摘要
由于工业过程常常受到扰动等因素影响导致工作点发生偏移,所以及时准确地掌握过程运行状况的变化显得尤为重要。目前的工况识别主要针对是否发生故障或者发生何种故障,很少有文献能够从安全、经济、故障等多方面考虑过程工况。针对这一问题,结合过程的历史数据和相关操作要求,获取不同安全状况、经济状况等的评判标准。基于这些判断准则,使用双层降噪自编码(denoising autoencoder,DAE)方法提取数据特征,并用自组织映射神经网络(self-organizing map,SOM)方法对特征提取后的数据聚类,将过程的状况可视化到二维映射图上。在该方法中,DAE方法可以降低工业过程扰动对数据的影响,而SOM方法能够更好地实现过程性能的可视化监控。通过实验可以发现,基于DAE-SOM的双层映射方法可以很好地判断系统的安全级别以及发生的故障类型、当前系统的经济效益状况等。
As disturbances and other factors often lead to shifting of work point in industrial process, it is particularly important to timely and accurately identify process changes. Current working condition identification methods mainly focus on whether or what fault occurs, but few consider process conditions from viewpoint of safety, economy, fault and other aspects. Decision criteria at different safety and economic conditions were proposed by combination of historical process data and related operational requirements. With these criteria, data characteristics was extracted by DAE method and extracted feature data was clustered by SOM method. Then, the process state was visually projected to two-dimensional maps. In this method, the DAE method can reduce influence of industrial process disturbance on data and the SOM method can better visually monitor process performance. Experimental study showed that the DAE-SOM multi-layer mapping method can determine security level, type of failure, and current economic efficiency of the system.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2018年第2期769-778,共10页
CIESC Journal
基金
国家自然科学基金重点项目(61333010)
国家自然科学基金面上项目(21376077)
国家自然科学基金优秀青年基金项目(61422303)
