基于独立子空间算法与集成策略的仪表微小故障诊断方法

摘要

To solve the problem of small fault detection of instruments in process industry,independent components were extracted by Independent Component Analysis (ICA) from instruments recorded data.And independent component subspaces were established according to the contribution matrix.Fault detection model was constructed in each independent component subspace with statistical variables.A proper ensemble strategy was chosen by combining all the fault detection results.Finally,the instrument with fault was located by contribution algorithm.The simulation results with TE (Tennessee Eastman) process show that this method has higher precision on small fault detection and more flexibility with proper ensemble strategy.%针对流程工业中多仪表微小故障难以检测的问题,利用独立元分析(ICA)提取仪表变量的独立元信息,根据独立元贡献度矩阵构建独立元子空间,并分别在每个独立元子空间上根据不同的贡献率选择独立元个数,得出三个统计量及其控制限,建立故障检测模型.再综合所有子空间故障检测模型的检测结果,根据实际需求制定集成故障检测策略,最后通过贡献度算法对故障源进行识别和分离.对Tennessee Eastman过程数据的仿真实验结果表明独立子空间算法提高了微小故障的检测精度,在流程工业中多仪表故障诊断中配合不同的集成故障检测策略在应用中更具有灵活性.