控制理论与应用2025，Vol.42Issue(9)：1721-1732,12.DOI:10.7641/CTA.2024.30492
双空间特征自适应融合的故障检测方法
Fault detection method with adaptive fusion of dual-space features
摘要
Abstract
Due to the complex structure of the large complex industrial processes,the process variables often exhibit hybrid correlations.A single model cannot accurately represent the hybrid correlations between variables,resulting in a large number of missed alarms or false alarms in the fault detection.To address this problem,a fault detection method with adaptive fusion of dual-space features is proposed.Firstly,the Gaussian linear features and non-Gaussian nonlinear features are extracted in the original data space and the residual kernel space,respectively,using a hierarchical feature extraction strategy.Then,the Bayesian inference is utilized to convert the monitoring statistics from different spaces into failure probabilities,and an adaptive probabilistic weighting strategy is designed to construct the total probabilistic statistical indices for monitoring the process operation status.Finally,several experiments on a numerical simulation and the Tennessee Eastman benchmark process are presented to demonstrate the feasibility and effectiveness of the proposed method.关键词
故障检测/特征提取/混合相关性/贝叶斯推理/统计指标Key words
fault detection/feature extraction/hybrid correlations/Bayesian inference/statistical index引用本文复制引用
刘美枝,孔祥玉,安秋生,罗家宇..双空间特征自适应融合的故障检测方法[J].控制理论与应用,2025,42(9):1721-1732,12.基金项目
国家自然科学基金项目(62273354,61673387),山西省高等学校科技创新项目(2022L434)资助.Supported by the National Natural Science Foundation of China(62273354,61673387)and the Science and Technology Innovation Project of Colleges and Universities in Shanxi Province(2022L434). （62273354,61673387）
