[1] 杨明辉, 刘晓月, 邓晓刚, 等. 基于加权概率CVDA的动态化工系统微小故障检测[J]. 化工学报, 2022, 73(09): 3963-3972.[2] Taqvi S A A, Zabiri H, Tufa L D, et al. A review on data-driven learning approaches for fault detection and diagnosis in chemical processes[J]. ChemBioEng Reviews, 2021, 8(3): 239-259.[3] 胡瑾秋, 郭放, 张来斌. 结合改进PSO算法和LSSVM的化工异常工况超早期监测预警研究[J]. 电子测量与仪器学报, 2018, 32(02): 36-41.[4] Deng F, Wang F. Misoperation monitoring and early warning during startup and shutdown of petrochemical units[J]. Journal of Loss Prevention in the Process Industries, 2020, 67: 104265.[5] Li L, Ding S, Peng X. Distributed data-driven optimal fault detection for large-scale systems[J]. Journal of Process Control, 2020, 96: 94-103.[6] Amin M T, Khan F, Ahmed S, et al. A data-driven Bayesian network learning method for process fault diagnosis[J]. Process Safety and Environmental Protection, 2021, 150: 110-122.[7] Ardali N R, Zarghami R, Gharebagh R S, et al. A data-driven fault detection and diagnosis by NSGAII-t-SNE and clustering methods in the chemical process industry[J]. Computer Aided Chemical Engineering, 2022, 49:1447-1452.[8] 王荣渤. 基于数字孪生的热处理设备健康评估技术研究[D]. 西安: 西安电子科技大学, 2021: 3-4.[9] 文成林, 吕菲亚, 包哲静, 等.基于数据驱动的微小故障诊断方法综述[J]. 自动化学报, 2016, 42(09): 1285-1299.[10] 何亚东, 袁壮, 林扬, 等. 基于极深因子分解机的化工过程故障诊断方法[J]. 过程工程学报, 2022, 22(01): 135-144.[11] Prakash O, Samantaray A K, Bhattacharyya R. Model-based multi-component adaptive prognosis for hybrid dynamical systems[J]. Control Engineering Practice, 2017, 72: 1-18.[12] Moliner-Heredia R, Penarrocha-Alos I, Abellan-Nebot J V. Model-based tool condition prognosis using power consumption and scarce surface roughness measurements[J]. Journal of Manufacturing Systems, 2021(61): 311-325.[13] 彭琦. 基于动态状态空间模型的滚动轴承寿命预测研究[D]. 北京: 北京化工大学, 2016: 13-32.[14] Hassannayebi E, Nourian R, Mousavi S M, et al. Predictive analytics for fault reasoning in gas flow control facility: A hybrid fuzzy theory and expert system approach[J]. Journal of loss prevention in the process industries, 2022, 77: 104796.[15] Guo Y, Wang J, Chen H, et al. An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems[J]. Applied Thermal Engineering, 2019, 149: 1223-1235.[16] Rui Z, Yan R, Chen Z, et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115: 213-237.[17] Deng Z, Han T, Cheng Z, et al. Fault detection of petrochemical process based on space-time compressed matrix and Naive Bayes[J]. Process Safety and Environmental Protection, 2022, 160: 327-340.[18] 袁壮, 凌逸群, 杨哲, 等. 基于TA-ConvBiLSTM的化工过程关键工艺参数预测[J]. 化工学报, 2022, 73(1): 342-351.[19] Lee H, Kim C, Lim S, et al. Data-driven fault diagnosis for chemical processes using transfer entropy and graphical lasso[J]. Computers & Chemical Engineering, 2020, 142: 107064.[20] Fezai R, Mansouri M, Abodayeh K, et al. Online reduced kernel PLS combined with GLRT for fault detection in chemical systems[J]. Process Safety and Environmental Protection, 2019, 128: 228-243.[21] Han Y, Ding N, Geng Z , et al. An optimized long short-term memory network based fault diagnosis model for chemical processes[J]. Journal of Process Control, 2020, 92: 161-168.[22] Pyun H, Kim K, Ha D, et al. Root causality analysis at early abnormal stage using principal component analysis and multivariate Granger causality[J]. Process Safety and Environmental Protection, 2020, 135: 113-125.[23] 林扬, 何亚东, 袁壮, 等. 基于PCA-SVDD的化工过程异常工况检测[J]. 过程工程学报, 2022, 22(07): 970-978.[24] 乔佳伟, 田慕琴. 基于AHP-TOPSIS综合评价法的离心泵健康状态评估[J]. 工矿自动化, 2022, 48(09): 69-76.[25] Lin S S, Shen S L, Zhou A, et al. Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels[J]. Water Research, 2020, 187: 116437.[26] Salih M M, Zaidan B B, Zaidan A A, et al. Survey on fuzzy TOPSIS state-of-the-art between 2007 and 2017[J]. Computers & Operations Research, 2019, 104: 207-227..[27] 邓超, 王远航, 吴军, 等. 机电装备性能退化建模与健康状态评估方法[J]. 计算机集成制造系统, 2018, 24(09): 2279-2287.Deng C, Wang Y H, Wu J, et al. Performance degradation modelling and health state evaluation for mechanical and electrical equipment[J]. Computer Integrated Manufacturing Systems, 2018, 24(09): 2279-2287.[28] 蔡金锭, 叶荣, 刘庆珍. 基于改进TOPSIS和时域特征量的油纸绝缘状态分类分级评估[J]. 电机与控制学报, 2020, 24(01): 86-94.[29] 刘志强, 王涛. 基于改进TOPSIS的驾驶行为实时安全性评估方法[J]. 重庆理工大学学报(自然科学), 2021, 35(11): 58-66.[30] Saraswat S K, Digalwar A K. Evaluation of energy alternatives for sustainable development of energy sector in India: An integrated Shannon's entropy fuzzy multi-criteria decision approach[J]. Renewable Energy, 2021, 171: 58-74.[31] 胡弦. 机床数控装置的健康状态评估方法研究[D]. 杭州: 浙江理工大学, 2021: 30-32.[32] T. Jarullah A, A. Awad N, M. Mujtaba, I. Optimal design and operation of an industrial fluidized catalytic cracking reactor[J]. Fuel, 2017, 206: 657-674.[33] Huang M, Zheng Y, Li S. Distributed economic model predictive control with pseudo-steady state modifier adaptation for an industrial fluid catalytic cracking unit[J]. Chemical Engineering Research and Design, 2022, 180: 379-390. |