基于时间动态因果图的复杂工业过程故障预测方法

故障预测技术有力地保证了生产过程的平稳有序和人员安全.但在实际操作过程中,过程数据的定性与定量信息并存,模型较为复杂.此外,在生产过程中,利用在线收集的数据进行故障预测时存在时序延迟问题.对此,建立一种基于时间动态因果图(TDCD)的故障预测模型.在模型建立过程中,提出参数的延迟时间间隔学习算法,即移动搜索最大信息系数(MIC)算法,充分考虑了时序方面的延迟问题.在推理过程中,加入趋势分析和延时信息排序以优化推理过程,减少因延迟时间造成的故障误报率.最后,使用某浮选过程因果图网络进行算法验证,并将所提出的策略应用于湿法冶金浸出过程,与单值/多值不确定动态因果图进行对比,以表明故障预测策略的先进性和有效性.

Fault prediction method for complex industrial process based on time dynamic causality diagram

Fault prediction technology effectively guarantees the smooth and orderly production process and the safety of personnel. However, in actual operation, qualitative and quantitative informations of process data coexist, and the model is complex. In addition, in the production process, there is a timing delay problem when using online collected data for fault prediction. This paper establishes, verifies and applies a fault prediction model based on time dynamic causality diagram(TDCD). In the process of model building, a parameter delay time interval learning algorithm is proposed, that is, the mobile search maximum maximal information coefficient(MIC) algorithm, which fully considers the timing delay problem. In the reasoning process, trend analysis and delay information sorting are added to optimize the reasoning process and reduce the false alarm rate caused by delay time. Finally, the algorithm is validated by using a causal graph network for a flotation process. The proposed strategy is applied to the hydrometallurgical leaching process, and compared with the single-valued/multi-valued uncertain dynamic causality diagram, which shows the advancement and effectiveness of the fault prediction strategy.