化工学报2025，Vol.76Issue(4)：1635-1646,12.DOI:10.11949/0438-1157.20241121
面向多采样率数据的TTPA-LSTM软测量建模
TTPA-LSTM soft sensor modeling for multi-sampling rate data
摘要
Abstract
In practical industrial production,the time lags and sampling rate differences among process variables can deteriorate the modeling quality,rendering many soft sensing models inapplicable.Therefore,a soft sensing modeling approach based on time-aware temporal pattern attention(TTPA)mechanism and long short-term memory network is proposed.In this study,we first reconstruct the data corresponding to high and low sampling rates into short-term and long-term information,respectively.A time-aware module is utilized to decompose the input information while considering the characteristics of time intervals.To address the issue of low proportion of quality-related information,a non-increasing heuristic decay function is designed to weight the short-term information.By combining these weighted components,we derive an integrated feature set that encapsulates both short-term and long-term information,thereby mitigating the impact of data loss resulting from multiple sampling rates.Secondly,a feature optimization module is introduced to achieve two-dimensional filtering of features,and the time lag information in the multivariate time series is analyzed across time steps to obtain more effective quality-related features.Finally,a soft sensing model based on TTPA-based long short-term memory network is established.The effectiveness and superiority of the proposed model were verified through the application simulation of IndPensim process and debutanizer process.关键词
多采样率/时间感知模式注意力/长短时记忆网络/软测量/神经网络/过程控制/动态建模Key words
multi-sampling rate/time-aware temporal pattern attention/long short-term memory network/soft-sensor/neural networks/process control/dynamic modeling分类
信息技术与安全科学引用本文复制引用
王法正,隋璘,熊伟丽..面向多采样率数据的TTPA-LSTM软测量建模[J].化工学报,2025,76(4):1635-1646,12.基金项目
国家自然科学基金项目(61773182) （61773182）
国家重点研发计划子课题项目(2018YFC1603705-03) （2018YFC1603705-03）
江南大学"轻工技术与工程"双一流学科与支撑学科协同发展支持计划项目(QGJC20230203) （QGJC20230203）
