Park et al., 2015 - Google Patents
A new multivariate EWMA control chart via multiple testingPark et al., 2015
- Document ID
- 13355316932240065188
- Author
- Park J
- Jun C
- Publication year
- Publication venue
- Journal of Process Control
External Links
Snippet
This paper proposes a new type of multivariate EWMA control chart for detecting the process mean shift on the basis of a series of most recent T-squared statistics. We established a multiple hypothesis testing which uses the false discovery rate as the error to be controlled …
- 238000000034 method 0 abstract description 45
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
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
-
- 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
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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 |
---|---|---|
Park et al. | A new multivariate EWMA control chart via multiple testing | |
Gauri et al. | Recognition of control chart patterns using improved selection of features | |
Hall et al. | Probabilistic physics-of-failure models for component reliabilities using Monte Carlo simulation and Weibull analysis: a parametric study | |
Harrou et al. | Statistical fault detection using PCA-based GLR hypothesis testing | |
Fang et al. | An adaptive functional regression-based prognostic model for applications with missing data | |
Chou et al. | Economic design of variable sampling intervals T2 control charts using genetic algorithms | |
Anzanello et al. | Multicriteria variable selection for classification of production batches | |
Zhang et al. | Correntropy based data reconciliation and gross error detection and identification for nonlinear dynamic processes | |
Kim et al. | Data mining model-based control charts for multivariate and autocorrelated processes | |
Du et al. | Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes | |
Alaeddini et al. | A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts | |
Wang et al. | Data-driven fault prediction and anomaly measurement for complex systems using support vector probability density estimation | |
Salah et al. | Inferential sensor-based adaptive principal components analysis of mould bath level for breakout defect detection and evaluation in continuous casting | |
He et al. | A nonparametric CUSUM scheme for monitoring multivariate time-between-events-and-amplitude data with application to automobile painting | |
Zarandi et al. | A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts | |
Singh et al. | Performance of CUSUM and EWMA charts for serial correlation | |
Ghute et al. | A nonparametric signed-rank control chart for bivariate process location | |
Huang et al. | Measuring the performance improvement of a double generally weighted moving average control chart | |
Kaib et al. | Improving kernel PCA-based algorithm for fault detection in nonlinear industrial process through fractal dimension | |
Sheu et al. | Monitoring process mean and variability with generally weighted moving average control charts | |
Jakubowski et al. | Roll wear prediction in strip cold rolling with physics-informed autoencoder and counterfactual explanations | |
Izadbakhsh et al. | Monitoring multinomial logistic profiles in Phase I using log-linear models | |
Karimi et al. | Measuring persistence in a stationary time series using the complex network theory | |
Nor et al. | Process monitoring and fault detection in non-linear chemical process based on multi-scale kernel Fisher discriminant analysis | |
Ghazizadeh et al. | Single-step change point estimation in nonlinear profiles using maximum likelihood estimation |