Zeng et al., 2014 - Google Patents
Regression‐based analysis of multivariate non‐Gaussian datasets for diagnosing abnormal situations in chemical processesZeng et al., 2014
- Document ID
- 15773582033270569072
- Author
- Zeng J
- Xie L
- Kruger U
- Gao C
- Publication year
- Publication venue
- AIChE Journal
External Links
Snippet
This article presents a regression‐based monitoring approach for diagnosing abnormal conditions in complex chemical process systems. Such systems typically yield process variables that may be both Gaussian and non‐Gaussian distributed. The proposed …
- 230000002159 abnormal effect 0 title abstract description 20
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yu | Localized Fisher discriminant analysis based complex chemical process monitoring | |
Liu et al. | Statistical‐based monitoring of multivariate non‐Gaussian systems | |
Mnassri et al. | Reconstruction-based contribution approaches for improved fault diagnosis using principal component analysis | |
Yin et al. | A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process | |
Feital et al. | Modeling and performance monitoring of multivariate multimodal processes | |
Jiang et al. | Just‐in‐time reorganized PCA integrated with SVDD for chemical process monitoring | |
Wen et al. | Data‐based linear Gaussian state‐space model for dynamic process monitoring | |
Yu et al. | A sparse PCA for nonlinear fault diagnosis and robust feature discovery of industrial processes | |
Iooss et al. | Advanced methodology for uncertainty propagation in computer experiments with large number of inputs | |
Ge et al. | Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions | |
Sharifi et al. | Sensor fault diagnosis with a probabilistic decision process | |
Li et al. | Fault detection method for non‐Gaussian processes based on non‐negative matrix factorization | |
Jahani et al. | Statistical monitoring of multiple profiles simultaneously using Gaussian processes | |
Zhang et al. | Spectral radius-based interval principal component analysis (SR-IPCA) for fault detection in industrial processes with imprecise data | |
Ge et al. | External analysis‐based regression model for robust soft sensing of multimode chemical processes | |
Liu et al. | Online Flooding Supervision in Packed Towers: An Integrated Data‐Driven Statistical Monitoring Method | |
Wang | Enhanced fault detection for nonlinear processes using modified kernel partial least squares and the statistical local approach | |
Mori et al. | A quality relevant non‐Gaussian latent subspace projection method for chemical process monitoring and fault detection | |
Venkateswaran et al. | Design of functional observers for fault detection and isolation in nonlinear systems in the presence of noises | |
Zeng et al. | Regression‐based analysis of multivariate non‐Gaussian datasets for diagnosing abnormal situations in chemical processes | |
Peng et al. | Fault detection and isolation for self powered neutron detectors based on Principal Component Analysis | |
Wang et al. | Nonlinear dynamic process monitoring based on ensemble kernel canonical variate analysis and bayesian inference | |
Zhang et al. | A local and global statistics pattern analysis method and its application to process fault identification | |
Zhu et al. | Mixture robust L1 probabilistic principal component regression and soft sensor application | |
Ramuhalli et al. | Uncertainty quantification techniques for sensor calibration monitoring in nuclear power plants |