通信与信息技术Issue(6)：22-25,4.
基于LSTM-AE的青霉素发酵工艺中残糖异常检测
Residual sugar anomaly detection in penicillin fermentation process based on LSTM-AE
张秀清 1王杰 1王晓君 1赵春丽2
作者信息
- 1. 河北科技大学信息科学与工程学院,河北 石家庄 050091
- 2. 国药集团威奇达药业有限公司,山西 大同 037000
- 折叠
摘要
Abstract
The production and fermentation process of penicillin exhibits pronounced time-varying and non-linear characteristics.This is exemplified by the sudden and anomalous behaviour of residual sugar time series data in the penicillin fermentation process in pharmaceutical factories.Such behaviour can be identified in a timely manner and employed to avert significant economic losses and con-serve resources.The features of the LSTM algorithm are embedded into the structure of the AE algorithm,with the concentration of residu-al sugar in the fermentation medium taken as the research object.This allows the time-varying and non-linear features in the residual sugar data to be captured,thereby further improving the detection capability of LSTM.The time-varying and non-linear features in the data can be effectively captured,thereby enhancing the anomaly detection function.The AE is responsible for capturing the latent space of the variables,which further enhances the detection ability of the LSTM.The results demonstrate that the algorithm exhibits excellent accuracy and adaptability when compared to a general reconstruction anomaly detection algorithm,with a detection accuracy exceeding 90%.The algorithm effectively identifies time-varying and nonlinear features in the data,providing valuable theoretical insights and a methodological foundation for anomaly detection in pharmacy data.关键词
异常检测/时间序列/长短期记忆网络/自编码网络/重构误差Key words
Anomaly detection/Time series/Long and short-term memory network/Self-coding network/Reconstruction error分类
信息技术与安全科学引用本文复制引用
张秀清,王杰,王晓君,赵春丽..基于LSTM-AE的青霉素发酵工艺中残糖异常检测[J].通信与信息技术,2025,(6):22-25,4.
