Goulding et al., 2000 - Google Patents
Fault detection in continuous processes using multivariate statistical methodsGoulding et al., 2000
View PDF- Document ID
- 2679067517184994252
- Author
- Goulding P
- Lennox B
- Sandoz D
- Smith K
- Marjanovic O
- Publication year
- Publication venue
- International journal of systems science
External Links
Snippet
The approach to process monitoring known as multivariate statistical process control (MSPC) has developed as a distinct technology, closely related to the field of fault detection and isolation. A body of technical research and industrial applications indicate a unique …
- 238000000034 method 0 title abstract description 55
Classifications
-
- 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
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
-
- 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
- G05B23/0243—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 model based detection method, e.g. first-principles knowledge model
- G05B23/0254—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 model based detection method, e.g. first-principles knowledge model based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
-
- 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
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0267—Fault communication, e.g. human machine interface [HMI]
-
- 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
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24056—Portable, detachable module to input test signals, read test results
-
- 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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
-
- 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
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Goulding et al. | Fault detection in continuous processes using multivariate statistical methods | |
Harrou et al. | Statistical process monitoring using advanced data-driven and deep learning approaches: theory and practical applications | |
JP4528335B2 (en) | Sensor performance verification apparatus and method | |
Portnoy et al. | An improved weighted recursive PCA algorithm for adaptive fault detection | |
Chen et al. | Dynamic process fault monitoring based on neural network and PCA | |
Mansouri et al. | Kernel PCA-based GLRT for nonlinear fault detection of chemical processes | |
Bakdi et al. | A new adaptive PCA based thresholding scheme for fault detection in complex systems | |
US9892238B2 (en) | System and method for monitoring a process | |
Mnassri et al. | Reconstruction-based contribution approaches for improved fault diagnosis using principal component analysis | |
Harrou et al. | Statistical fault detection using PCA-based GLR hypothesis testing | |
Yu | Local and global principal component analysis for process monitoring | |
Norvilas et al. | Intelligent process monitoring by interfacing knowledge-based systems and multivariate statistical monitoring | |
Zhou et al. | Probabilistic latent variable regression model for process-quality monitoring | |
Zhao et al. | Online fault prognosis with relative deviation analysis and vector autoregressive modeling | |
Chen et al. | Probabilistic contribution analysis for statistical process monitoring: A missing variable approach | |
US10955818B2 (en) | System and method for extracting principal time series data | |
Harrou et al. | Amalgamation of anomaly-detection indices for enhanced process monitoring | |
Godoy et al. | A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space | |
Monroy et al. | Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques | |
Zhang et al. | Spectral radius-based interval principal component analysis (SR-IPCA) for fault detection in industrial processes with imprecise data | |
Yang et al. | Quality-related monitoring of distributed process systems using dynamic concurrent partial least squares | |
Chetouani | Fault detection by using the innovation signal: application to an exothermic reaction | |
CN118643320B (en) | Quality-related minor fault detection method based on dynamic orthogonal subspace | |
Márquez-Vera et al. | Adaptive threshold PCA for fault detection and isolation | |
Stork et al. | Distinguishing between process upsets and sensor malfunctions using sensor redundancy |