多模态化工过程动态多点故障监测方法

为了提高多模态化工过程非高斯变量监测的准确性，针对传统方法采用静态控制限不能排除过程噪声干扰而产生大量误报警的问题，提出一种多模态化工过程动态多点故障监测方法。首先将化工过程划分为平稳模态和过渡模态，基于自回归模型和粒子群优化的独立成分分析算法，构造平稳模态的单点监测统计量和多点异常统计量，建立起平稳模态的非高斯监测模型，基于粒子群优化的独立成分分析算法构造过渡模态的非高斯监测模型。平稳模态监测模型和过渡模态监测模型均采用动态监控策略，实现在线故障监测。将多模态动态多点监测方法应用到丙烯计量罐装置中，结果表明，该方法的监测漏报率小于0.8%，大大提高了报警的准确性。与3σ阈值监测法相比，误报率降低了6.33%，并控制在1.2%以内。

Dynamic Multi-Point Fault Monitoring Method for Multi Model Chemical Process

In order to improve the accuracy of non-Gaussian variables monitoring in multi-modal chemical process, aiming at the problem that the normal transient fluctuation of the process variable was misjudged as the process error caused by the static control limit in the traditional multivariate statistical monitoring method, a dynamic multi-point fault monitoring method was proposed. Firstly, the chemical process was divided into stationary mode and transition mode. Based on the independent component analysis algorithm of the autoregressive model and particle swarm optimization, the single-point monitoring statistic and the multi-point anomaly statistic of the stationary mode were constructed, and then the stationary mode non-Gaussian monitoring model was founded. Based on the particle swarm optimization.Independent component analysis algorithm, the non-Gaussian monitoring model of the transition mode was structured. Dynamic monitoring strategy was used in both the stationary mode monitoring model and the transition mode monitoring model to realize on-line fault monitoring. The multi-modal dynamic multi-point monitoring method was applied to the propylene metering tank device. Test results showed that false negative rate was less than 0.8%; false alarm rate was reduced by 6.33% compared with the 3σ  threshold value monitoring method, and well controlled within 1.2%.