy of matching and will not increase the additional computation.
(4)Establish the rotating stall prediction model of the fan based on the phase space transformation and the support of vector regression. The stall prediction model of the fan is trained by the progressive signal from the normal state to the stall state so as to realize the real-time prediction of rotating stall. Extracting the signal features by the means of phase space transformation and finding the nature of the law hidden in the one-dimensional time sequences provide more substantial information for regression research. Improved SVR regression model is used for real-time prediction of rotating stall and wavelet transform method is used for detecting the starting point of stall. Multi-step prediction is studied for a longer prediction time. The result shows that rotating stall prediction model used in this paper can predict the starting point of stall five steps ahead and meet the time requirements of stall warning.
引文
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