Ramaker et al., 2004 - Google Patents
The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoringRamaker et al., 2004
View HTML- Document ID
- 341875567924795745
- Author
- Ramaker H
- van Sprang E
- Westerhuis J
- Smilde A
- Publication year
- Publication venue
- Chemometrics and intelligent laboratory systems
External Links
Snippet
This paper describes the sensitivity of false alarm rate to misspecification of the number of PCA components in multivariate statistical process control (MSPC) models. Using simulations, it is shown that choosing an incorrect number of components in monitoring …
- 238000000034 method 0 title abstract description 25
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
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- 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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rato et al. | A systematic comparison of PCA‐based statistical process monitoring methods for high‐dimensional, time‐dependent processes | |
| Ramaker et al. | The effect of the size of the training set and number of principal components on the false alarm rate in statistical process monitoring | |
| US7421351B2 (en) | Monitoring and fault detection in dynamic systems | |
| Van Sprang et al. | Critical evaluation of approaches for on-line batch process monitoring | |
| Ngo et al. | The steps to follow in a multiple regression analysis | |
| Chen et al. | The application of principal component analysis and kernel density estimation to enhance process monitoring | |
| HARALAMPU et al. | Estimation of Arrhenius model parameters using three least squares methods | |
| Russell et al. | Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis | |
| Lane et al. | Performance monitoring of a multi-product semi-batch process | |
| Ramaker et al. | Fault detection properties of global, local and time evolving models for batch process monitoring | |
| García-Muñoz et al. | Model predictive monitoring for batch processes | |
| Flores‐Cerrillo et al. | Multivariate monitoring of batch processes using batch‐to‐batch information | |
| Soderstrom et al. | A mixed integer optimization approach for simultaneous data reconciliation and identification of measurement bias | |
| Andrade et al. | A Bayesian approach to calibrate system dynamics models using Hamiltonian Monte Carlo | |
| Zhao et al. | Improved batch process monitoring and quality prediction based on multiphase statistical analysis | |
| Chang et al. | Addressing multicollinearity in semiconductor manufacturing | |
| Luo et al. | Hierarchical monitoring of industrial processes for fault detection, fault grade evaluation, and fault diagnosis | |
| Yang et al. | Nonparametric profile monitoring using dynamic probability control limits | |
| Harkat et al. | New sensor fault detection and isolation strategy–based interval‐valued data | |
| Rato et al. | On-line process monitoring using local measures of association. Part II: Design issues and fault diagnosis | |
| Thissen et al. | Nonlinear process monitoring using bottle-neck neural networks | |
| Amand et al. | Plant monitoring and fault detection: Synergy between data reconciliation and principal component analysis | |
| González et al. | A robust partial least squares regression method with applications | |
| Hatayama et al. | Bayesian central statistical monitoring using finite mixture models in multicenter clinical trials | |
| Xin et al. | Research on Test Data Quality Assessment and Outlier Processing Methods in Semiconductor Chip Manufacturing Process |