【摘要】 实际工业过程中往往包含不同运行工况,且每种工况数据一般不服从同一种分布.数据的多分布性和分布的不确定性使得传统的故障诊断方法难以获得满意的效果,因此提出一种基于局部邻域和贝叶斯推断的多工况故障诊断方法.首先,通过局部邻域标准化算法对多工况数据进行预处理;再利用ICA-PCA(independent component analysis and principal component analysis)方法分别对该数据集的高斯特性和非高斯特性进行分析处理,获得全局模型;然后结合贝叶斯推断将多个统计量组合成一个监测统计量,实现多工况过程的在线监测;最后通过数值例子和TE过程的仿真研究,验证了提出方法的可行性和有效性.更多还原

【Abstract】 Practical industrial processes are often characterized by different operation modes,in which the sampling data no longer follow the same distribution.Multi-distribution characteristics and distribution uncertainty of the sampled data have made traditional fault diagnosis methods difficult.In this paper,a novel process monitoring method,based on local neighborhood standardization and Bayesian inference,is proposed for a multimode process.First,the data is preprocessed through a local neighborhood standardization algorithm.Second,an independent component analysis and principal component analysis(ICA-PCA) method is used to extract the Gaussian and non-Gaussian characteristics of the process dataset.A monitoring statistic can then be obtained to realize online monitoring,by combining multiple statistics with the Bayesian inference approach.Finally,the reliability and effectiveness of the proposed method are verified through simulation results of a numerical example and the Tennessee Eastman(TE) process.更多还原