基于数据驱动的密闭鼓风炉故障诊断及预测研究
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摘要
密闭鼓风炉铅锌冶炼过程是一个复杂的物理化学变化过程,具有多变量、非线性、强耦合、大滞后、不确定性等特点,由于原料复杂,成分不稳定,导致炉况波动大、故障多,从而影响铅锌产量和质量。因此,研究密闭鼓风炉的故障诊断与预测新技术,对于保证生产的安全稳定运行,提高企业的经济效益具有重要的现实意义。
本文针对密闭鼓风炉生产过程的特点,利用生产现场采集的大量数据,研究了基于数据驱动的故障检测诊断及预测的新方法。根据密闭鼓风炉生产过程中不同类型故障的特征,提出了基于核密度估计的主元分析故障检测方法、基于主元分析与支持向量机的故障诊断方法、基于案例推理的不完备信息故障诊断方法、基于WLS-SVM的Hammerstein故障预测方法,并将其应用于密闭鼓风炉的实际生产中,取得了明显的效果。主要工作包括以下几个方面:
(1)针对密闭鼓风炉冶炼过程测量数据呈现非正态分布,传统主元分析方法故障检测率低的问题,提出了一种基于核密度估计的主元分析故障检测方法。首先,为了提高建模数据的质量,采取指数加权滑动平均和异常值剔除的方法,对数据进行预处理,建立密闭鼓风炉故障检测的主元模型;然后,采用核密度估计的方法计算主元模型控制限,有效降低了传统方法因控制限不准确引起的故障漏报或误报率,提高了密闭鼓风炉检测系统的敏感度和准确率。
(2)针对密闭鼓风炉数据变量繁多,变量间相关性强的问题,提出了一种主元分析和支持向量机相结合的密闭鼓风炉故障诊断方法。首先利用主元分析方法对过程数据进行特征提取,建立主元分析的检测模型,根据T~2和Q统计量判断检测过程是否超出了正常的控制限,若有故障发生,则检测程序将给予警告提示,提示过程出现了异常操作状况;然后将代表过程特征的主元分量送入到多支持向量机分类器中,利用改进“一对其余”算法进行故障诊断。结果表明,该故障诊断方法具有良好的性能,诊断率更高,所需时间更短。
(3)针对密闭鼓风炉数据存在不完备信息的问题,提出了基于案例技术的不完备信息故障诊断方法。通过基于距离或基于关联度测量的方法,测量未知特征向量与历史数据库中的样本之间的相似度,检索出最优相似的样本,填补缺失特征值得到完备的样本。结果表明,该方法能够解决采集样本中特征值缺失的问题,具有较高的故障诊断率。
(4)针对密闭鼓风炉铅锌冶炼过程存在非线性、时变性,使用传统方法构建预测模型性能较差的问题,提出基于加权最小二乘支持向量机(WLS-SVM)的Hammerstein故障预测模型的方法。首先根据最小二乘支持向量机(LS-SVM)的误差分布对误差进行加权,采用WLS-SVM回归方法构造Hammerstein模型非线性函数部分,再运用奇异值分解的方法辨识Hammerstein模型参数。在此基础上,建立密闭鼓风炉的故障预测模型,通过对炉子关键参数进行预测,实现对炉况的预测,降低了故障发生率。
(5)设计和开发了基于数据驱动的密闭鼓风炉铅锌冶炼过程故障诊断及预测系统。实际应用结果表明:该系统实现了炉况运行状态和运行趋势的检测预报,实现了异常炉况的报警与诊断,有利于密闭鼓风炉的稳定运行。
The smelting process of the Imperial Smelting Furnace (ISF) involves various complex physicochemical reactions, characterized by properties like multivariate, nonlinearity, strong coupling, large delay and uncertainty, etc. The complex and unstable component of the raw materials lead to the furnace running unsteadily, which consequently cause the poor output and quality of Lead-Zinc. Therefore, research on the new effective methods for the ISF fault diagnosis and prediction has an important and practical significance for guaranteeing the production safety and stability and improving industry economy profits.
In view of the characteristics of Imperial Smelting Process (ISP), this thesis focuses the research on new methods of fault detection, fault diagnosis and prediction based on the data-driven technique. These methods presented are as follows: the improved fault detection method based on principal component analysis (PCA); the fault diagnosis approach based on PCA and multi-class classifiers of SVM; a novel fault diagnosis strategy for the incomplete samples based on Case-Based Reasoning (CBR) technique and SVM; the new fault prediction method based on Hammerstein model using weighted least squares support vector machines (WLS-SVM). All the methods are applied to the fault diagnosis and prediction system in the melting process of the imperial smelting furnace (ISF), and achieve good performances. The major innovation research achievements include:
(1) Considering the characteristic of the ISP data in abnormal distribution, the novel fault detection method based on the improved principal component analysis (PCA) is presented. Firstly, the data are preprocessed by the exponentially weighted moving average (EWMA) and the improved method for the abnormal value removal, and the PCA model for the ISF is obtained. And then, the control limit of the PCA model is calculated by the method of multivariate kernel density estimation (KDE), which helps to reduce the false alarm rate or missing alarm rate of the traditional PCA model, and increase the sensitivity of the monitoring process and improve the detection of the ISF.
(2) A fault diagnosis approach of the ISF based on PCA and multi-class classifiers of SVM is proposed. Firstly, the PCA approach is adopted to extract the feature, and the monitoring model is established. And then the reconstructed data are analyzed by the Q and T~2 statistics to determine if the supervised process exceeds the normal control limits. If fault occurs, then the supervising program will alarm to prompt that the process has abnormal states. Secondly, the SVM multi-class classifiers with 'one to other' algorithm are used for classification with the input of the feature. Experimental results have demonstrated that the proposed methods achieve excellent performance with high diagnosis accuracy and rapid speed.
(3) For the incomplete samples for ISF, this paper presents a novel fault diagnosis strategy based on Case-Based Reasoning (CBR) technique. Firstly, the similarities between the retrieval feature vector and the complete samples in the historical database are measured by the method of distance measure or the relevance measure, the optimal case is retrieved and the imputation is obtained, finally the generated complete samples are inputted to the fault diagnosis models to diagnose the ISF system. The presented strategy is validated and has achieved satisfying results.
(4) The ISP has characteristics such as nonlinearity, strong coupling, time-varying parameters. It is hard to predict its condition precisely by traditional method under fault condition. To solve this problem, the new fault prediction method based on Hammerstein model using weighted least squares support vector machines (WLS-SVM) is presented. Firstly, the error variables of the least squares support vector machines are weighted according to their distributions, and then the nonlinear function of Hammerstein model is constructed by the weighted least squares support vector machines regression. A numerical algorithm for subspace system (singular value decomposition, SVD) is utilized to identify the Hammerstein model. Finally, the fault prediction model for ISF is used to predict the key parameters and dynamic behavior of the furnace, which can reduce the failure rate and make the furnace steady run smoothly.
(5) A data-driven fault diagnosis and prediction system of the ISP is designed and developed. It is able to forecast running state, track the trend of the running furnace, and give alarm and diagnose signals for abnormal parameters and states.
本文针对密闭鼓风炉生产过程的特点,利用生产现场采集的大量数据,研究了基于数据驱动的故障检测诊断及预测的新方法。根据密闭鼓风炉生产过程中不同类型故障的特征,提出了基于核密度估计的主元分析故障检测方法、基于主元分析与支持向量机的故障诊断方法、基于案例推理的不完备信息故障诊断方法、基于WLS-SVM的Hammerstein故障预测方法,并将其应用于密闭鼓风炉的实际生产中,取得了明显的效果。主要工作包括以下几个方面:
(1)针对密闭鼓风炉冶炼过程测量数据呈现非正态分布,传统主元分析方法故障检测率低的问题,提出了一种基于核密度估计的主元分析故障检测方法。首先,为了提高建模数据的质量,采取指数加权滑动平均和异常值剔除的方法,对数据进行预处理,建立密闭鼓风炉故障检测的主元模型;然后,采用核密度估计的方法计算主元模型控制限,有效降低了传统方法因控制限不准确引起的故障漏报或误报率,提高了密闭鼓风炉检测系统的敏感度和准确率。
(2)针对密闭鼓风炉数据变量繁多,变量间相关性强的问题,提出了一种主元分析和支持向量机相结合的密闭鼓风炉故障诊断方法。首先利用主元分析方法对过程数据进行特征提取,建立主元分析的检测模型,根据T~2和Q统计量判断检测过程是否超出了正常的控制限,若有故障发生,则检测程序将给予警告提示,提示过程出现了异常操作状况;然后将代表过程特征的主元分量送入到多支持向量机分类器中,利用改进“一对其余”算法进行故障诊断。结果表明,该故障诊断方法具有良好的性能,诊断率更高,所需时间更短。
(3)针对密闭鼓风炉数据存在不完备信息的问题,提出了基于案例技术的不完备信息故障诊断方法。通过基于距离或基于关联度测量的方法,测量未知特征向量与历史数据库中的样本之间的相似度,检索出最优相似的样本,填补缺失特征值得到完备的样本。结果表明,该方法能够解决采集样本中特征值缺失的问题,具有较高的故障诊断率。
(4)针对密闭鼓风炉铅锌冶炼过程存在非线性、时变性,使用传统方法构建预测模型性能较差的问题,提出基于加权最小二乘支持向量机(WLS-SVM)的Hammerstein故障预测模型的方法。首先根据最小二乘支持向量机(LS-SVM)的误差分布对误差进行加权,采用WLS-SVM回归方法构造Hammerstein模型非线性函数部分,再运用奇异值分解的方法辨识Hammerstein模型参数。在此基础上,建立密闭鼓风炉的故障预测模型,通过对炉子关键参数进行预测,实现对炉况的预测,降低了故障发生率。
(5)设计和开发了基于数据驱动的密闭鼓风炉铅锌冶炼过程故障诊断及预测系统。实际应用结果表明:该系统实现了炉况运行状态和运行趋势的检测预报,实现了异常炉况的报警与诊断,有利于密闭鼓风炉的稳定运行。
The smelting process of the Imperial Smelting Furnace (ISF) involves various complex physicochemical reactions, characterized by properties like multivariate, nonlinearity, strong coupling, large delay and uncertainty, etc. The complex and unstable component of the raw materials lead to the furnace running unsteadily, which consequently cause the poor output and quality of Lead-Zinc. Therefore, research on the new effective methods for the ISF fault diagnosis and prediction has an important and practical significance for guaranteeing the production safety and stability and improving industry economy profits.
In view of the characteristics of Imperial Smelting Process (ISP), this thesis focuses the research on new methods of fault detection, fault diagnosis and prediction based on the data-driven technique. These methods presented are as follows: the improved fault detection method based on principal component analysis (PCA); the fault diagnosis approach based on PCA and multi-class classifiers of SVM; a novel fault diagnosis strategy for the incomplete samples based on Case-Based Reasoning (CBR) technique and SVM; the new fault prediction method based on Hammerstein model using weighted least squares support vector machines (WLS-SVM). All the methods are applied to the fault diagnosis and prediction system in the melting process of the imperial smelting furnace (ISF), and achieve good performances. The major innovation research achievements include:
(1) Considering the characteristic of the ISP data in abnormal distribution, the novel fault detection method based on the improved principal component analysis (PCA) is presented. Firstly, the data are preprocessed by the exponentially weighted moving average (EWMA) and the improved method for the abnormal value removal, and the PCA model for the ISF is obtained. And then, the control limit of the PCA model is calculated by the method of multivariate kernel density estimation (KDE), which helps to reduce the false alarm rate or missing alarm rate of the traditional PCA model, and increase the sensitivity of the monitoring process and improve the detection of the ISF.
(2) A fault diagnosis approach of the ISF based on PCA and multi-class classifiers of SVM is proposed. Firstly, the PCA approach is adopted to extract the feature, and the monitoring model is established. And then the reconstructed data are analyzed by the Q and T~2 statistics to determine if the supervised process exceeds the normal control limits. If fault occurs, then the supervising program will alarm to prompt that the process has abnormal states. Secondly, the SVM multi-class classifiers with 'one to other' algorithm are used for classification with the input of the feature. Experimental results have demonstrated that the proposed methods achieve excellent performance with high diagnosis accuracy and rapid speed.
(3) For the incomplete samples for ISF, this paper presents a novel fault diagnosis strategy based on Case-Based Reasoning (CBR) technique. Firstly, the similarities between the retrieval feature vector and the complete samples in the historical database are measured by the method of distance measure or the relevance measure, the optimal case is retrieved and the imputation is obtained, finally the generated complete samples are inputted to the fault diagnosis models to diagnose the ISF system. The presented strategy is validated and has achieved satisfying results.
(4) The ISP has characteristics such as nonlinearity, strong coupling, time-varying parameters. It is hard to predict its condition precisely by traditional method under fault condition. To solve this problem, the new fault prediction method based on Hammerstein model using weighted least squares support vector machines (WLS-SVM) is presented. Firstly, the error variables of the least squares support vector machines are weighted according to their distributions, and then the nonlinear function of Hammerstein model is constructed by the weighted least squares support vector machines regression. A numerical algorithm for subspace system (singular value decomposition, SVD) is utilized to identify the Hammerstein model. Finally, the fault prediction model for ISF is used to predict the key parameters and dynamic behavior of the furnace, which can reduce the failure rate and make the furnace steady run smoothly.
(5) A data-driven fault diagnosis and prediction system of the ISP is designed and developed. It is able to forecast running state, track the trend of the running furnace, and give alarm and diagnose signals for abnormal parameters and states.
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