[1] GUO W, PAN T H, LI Z M, et al. Modelling for multiphase batch processes using steady state identification and deep recurrent neural network [C]//2019 12th Asian Control Conference. Kitakyushu: IEEE, 2019: 1084-1089. [2] TIAN Y, ZOU Q, HAN J. Data-driven fault diagnosis for automotive PEMFC systems based on the steadystate identification [J]. Energies, 2021, 14(7): 1918. [3] LI J Z, GAO M, Lü Y, et al. Overview on the steadystate detection methods of process operating data [J]. Chinese Journal of Scientific Instrument, 2013, 34(8): 1739-1748 (in Chinese). [4] NARASIMHAN S, MAH R S H, TAMHANE A C, et al. A composite statistical test for detecting changes of steady states [J]. AIChE Journal, 1986, 32(9): 1409-1418. [5] NARASIMHAN S, KAO C S, MAH R S H. Detecting changes of steady states using the mathematical theory of evidence [J]. AIChE Journal, 1987, 33(11): 1930-1932. [6] KIM M, YOON S H, DOMANSKI P A, et al. Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner [J]. International Journal of Refrigeration, 2008, 31(5): 790-799. [7] CAO S L, RHINEHART R R. An efficient method for on-line identification of steady state [J]. Journal of Process Control, 1995, 5(6): 363-374. [8] CAO S L, RHINEHART R R. Critical values for a steady-state identifier [J]. Journal of Process Control, 1997, 7(2): 149-152. [9] SHROWTI N A, VILANKAR K P, RHINEHART R R. Type-II critical values for a steady-state identifier [J]. Journal of Process Control, 2010, 20(7): 885-890. [10] BHAT S A, SARAF D N. Steady-state identification, gross error detection, and data reconciliation for industrial process units [J]. Industrial & Engineering Chemistry Research, 2004, 43(15): 4323-4336. [11] RHINEHART R R. Automated steady and transient state identification in noisy processes [C]//2013 American Control Conference. Washington: IEEE, 2013: 4477-4493. [12] JIANG T W, CHEN B Z, HE X R, et al. Application of steady-state detection method based on wavelet transform [J]. Computers & Chemical Engineering, 2003, 27(4): 569-578. [13] KORBEL M, BELLEC S, JIANG T W, et al. Steady state identification for on-line data reconciliation based on wavelet transform and filtering [J]. Computers & Chemical Engineering, 2014, 63: 206-218. [14] FLEHMIG F, MARQUARDT W. Detection of multivariable trends in measured process quantities [J]. Journal of Process Control, 2006, 16(9): 947-957. [15] LE ROUX G A C, SANTORO B F, SOTELO F F, et al. Improving steady-state identification [J]. Computer Aided Chemical Engineering, 2008, 25: 459-464. [16] TAO L L, LI C C, KONG X D, et al. Steady-state identification with gross errors for industrial process units [C]//10th World Congress on Intelligent Control and Automation. Beijing: IEEE, 2012: 4151-4154. [17] RINCON F D, LIMA F V, LE ROUX G A C. An ARX-based technique for steady-state identification of chemical processes [C]//2015 American Control Conference. Chicago: IEEE, 2015: 1113-1118. [18] YAO Y, ZHAO C H, GAO F R. Batch-to-batch steady state identification based on variable correlation and mahalanobis distance [J]. Industrial & Engineering Chemistry Research, 2009, 48(24): 11060-11070. [19] DALHEIM ? ?, STEEN S. A computationally efficient method for identification of steady state in time series data from ship monitoring [J]. Journal of Ocean Engineering and Science, 2020, 5(4): 333-345. [20] CAO P F, WANG J D, ZHANG C. Steady-state interval detection and nonlinear modeling for automatic generation control systems [J]. IEEE Access, 2019, 7: 139592-139600. [21] WU J G, XU H L, ZHANG C, et al. A sequential Bayesian partitioning approach for online steady-state detection of multivariate systems [J]. IEEE Transactions on Automation Science and Engineering, 2019, 16(4): 1882-1895. [22] LIU X M. Configuration, programming, implementation, and evaluation of distributed control system for a process simulator [D]. Ontario: The University of Western Ontario, 2015.
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