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
针对快速扩展随机树(rapid-exploration random trees,RRT)算法难以有效解决多场景环境下的机械臂快速运动规划问题,提出一种融合长短时记忆机制的快速运动规划算法.首先,采用高斯混合模型(Gaussian mixture models,GMM)在规划的初始阶...针对快速扩展随机树(rapid-exploration random trees,RRT)算法难以有效解决多场景环境下的机械臂快速运动规划问题,提出一种融合长短时记忆机制的快速运动规划算法.首先,采用高斯混合模型(Gaussian mixture models,GMM)在规划的初始阶段通过随机采样构建环境的场景模型,并利用该模型进行碰撞检测,以提高运动规划效率;然后,根据人类的记忆机制原理,对多场景的不同GMM按照即时记忆、短期记忆和长期记忆进行存储,并通过场景匹配算法实现不同场景GMM的快速自适应提取,提高对变化环境的适应能力;最后,通过在Matlab以及ROS仿真环境下6自由度柔性机械臂的运动规划仿真实验对所提出的算法进行验证.实验结果表明,所提出算法可以快速提取场景的记忆信息,有效提高多场景环境下的运动规划效率,具有较强的适应性.展开更多
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.
文摘针对快速扩展随机树(rapid-exploration random trees,RRT)算法难以有效解决多场景环境下的机械臂快速运动规划问题,提出一种融合长短时记忆机制的快速运动规划算法.首先,采用高斯混合模型(Gaussian mixture models,GMM)在规划的初始阶段通过随机采样构建环境的场景模型,并利用该模型进行碰撞检测,以提高运动规划效率;然后,根据人类的记忆机制原理,对多场景的不同GMM按照即时记忆、短期记忆和长期记忆进行存储,并通过场景匹配算法实现不同场景GMM的快速自适应提取,提高对变化环境的适应能力;最后,通过在Matlab以及ROS仿真环境下6自由度柔性机械臂的运动规划仿真实验对所提出的算法进行验证.实验结果表明,所提出算法可以快速提取场景的记忆信息,有效提高多场景环境下的运动规划效率,具有较强的适应性.
