Singh et al., 2013 - Google Patents
Performance of CUSUM and EWMA charts for serial correlationSingh et al., 2013
- Document ID
- 5194254099125703235
- Author
- Singh S
- Prajapati D
- Publication year
- Publication venue
- The TQM Journal
External Links
Snippet
Purpose–The purpose of this paper is to study the effect of correlation on the performance of CUSUM and EWMA charts. The performance of the CUSUM and EWMA charts is measured in terms of average run lengths (ARLs) for the positively correlated data. The ARLs at …
- 238000000034 method 0 abstract description 96
Classifications
-
- 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
- G06Q10/063—Operations research or analysis
-
- 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
- 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
- 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
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Serdio et al. | Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations | |
Ainur et al. | Sample size and non-normality effects on goodness of fit measures in structural equation models. | |
Kazemzadeh et al. | Phase I monitoring of polynomial profiles | |
Psarakis et al. | SPC procedures for monitoring autocorrelated processes | |
Abujiya et al. | Enhancing the performance of combined Shewhart‐EWMA charts | |
Singh et al. | Performance of CUSUM and EWMA charts for serial correlation | |
Dorj et al. | A bayesian hidden markov model-based approach for anomaly detection in electronic systems | |
Park et al. | A new multivariate EWMA control chart via multiple testing | |
Lee et al. | Time-adaptive support vector data description for nonstationary process monitoring | |
Saghir et al. | Monitoring process variation using modified EWMA | |
Niaki et al. | Statistical monitoring of autocorrelated simple linear profiles based on principal components analysis | |
Bersimis et al. | Methods for interpreting the out‐of‐control signal of multivariate control charts: A comparison study | |
Zarandi et al. | A general fuzzy-statistical clustering approach for estimating the time of change in variable sampling control charts | |
Nidsunkid et al. | The effects of violations of the multivariate normality assumption in multivariate Shewhart and MEWMA control charts | |
Ghute et al. | A nonparametric signed-rank control chart for bivariate process location | |
Kaw et al. | Improved methodology and set-point design for diagnosis of model-plant mismatch in control loops using plant-model ratio | |
Guh | Real-time recognition of control chart patterns in autocorrelated processes using a learning vector quantization network-based approach | |
Izadbakhsh et al. | Monitoring multinomial logistic profiles in Phase I using log-linear models | |
Shamsuzzaman et al. | Design of EWMA control chart for minimizing the proportion of defective units | |
Zideh et al. | Physics-informed convolutional autoencoder for cyber anomaly detection in power distribution grids | |
Lee et al. | Multiple-fault diagnosis under uncertain conditions by the quantification of qualitative relations | |
Prajapati | Effectiveness of conventional CUSUM control chart for correlated observations | |
Safarihamid et al. | A joint-entropy approach to time-series classification | |
Kovářík et al. | Implementing control charts to corporate financial management | |
Prajapati et al. | Determination of level of correlation for products of pharmaceutical industry by using modified X-bar chart |