There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because the...There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.展开更多
为解决传统检测方法在处理复杂、动态以及数据长度实时变化的飞行轨迹数据时特征提取不准确、检测效率较低的问题,提出一种结合长短时记忆(Long Short-Term Memory, LSTM)网络和支持向量数据描述(Support Vector Data Description, SVDD...为解决传统检测方法在处理复杂、动态以及数据长度实时变化的飞行轨迹数据时特征提取不准确、检测效率较低的问题,提出一种结合长短时记忆(Long Short-Term Memory, LSTM)网络和支持向量数据描述(Support Vector Data Description, SVDD)的无监督异常检测方法。利用LSTM网络提取可变长度飞行轨迹的关键特征,并将其转化为固定长度的序列表示;通过SVDD算法构建多维超球分类器,对正常飞行轨迹进行建模,从而识别潜在异常轨迹。为进一步提升模型性能,引入基于梯度的优化算法(Gradient-Based training algorithm, GB),实现LSTM与SVDD参数的联合训练,大幅度提高检测精度和计算效率。仿真实验结果表明,新提出的基于梯度优化的长短时记忆网络和支持向量数据描述模型(Long Short-Term Memory network and Support Vector Data Description model based on Gradient-Based training algorithm optimization, LSTM-GBSVDD)的飞行轨迹异常检测方法在处理复杂、多变的飞行轨迹异常检测任务中表现出较好的有效性和优越性,有较强的应用前景。展开更多
随着数据维度和规模的不断增加,基于深度学习的异常检测方法取得了优异的检测性能,其中深度支持向量数据描述(Deep SVDD)得到了广泛应用。然而,要缓解超球崩溃问题,就需要对Deep SVDD中映射网络的各种参数施加约束。为了进一步提高Deep ...随着数据维度和规模的不断增加,基于深度学习的异常检测方法取得了优异的检测性能,其中深度支持向量数据描述(Deep SVDD)得到了广泛应用。然而,要缓解超球崩溃问题,就需要对Deep SVDD中映射网络的各种参数施加约束。为了进一步提高Deep SVDD中映射网络的特征学习能力,同时解决超球崩溃问题,提出了基于混合高斯先验变分自编码器的深度多球支持向量数据描述(Deep Multiple-Sphere Support Vector Data Description Based on Variational Autoencoder with Mixture-of-Gaussians Prior,DMSVDD-VAE-MoG)。首先,通过预训练初始化网络参数和多个超球中心;其次,利用映射网络获得训练数据的潜在特征,对VAE损失、多个超球的平均半径和潜在特征到所对应超球中心的平均距离进行联合优化,以获得最优网络连接权重和多个最小超球。实验结果表明,所提DMSVDD-VAE-MoG在MNIST,Fashion-MNIST和CIFAR-10上均取得了优于其他8种相关方法的检测性能。展开更多
现有的多模态间歇过程软测量未考虑过程数据的批次差异及过渡模态的复杂时变特性,影响了间歇过程模态识别的合理性及质量变量在线软测量的准确性。提出了一种基于双边界支持向量数据描述-相关向量回归(double boundary support vector d...现有的多模态间歇过程软测量未考虑过程数据的批次差异及过渡模态的复杂时变特性,影响了间歇过程模态识别的合理性及质量变量在线软测量的准确性。提出了一种基于双边界支持向量数据描述-相关向量回归(double boundary support vector data description-relevance vector regression,DBSVDD-RVR)的间歇过程质量变量在线软测量方法。依据间歇过程离线模态划分获得的各稳定及过渡模态历史数据,建立DBSVDD在线模态识别模型,并引入滑动窗,构建间歇过程在线模态识别策略,利用DBSVDD模型实现在线测量数据的模态识别;在此基础上,构建了基于超球体距离的数据相似度计算方法,选择过渡模态在线数据的相似建模数据集,建立过渡模态的即时学习RVR软测量模型,并依据历史数据建立各稳定模态的RVR软测量模型,实现间歇过程质量变量的在线软测量。青霉素发酵过程的实验结果表明,所提方法有效地提高了间歇过程模态识别的合理性和质量变量在线软测量的准确性。展开更多
为解决传统航空发动机异常检测方法准确率和泛化性能较低的问题,提出一种混合核最大相关熵的深度支持向量数据描述(mixed kernel maximum correntropy criterion-deep support vector data description,MKMCC-DSVDD)方法。首先,采用合...为解决传统航空发动机异常检测方法准确率和泛化性能较低的问题,提出一种混合核最大相关熵的深度支持向量数据描述(mixed kernel maximum correntropy criterion-deep support vector data description,MKMCC-DSVDD)方法。首先,采用合成少数类过采样技术扩充异常样本规模,提高对非均衡样本的泛化性能;其次,建立基于混合核改进的最大相关熵损失函数,可以在无须数据分布假设的前提下提升准确率;最后,构建基于MKMCC-DSVDD的航空发动机异常检测方法。在航空发动机气路系统和滑油系统异常检测实验中,所提方法平均曲线下的面积(area under curve,AUC)达到98.53%,表明其具有较高的实用性和泛化性能。展开更多
基金Project(61374140)supported by the National Natural Science Foundation of China
文摘There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.
文摘为解决传统检测方法在处理复杂、动态以及数据长度实时变化的飞行轨迹数据时特征提取不准确、检测效率较低的问题,提出一种结合长短时记忆(Long Short-Term Memory, LSTM)网络和支持向量数据描述(Support Vector Data Description, SVDD)的无监督异常检测方法。利用LSTM网络提取可变长度飞行轨迹的关键特征,并将其转化为固定长度的序列表示;通过SVDD算法构建多维超球分类器,对正常飞行轨迹进行建模,从而识别潜在异常轨迹。为进一步提升模型性能,引入基于梯度的优化算法(Gradient-Based training algorithm, GB),实现LSTM与SVDD参数的联合训练,大幅度提高检测精度和计算效率。仿真实验结果表明,新提出的基于梯度优化的长短时记忆网络和支持向量数据描述模型(Long Short-Term Memory network and Support Vector Data Description model based on Gradient-Based training algorithm optimization, LSTM-GBSVDD)的飞行轨迹异常检测方法在处理复杂、多变的飞行轨迹异常检测任务中表现出较好的有效性和优越性,有较强的应用前景。
文摘随着数据维度和规模的不断增加,基于深度学习的异常检测方法取得了优异的检测性能,其中深度支持向量数据描述(Deep SVDD)得到了广泛应用。然而,要缓解超球崩溃问题,就需要对Deep SVDD中映射网络的各种参数施加约束。为了进一步提高Deep SVDD中映射网络的特征学习能力,同时解决超球崩溃问题,提出了基于混合高斯先验变分自编码器的深度多球支持向量数据描述(Deep Multiple-Sphere Support Vector Data Description Based on Variational Autoencoder with Mixture-of-Gaussians Prior,DMSVDD-VAE-MoG)。首先,通过预训练初始化网络参数和多个超球中心;其次,利用映射网络获得训练数据的潜在特征,对VAE损失、多个超球的平均半径和潜在特征到所对应超球中心的平均距离进行联合优化,以获得最优网络连接权重和多个最小超球。实验结果表明,所提DMSVDD-VAE-MoG在MNIST,Fashion-MNIST和CIFAR-10上均取得了优于其他8种相关方法的检测性能。
文摘现有的多模态间歇过程软测量未考虑过程数据的批次差异及过渡模态的复杂时变特性,影响了间歇过程模态识别的合理性及质量变量在线软测量的准确性。提出了一种基于双边界支持向量数据描述-相关向量回归(double boundary support vector data description-relevance vector regression,DBSVDD-RVR)的间歇过程质量变量在线软测量方法。依据间歇过程离线模态划分获得的各稳定及过渡模态历史数据,建立DBSVDD在线模态识别模型,并引入滑动窗,构建间歇过程在线模态识别策略,利用DBSVDD模型实现在线测量数据的模态识别;在此基础上,构建了基于超球体距离的数据相似度计算方法,选择过渡模态在线数据的相似建模数据集,建立过渡模态的即时学习RVR软测量模型,并依据历史数据建立各稳定模态的RVR软测量模型,实现间歇过程质量变量的在线软测量。青霉素发酵过程的实验结果表明,所提方法有效地提高了间歇过程模态识别的合理性和质量变量在线软测量的准确性。
文摘为解决传统航空发动机异常检测方法准确率和泛化性能较低的问题,提出一种混合核最大相关熵的深度支持向量数据描述(mixed kernel maximum correntropy criterion-deep support vector data description,MKMCC-DSVDD)方法。首先,采用合成少数类过采样技术扩充异常样本规模,提高对非均衡样本的泛化性能;其次,建立基于混合核改进的最大相关熵损失函数,可以在无须数据分布假设的前提下提升准确率;最后,构建基于MKMCC-DSVDD的航空发动机异常检测方法。在航空发动机气路系统和滑油系统异常检测实验中,所提方法平均曲线下的面积(area under curve,AUC)达到98.53%,表明其具有较高的实用性和泛化性能。
