针对化学工业过程中存在的强非线性和时变特性等问题,提出了一种基于牛顿-拉夫逊优化算法(Newton-Raphson based optimizer,NRBO)驱动的堆叠长短期记忆网络(stacked long short-term memory network,SLSTM)的运行状态评价方法。该方法...针对化学工业过程中存在的强非线性和时变特性等问题,提出了一种基于牛顿-拉夫逊优化算法(Newton-Raphson based optimizer,NRBO)驱动的堆叠长短期记忆网络(stacked long short-term memory network,SLSTM)的运行状态评价方法。该方法通过堆叠多层LSTM网络并引入Dropout层,增强了时序数据的表达能力。同时利用NRBO算法的二阶导数优化特性,有效提高了模型的收敛速度和分类精度,避免了传统LSTM评价方法在高维参数空间中易陷入局部最优的问题。在Tennessee Eastman(TE)过程的实验验证中,所提方法的预测准确率达到了99.31%,显著优于其他几种对比方法。针对非优状态,提出了基于主元分析和组套索正则化贡献(principal component analysis and group lasso regularization contribution,PCA-GLC)相结合的非优因素识别方法,该方法能够有效识别关键变量,减少误判和干扰,为工业过程的实时调整提供准确依据。在TE过程的实验验证中,所提方法相对于基于PCA的图贡献法,对关键变量的识别更加准确,并且降低了其他变量对结果的干扰。展开更多
Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a nove...Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.展开更多
文摘针对化学工业过程中存在的强非线性和时变特性等问题,提出了一种基于牛顿-拉夫逊优化算法(Newton-Raphson based optimizer,NRBO)驱动的堆叠长短期记忆网络(stacked long short-term memory network,SLSTM)的运行状态评价方法。该方法通过堆叠多层LSTM网络并引入Dropout层,增强了时序数据的表达能力。同时利用NRBO算法的二阶导数优化特性,有效提高了模型的收敛速度和分类精度,避免了传统LSTM评价方法在高维参数空间中易陷入局部最优的问题。在Tennessee Eastman(TE)过程的实验验证中,所提方法的预测准确率达到了99.31%,显著优于其他几种对比方法。针对非优状态,提出了基于主元分析和组套索正则化贡献(principal component analysis and group lasso regularization contribution,PCA-GLC)相结合的非优因素识别方法,该方法能够有效识别关键变量,减少误判和干扰,为工业过程的实时调整提供准确依据。在TE过程的实验验证中,所提方法相对于基于PCA的图贡献法,对关键变量的识别更加准确,并且降低了其他变量对结果的干扰。
基金Project (61203021) supported by the National Natural Science Foundation of ChinaProject (2011216011) supported by the Key Science and Technology Program of Liaoning Province,China+1 种基金Project (2013020024) supported by the Natural Science Foundation of Liaoning Province,ChinaProject (LJQ2015061) supported by the Program for Liaoning Excellent Talents in Universities,China
文摘Fault diagnosis plays an important role in complicated industrial process.It is a challenging task to detect,identify and locate faults quickly and accurately for large-scale process system.To solve the problem,a novel Multi Boost-based integrated ENN(extension neural network) fault diagnosis method is proposed.Fault data of complicated chemical process have some difficult-to-handle characteristics,such as high-dimension,non-linear and non-Gaussian distribution,so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features.Then,the affinity propagation(AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time.Afterwards,an integrated ENN classifier based on Multi Boost strategy is constructed to identify fault types.The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters.Finally,a real industrial system—Tennessee Eastman(TE) process is employed to evaluate the performance of the proposed method.And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.
