基于灰色时序模型的蒸馏装置腐蚀预测

利用灰色GM(1,1)模型预测蒸馏装置腐蚀的宏观变化趋势,再利用时间序列模型对灰色模型的预测误差进行预测.利用灰色时间序列组合模型对蒸馏塔塔顶换热器入口分布管弯头的管壁厚度进行预测并与实际测量值进行比较,该模型比仅用灰色模型预测效果更好.最后利用灰色时序模型对弯管使用寿命进行了预测,为设备检修提供参考依据.     

蓝藻生长时变系统非线性动力学分析及水华预测方法

为解决现有蓝藻生长动力学模型难以有效描述实际水体中蓝藻生长时变系统的非线性动力学特性,导致水华预测准确性不高的问题,构建蓝藻摄食和营养盐循环模型,并考虑水温、光照等主要影响因素随时间变化对蓝藻生长的影响,进一步建立蓝藻生长时变系统非线性动力学模型,对其常值参数采用遗传算法与数值算法结合的方法进行优化率定,对其时变参数采用多元时序方法进行建模预测,根据分岔理论及时变系统理论分析水华暴发行为的非线性动力学机理,实现对蓝藻生长时变系统的水华预测。通过太湖流域监测实例表明,与现有研究相比,引入时变参数的蓝藻生长动力学模型更能反映蓝藻生长时变系统下水华暴发行为的非线性动力学特性,其水华预测结果更为准确。     

航空发动机磨损趋势预测模型研究

文章讨论了神经网络的BP算法和遗传算法,提出用遗传算法来优化BP神经网络,应用遗传算法训练神经网络权重,实现网络结构的优化,用优化后的BP人工神经网络建立了航空发动机磨损故障趋势预测模型,利用发动机的光谱监测数据作为预测磨损趋势的特征参数,进行了模型的训练和预测试验,并将该模型预测结果与BP算法和多元线性回归法的预测结果进行了比较,证明了基于遗传算法的人工神经网络是航空发动机磨损故障趋势预测的一种理想方法。     

基于模拟疫苗的人工免疫系统在氯乙烯聚合间歇过程故障诊断中的应用

根据聚氯乙烯工业生产过程的工艺信息、操作规程和历史数据,利用Aspen软件建立了氯乙烯悬浮聚合间歇过程的动态仿真模型,由模拟得到的多个状态的样本数据生成正常模拟疫苗和故障模拟疫苗,结合工业数据建立正常抗体库和故障抗体库,解决了氯乙烯聚合过程故障诊断中故障样本数据缺乏的问题。利用动态时间弯曲算法和人工免疫系统对该聚合反应间歇过程进行故障诊断,具有较好的诊断效果。     

基于灰色GM(1,1)模型的瓷砖出口量的模拟与预测

以原始时间序列数据为基础,建立瓷砖出口量的模拟和预测改进的GM(1,1)模型,通过实例证明了本文提出的改进模型的精度比常规模型的精度要高,预测结果能为我国建筑陶瓷行业制定经营决策和出口政策提供参考。     

Cycle temporal algorithm-based multivariate statistical methods for fault diagnosis in chemical processes

Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis. In this article,(I) the cycle temporal algorithm(CTA) combined with the dynamic kernel principal component analysis(DKPCA) and the multiway dynamic kernel principal component analysis(MDKPCA) fault detection algorithms are proposed, which are used for continuous and batch process fault detections,respectively. In addition,(II) a fault variable identification model based on reconstructed-based ...     

Chemical process fault diagnosis based on a combined deep learning method

The study on fault detection and diagnosis (FDD) of chemical processes has always been the top priority of the chemical process safety. In this paper, a fault diagnosis method combining the deep convolutional with the recurrent neural network (DCRNN) is proposed. In this method, the data from chemical processes are input to the deep convolutional neural network (DCNN) to extract features in spatial domains, and then, the features are fused into the bidirectional recurrent neural network (BRNN). Due to the powerful capabilities of DCNN to extract features in spatial domains and the sensitivity to time series of RNN, the combined method can adaptively learn the dynamic information of the raw data in both spatial and temporal domains and has unique advantages in multivariate chemical processes. The application of the DCRNN model in the Tennessee Eastman (TE) process demonstrates the high accuracy of this proposal in identifying abnormal conditions for the chemical process, compared with the traditional fault identification algorithms of deep learning.     

基于灰色BP神经网络的水泥强度预测模型研究

水泥强度的预测具有多变量、非线性和大时滞特性,因此传统线性回归方法的结果不准确。除此之外,传统的神经网络预测可能对少量样本不够精确。本文建立灰色BP模型,以此来预测水泥的强度。建立一个多因素灰色模型GM(1,N)用于水泥化学成分的样本数据进行预处理,得到新的数据来作为建立预测模型的样本数据,通过BP神经网络建立预测模型。最终通过建立的灰色BP神经网络预测模型来预测28天水泥强度。仿真结果表明:灰色BP预测模型的效果比BP预测的要准确。     

数据驱动的设备时变可靠度评价及故障预测

针对化工设备的可靠性随时间或载荷作用次数等寿命指标发生变化这一问题,在传统可靠性模型基础上构造了一种考虑设备运行影响因素的可靠度评价方法,可处理化工设备的运行工况、载荷、应力和强度等参数随时间变化的可靠性计算问题。该方法首先利用传统威布尔分布可靠性模型对设备进行可靠性评价,计算设备的可靠性参数;采用灰色关联分析选择影响威布尔分布模型参数的主要因素,然后应用响应面法,在基于威布尔分布的失效速率模型中引入设备运行的影响因素,建立多变量故障失效速率模型,在此基础上对设备进行时变可靠度评价,并对设备进行简单的故障预测。最后将该方法应用于某化工设备,对其进行时变可靠度评价和设备故障时间预测,验证了该方法的有效性。     

Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Time-series prediction is one of themajor methodologies used for fault prediction. Themethods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problemof reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the outputweight of the reservoir neural network. As a result, the amplitude of outputweight is effectively controlled and the ill-posedness problemis solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey-Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data. The final prediction correct rate reaches 81%.     

基于LSTM-RNN模型的铁水硅含量预测

针对高炉炼铁是一个动态过程,具有大延迟,工况复杂的特性。采用LSTM-RNN模型进行硅含量预测,充分发挥了其处理时间序列时挖掘前后关联信息的优势。首先根据时间序列趋势及相关系数选择自变量,并采用复杂工况的实际生产数据进行验证。然后用程序自动求解最优参数进行硅含量预测。最后将LSTM-RNN模型与PLS模型及RNN模型的结果进行对比,验证该方法的优势。研究发现LSTM-RNN模型预测误差稳定,预测精度较高,比传统的统计学及神经网络方法取得了更好的预测精度。     

基于中心损失的条件生成式对抗网络的冷水机组故障诊断

针对冷水机组产生的故障数据不足,数据集中正常数据和故障数据数量不平衡,进而导致故障诊断精度下降的问题,提出一种基于中心损失的条件生成式对抗网络(central loss conditional generative adversarial network,CLCGAN)和支持向量机(support vector machine,SVM)的故障诊断方法。首先,CLCGAN利用少量真实故障数据生成新的故障数据;然后,将生成的故障数据与初始数据集混合,使正常数据与故障数据的数量达到平衡;最后,利用平衡数据集构建SVM模型进行故障诊断。在GAN生成冷水机组故障数据时,构建动态中心损失项并加入到目标函数中,利用动态的中心损失减少冷水机组生成的各种故障数据的类内距离,从而降低各个故障生成数据之间的重叠程度,增加生成数据的可靠性。在生成故障数据之前配置相应的故障标签,并输入到CLCGAN中指导数据生成过程,使生成的故障数据可以均衡地分布于各个故障类别。在ASHRAE 1043-RP数据集上对所提方法进行了验证,结果表明,相较于其他解决数据不平衡问题的故障诊断方法,所提方法具有更高的故障诊断准确率。     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network, AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

基于非等间距GM_GRNN组合模型的转炉应用

建立以终点碳和温度为间距的氧气用量非等间距灰色数列预测模型,并利用广义回归神经网络对灰模预测结果进行非线性组合优化,得到氧气质量的综合预测值.通过采集到的某钢厂实际生产数据,建立氧气质量的组合预测模型,得到平均相对精度达到97.39％的一步预测值.验证结果表明该组合模型是准确而有效的.     

Development of a 3D Hybrid Intelligent‐Mechanistic Model for Simulation of Multiphase Chemical Reactors

A novel intelligent‐mechanistic model was developed to understand the behavior of multiphase chemical reactors. Computational fluid dynamics (CFD) and an intelligent algorithm were combined to predict different levels of 3D cylindrical bubble‐column reactors. An adaptive neuro‐fuzzy inference system (ANFIS) was used as the intelligence algorithm, and different ANFIS parameters were evaluated. With about one third of the training data the method can predict the overall behavior of the gas fraction in the reactor. A number of rules significantly influence the accuracy of the ANFIS method. After finding appropriate parameters, the method is applied for prediction of points which are not simulated with CFD, representing ANFIS mesh refinement. Also, bubble‐column reactors without training of exact values of measured data or numerical results can be predicted. Main advantages are time savings and reduction of computational expenses.     

Data‐based linear Gaussian state‐space model for dynamic process monitoring

This article develops a data‐based linear Gaussian state‐space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state‐space model, and an iterative expectation‐maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

Chemical process fault diagnosis based on enchanted machine‐learning approach

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F₁‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.     

一类非线性动态系统的一致性分散可靠性预测

针对一类带有隐含退化过程的非线性动态系统,提出了一种基于粒子滤波的一致性分散可靠性预测方法。该方法将原系统重叠分解为若干个相互关联的子系统,每个子系统利用其局部测量信息以及子系统间通信得到的信息,估计出原系统的状态和参数,进而预测原系统的可靠性。同时,该方法通过一致性策略使得所有子系统的估计和预测结果保持一致。五容水箱模型的仿真实验结果表明,该方法能够很好地预测系统的可靠性,同时该方法能够使得所有子系统的估计和预测结果保持一致。     

基于潜变量自回归算法的化工过程动态监测方法

从建立潜变量自回归（AR）模型的角度出发，提出了一种基于潜变量自回归（LVAR）算法的化工过程动态建模与监测方法，旨在提取动态潜变量的同时给出各潜变量的AR模型。LVAR算法在最小化潜变量的AR模型残差的约束下，通过同时搜寻投影变换向量与AR系数向量，实现了对动态潜变量的特征提取及其AR模型的建立。此外，LVAR算法通过先提取动态潜变量后提取静态成分信息的方式，有效地区分了采样数据中的自相关性与交叉相关性。在对比实验中，通过比较分析LVAR方法与其他三种典型的动态过程监测方法在经典化工过程对象上的故障监测结果，验证了LVAR方法在动态过程监测上的优越性与可靠性。     

A non-Gaussian pattern matching based dynamic process monitoring approach and its application to cryogenic air separation process

Principal component analysis (PCA) based pattern matching methods have been applied to process monitoring and fault detection. However, the conventional pattern matching approaches do not specifically take into account the non-Gaussian dynamic features in chemical processes. Furthermore, those techniques are more focused on fault detection instead of fault diagnosis. In this study, a non-Gaussian pattern matching based fault detection and diagnosis method is developed and applied to monitor cryogenic air separation process. First, independent component analysis (ICA) models are built on the normal benchmark and monitored data sets along sliding windows. The IC subspaces from the benchmark and monitored data are then extracted to evaluate the non-Gaussian patterns and detect process faults through a mutual information based dissimilarity index. Further, a difference subspace between the two IC subspaces is computed to characterize the divergence of the dynamic and non-Gaussian patterns between the benchmark and monitored data. Subsequently, the mutual information between the IC difference subspace and each process variable direction is defined as a new non-Gaussian contribution index for fault identification and diagnosis. The presented approach is applied to a simulated cryogenic air separation plant and the monitoring results are compared against those of PCA based pattern matching techniques and ICA based monitoring method. The application study demonstrates that the developed non-Gaussian pattern matching approach can effectively monitor the complex air separation process with superior fault detection and diagnosis capability.