基于公共特有子空间提取的工业设备多模式运行过程故障检测方法

工业设备运行状态直接影响到最终产品质量,有必要对设备运行过程开展监控,因此着重对工业设备运行数据中存在的不同阶次信息以及多模式等复杂数据特性展开讨论.针对过程中存在的不同阶次信息问题,首先通过引入最大交互熵展开与偏最小二乘方法,将原始空间信息分解为高阶和低阶信息,并构建相应隐空间模型来提取高阶与低阶质量相关关系;其次,...

Multimode process monitoring for industrial equipments utilizing common-specific information extraction strategy

Industrial equipments have been widely used in modern industry, which indicates that the final product quality highly relies on the operational status of industrial equipments. Therefore, it is of high importance to detect the quality-related faults in equipments where non-linearity and non-stationarity are considered as two crucial characteristics in operating data. In this paper, high order and low order information in process data are discussed in details where a two-step latent variables extraction method is implemented using maximum mutual information and partial least square (PLS). In order to handle multimode process monitoring issue, a common-specific information strategy is designed. Combined with the high order and low order information extraction, the original data are further separated into four subspaces: common high, common low, specific high and specific low order information, respectively. Secondly, an online monitoring algorithm is developed, which helps identify mode switch or fault and improves the performance of multi-mode process monitoring. Finally, monitoring performance is further demonstrated on a real mechanical arm where the result shows the superiority of the proposed method.